Posted: June 13th, 2022

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ATTACHED FILE(S)
The Effect of Unemployment on Suicide Rates in Canada
MDM4U Cumulative Assignment
Introduction
Unemployment rates and suicide rates, are two factors that every country strives to decrease in
its population. These factors have an impact on the stability of Canada’s economy and the quality
of life it’s citizens experience. These two statistics are carefully monitored annually by various
organizations includingStatistics CanadaandOurWorld In Datato study how they can be
decreased and to see if we are improving as a country. Unemployment rates and suicide rates will
be studied in this report to see if there is any solid relationship between the two. To do this,
extraneous variables will be outlined and a data set with 30 data points will be thoroughly
analyzed in order to achieve an accurate and well informed conclusion.
The purpose of this study is to find out if unemployment rates have a direct effect on suicide
rates for males and females. The unemployment rate is calculated by dividing the number of
unemployed people by the number of people in the labour force. This number is represented as a
percentage. The suicide rate will be represented as number of deaths by suicide per 100,000
people. If this can be proven, it will serve as evidence that the Government of Canada should
place a higher importance on tackling unemployment to reduce deaths caused by self harm,
therefore bringing prosperity and increase in overall wellbeing to the country.
Unemployment rates have an effect on suicide rates beacuse of the emotional instability of the
person who is unemployed. This unemployment can be caused by two major factors, cyclical
unemployment and the natural rate of unemployment. The first occurs when the economy is in a
recession, and the second can be caused by labour market factors such as government decisions
on hiring and opening of businesses (Amadeo). The two variables, unemployment rates and
suicide rates will be explored in further detail but in theory, an increase in unemployment rates
will in turn cause an increase in suicide rates. That will be proven in this investigation.
Extra Research
A study on this same exact topic was done in New Zealand and it is titled “Unemployment and
suicide. Evidence for a causal association?” This study was conducted by Blakely, Collins, and
Attickson. Its main objective was “to determine theindependent associations of labour force
status and socioeconomic position with death by suicide.” The participants were 2.04 million
people ages 18-64. The study measured the number of deaths by suicide 3 years after the survey
was taken. The conclusion made was that “being unemployed was associated with a twofold to
threefold increased relative risk of death by suicide, compared with being employed”(Blakely).
This is an extremely convincing result. This study also found that 50% of the relationship
between unemployment and suicide rates may have been caused by mental illness such as
depression.
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3
Independent Variable: Unemployment Rates
Unemployment as defined byOur World in Datais the“share of the labour force that is without
work but available for and seeking unemployment”(Our World in Data). The unemployment rate
is difficult to calculate because of many factors. There may be people who are unemployed, but
do not have the desire to work. Some unemployment is unavoidable because of the time the
survey is conducted such as unemployment in the agricultural space, as there are times when
there is no work to be done because of the cold temperatures. It is important to note, women tend
to be excluded from this data for three main reasons. Firstly, women face many problems when it
comes to discrimination in the workforce that deter them from seeking work or ataining that job.
Secondly, women are usually responsible for taking care of their families at home which makes
them unavailable to work. Lastly, women are considered to be employed when they are working
part time jobs even when those jobs lack security and stability (Our World in Data). For the sake
of this study, both males and females will be included to get an overall picture of the effect of
unemployment on suicide.
The data I will be using is an estimate of the true unemployment rate in Canada that was taken
from Our World in Data (See Appendix A: Table 1). They sourced this data from the ILO
(International Labour Organization) which is a nationalorganization that helps “set labour
standards, develop policies and devise programmes promoting decent work for all women and
men” (ILO).Our World in Datais a trustworthy andunbiased source because they accumulate
data from only the most accurate and trustworthy sources, and compile that information in a
manageable format and useful statistics for a wide range of topics. The sampling method is
mainly stratified random sampling. In this type of sample, the entire population is grouped and
simple samples are conducted from each group. This is useful to provide an accurate
representation of large populations (IWH). This is the main sampling method I found but it is
difficult to pinpoint a certain method since the data on unemployment rates is an amalgamation
of many reports, population censuses, and national estimates.
It is important to note extraneous variables that may have an effect on the study of
unemployment rates in Canada. Among these are automation in the industry. When robots and
machines take over jobs, people are laid off and they struggle to find jobs afterwards.
Unemployment also includes; people quitting their jobs, new graduates who just entered the
workforce, people who enter the workforce after a time where they were not interested in jobs,
and the relocation of the workplace or of the employee (Amadeo). The point is, there is a
percentage of unemployment that is not necessarily negative or bad for the economy and will not
directly impact suicide rates. Although it is still worth taking a look because it is still possible
these situations still cause lack of security and stability in a person that can in turn cause death by
self harm.
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One Variable Analysis of Unemployment Rates
The mean of the data is 7.8%, the mode is 6.91%, and the median is 7.4%. Based on the
measures of central tendency, we can observe that the data does not fluctuate much, although
when looking at the line graph above we do see that there are ups and downs and the data is not
evenly spread. The measures of spread give a much better look at the data. The range of the data
is 5.72% which is quite large. This is a positive thing because the highest value was in 1993, and
the lowest value was in 2019. This shows that unemployment rates considerably decreased in the
last 25 years. The standard deviation of the data is around 1.56% and the variance is around
2.44%. Overall, unemployment looks to be on a slight downward trend but each data point is on
average 1.56% away from the mean which is not a small number given that it is representative of
a fraction of the entire workforce.
The histogram below appears to be right skewed because most of the data lies on the left side of
the graph. The mode is smaller than the median, and the median is smaller than the mean which
is a key characteristic of a right skewed distribution. It shows that one third of the points are
between the 6.5% to 7.5% range. It also shows that many points are between 8.5% and 11.5%.
There are no outliers in the data because each point is an accurate representation of the specific
time period. Its good to see an overall downward trend because now it is possible to prove that
there is a correlation with suicide rates as I expect to see a downward trend there as well.
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This bar graph shows the difference between the points more clearly.
6
For the box and whisker plot, quartile 1 is at 6.85%, quartile 3 is at 8.91% and the interquartile
range is 2.06%. This again shows that most of the data is clustered around the mean, but still
there are lots of points outside that range.
(See Appendix B: Section 1 for calculations)
Dependent Variable: Suicide Rates
Suicide rates are defined byOur World in Dataas,“the number of deaths by suicide per 100,000
people in any given demographic” (OWD). Suicide is a disturbing statistic that many people do
not talk about. It is a sensitive topic but it is an important one to tackle. Suicide rates in men
ecpecially tend to be almost 4 times that of women, and that is for a number of reasons. Firstly,
the way men deal with depression and thoughts of suicide is very differnt than women. Women
are better at dealing with stress because they open up more often to friends and family, while
men tend to keep it to themselves. Despite this, women have a higher rates of self harm, and that
is a massive risk factor for suicides (Public HealthAgency). For this reason both men and women
were included in this data.Our World in Datasourcedits data fromGlobal Health Data
Exchange, the most comprehensive catalog of healthrelated data (GHDx). The data I will use is
the rates for all ages. I could not find the rates for ages 15 and up. This is not a major problem
because the rates for ages 0-15 are negligible.
The GHDx “was created as a dedicated place for anyone interested in global health and
demography to quickly find and share information about data along with actual datasets”
(GHDx). The sampling technique again is tough to specify becauseOur World in Datacombines
many population surveys and reports but the main method that is used is the stratified random
sample which has been explained above. The GHDx andOur World in Databoth have no bias
because they both look at many international and national sources and collect the data to
represent it in the most fair manner. They are both well known and trusted sources.
Just like unemployment rates, suicide rates have a lot of extrenous vairables. For example,
people who have experienced emotional or physical abuse, bullying, or sexual violence have a
higher risk of commiting suicide (Centers for DiseaseControl and Prevention). It is important to
note these external variables but, the reason I feel there is a corrolation between unemployment
7
and suicide is that unemployemnt is known to cause stress, anxiety, and depression. Depression
is one of the leading risk factors to suicide according toThe National Institute of Mental Health.
One Variable Analysis of Suicide rates
The mean of the data is 13.8, the median is 13.6, and there is no mode for this set of data as there
are no two points that are the same. The mean and median are almost the same and the line graph
above for all ages is almost flat, so it is apparent that the data is fairly uniform. There is a slight
downward trend that shows deaths have been decreasing slightly. The range is only 2.35 which
means there was almost no difference between the highest and lowest value. The standard
deviation is around 0.77, and the variance is around 0.59. These values are extremely small
confirming that the data looks uniform but with a slight downward trend. The histogram appears
to be bimodal. The two peaks both have a frequency of 9 and show that most values are either
between 13 to 13.5 or 14.5 to 15 deaths per 100,000. Even though it does not appear to be
uniform, the difference between the 2 peaks is still very small. This histogram is not the best
representation of the data. The bar graph below really shows how uniform the data is and is a
much better representation. There are no outliers in the data because each point is an accurate
representation of the specific time period.
I am happy to see a slight decrease in suicide rates over time. Even though the decreace is
extremely small, it is representative of less lives being lost to suicide and it is what I needed to
compare suicide rates with unemployment rates. So far the results seem to match with my thesis
that as unemployment rates increase so will suicide rates, and vice versa.
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9
For the box and whisker plot, quartile 1 is 13.2, quartile 3 is 14.6 and the interquartile range is
1.4. The fact that the middle portion of the data only has a difference of 1.4, shows how close the
data points are to each other.
(See Appendix B: Section 2 for calculations)
Two Variable Analysis:
From the one variable analysis it is noticeable that the unemployment rates are decreasing at a
slow pace with ups and downs, but with an overall downward trend. Suicide rates are also on a
downward trend but very slight fluctuations from point to point. To recall the hypothesis, I want
to prove that an increase in unemployment rates will cause an increase in suicide rates. From
what was observed so far and based on background information it is likely there will be a
positive correlation between the two variables but it is not clear if there will be a weak, moderate
or strong correlation.
The data will be displayed on a scatter plot to analyse the trend in the data. Different regression
models will be used to find the best fit for the data.
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Linear Regression
The equation of the line is y=0.405x + 10.6. The correlation coefficient is approximately 0.8
which is a strong positive linear correlation.
𝑟
2
=0 .637
𝑟 =0 .637
𝑟 =∼0 .80
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Quadratic Regression
The equation of the curve is y=9.85 + 0.578x + -0.01x^2. The correlation coefficient is
approximately 0.86 which is a very strong positive linear correlation.
𝑟
2
=0 .743
𝑟 =0 .743
𝑟 =∼0 .86
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Cubic Regression:
The equation of the curve is y=36 + -8.94x + 1.12x^2 + -0.0438x^3. The correlation coefficient
is approximately 0.88 which is the strongest positive linear correlation of all types of regression I
examined.
𝑟
2
=0 .769
𝑟 =0 .769
𝑟 =∼0 .88
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Exponential Regression
The equation of the curve is y=11e^0.0288x. The correlation coefficient is approximately 0.8
which is a strong positive linear correlation and the weakest correlation examined.
𝑟
2
=0 .635
𝑟 =0 .635
𝑟 =∼0 .80
The strongest correlation observed was that of the cubic regression with a correlation coefficient
of 0.88 which is extremely strong. The weakest correlation was the exponential regression
although the linear regression was only a little stronger. It is safe to say that with every
regression model used, there is always a strong positive correlation showing that as
unemployment rates increase, so does the suicide rate, and as unemployment rates decrease, so
does the suicide rate. The cubic function is the best fit for the data and it makes the most sense
since fluctuations are normal when there are slight spikes in unemployment or suicide rates. This
model confirms my thesis and background knowledge, and affirms other studies that were
researched. The question still remains as to whether this is a cause and effectrelationship or
accidental. Since the relationship was so strong across all models it is possible that there is an
error in the data or that the data was too general to produce an accurate result. Also the
unemployment rate represents a much greater value than the suicide rates. These are things to
keep in mind but it is safe to say that there is a relationship between these two variables that
should not be ignored.
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Conclusion
To sum up all the data analysed, it is apparent that there is a strong positive correlation between
the rate of unemployment and suicide rates through all the regression models in the two variable
analysis. The background information and the one variable analysis support this conclusion as
well. This confirms my hypothesis that an increase in unemployment rates will cause an increase
in suicide rates.
The main problem with this comparison is that there are countless external variables as this is not
a simple issue. I listed only a few variables that may have an effect on the unemployment and
suicide rates. This puts a limitation on how accurate the model is because there is no way to
pinpoint the actual cause for unemployment or the cause for suicide from this data alone. A
solution to this would be to simplify the sample. For example, instead of unemployment rates in
Canada, a more precise grouping would be unemployment rates in males ages 25-45 in Canada.
Even with that specification there are still a lot of variables involved like the reason behind their
unemployment which is hard to pinpoint. For suicide rates in Canada, I could have examined
suicides casued by financial stress for the same age group in males. The problem that arises is
that the parameters are too specific to find data for. Secondly the number of deaths would be so
small it would almost be irrelevant when put next to unemployment rates. For these various
reasons, I kept the two variables very broad to get a good representation of the overall picture. I
think the 30 data points were enough, but it is always possible to extend that time frame as long
as the data is available. I do not think that it would have a significant effect on the two variable
graphs I have currently.
Even with the problems and external variables discussed, a strong correlation coefficient of
around 0.88, and in the worst case 0.8 should not be ignored. At the very least I can conclude
that unemployment rates areacause for suicide ratesbut they are not the only casue. Also not all
types of unemployment will have a direct cause on suicide rates. This argument is also supported
by the similar study done by Blakely, Collins, and Attickson in New Zealand which found that
unemployment caused 2-3 times more deaths by suicide compared with employment. Overall my
hypothesis was proven given the data I presented, and the results were beneficial and
eye-opening.
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Appendix A: Data Tables
Table 1: Unemployment Rates in Canada (%) from 1900 to 2021
(https://ourworldindata.org/grapher/unemployment-rate?tab=chart&time=1991..latest&country=~CAN)
YearRate of Unemployment (%)
1900NA
199110.32
199211.2
199311.38
199410.4
19959.49
19969.62
19979.1
19988.28
19997.58
20006.83
20017.22
20027.67
20037.57
20047.19
20056.76
20066.32
20076.04
20086.14
20098.34
20108.06
20117.51
20127.29
20137.07
20146.91
20156.91
16
https://ourworldindata.org/grapher/unemployment-rate?tab=chart&time=1991..latest&country=~CAN
20167
20176.34
20185.83
20195.66
20209.48
2021NA
Table 2: Suicide Rates in Canada from 1990 to 2021
(https://ourworldindata.org/grapher/suicide-rates-by-age-detailed?time=earliest..latest&country=~CAN)
YearSuicide rate (per 100,000)
199014.67
199114.64
199214.75
199315.03
199414.9
199514.99
199614.92
199714.71
199814.51
199914.53
200014
200113.88
200213.71
200313.79
200413.48
200513.54
200613.16
200713.25
200813.27
200913.22
201013
17
https://ourworldindata.org/grapher/suicide-rates-by-age-detailed?time=earliest..latest&country=~CAN
201112.68
201212.99
201312.88
201413.23
201513.37
201613.04
201712.83
2018NA
2019NA
2020NA
2021NA
Table 3: Unemployment Rates and Suicide Rates in Canada
YearRate of Unemployment (%)Suicide Rate (per 100,000)
1990NA14.67
199110.3214.64
199211.214.75
199311.3815.03
199410.414.9
19959.4914.99
19969.6214.92
19979.114.71
19988.2814.51
19997.5814.53
20006.8314
20017.2213.88
20027.6713.71
20037.5713.79
20047.1913.48
20056.7613.54
18
20066.3213.16
20076.0413.25
20086.1413.27
20098.3413.22
20108.0613
20117.5112.68
20127.2912.99
20137.0712.88
20146.9113.23
20156.9113.37
2016713.04
20176.3412.83
20185.83NA
20195.66NA
20209.48NA
2021NANA
Appendix B: Calculations
Section 1: Independent Variable
MinQ1MedianQ3Max
Values5.666.857.48.9111.38
MeanModeRangeIQRStandard DVariance
7.8503333336.915.722.061.5627699412.442249889
Section 2: Dependant Variable
MinQ1MedianQ3Max
Values12.6813.20513.62514.647515.03
MeanModeRangeIQRStandard DVariance
13.82035714#N/A2.351.44250.76976565150.5925391582
19
Link to Spreadsheet
https://docs.google.com/spreadsheets/d/1ajVRvOPsh4ePIorIKVz2FnjAUjCO4BrWUfNtztVSxm
w/edit?usp=sharing
20
https://docs.google.com/spreadsheets/d/1ajVRvOPsh4ePIorIKVz2FnjAUjCO4BrWUfNtztVSxmw/edit?usp=sharing
https://docs.google.com/spreadsheets/d/1ajVRvOPsh4ePIorIKVz2FnjAUjCO4BrWUfNtztVSxmw/edit?usp=sharing
Bibliography:
Amadeo, Kimberly. “When Unemployment Spirals out of Control.”The Balance, The Balance,
18 Oct. 2020,
https://www.thebalance.com/cyclical-unemployment-3305520#:~:text=The%20cyclical%
20unemployment%20rate%20is,why%20each%20person%20is%20unemployed.
Blakely, T A, et al. “Unemployment and Suicide. Evidence for a Causal Association?”Journal of
Epidemiology and Community Health, BMJ Group, Aug.2003,
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1732539/.
Canada, Public Health Agency of. “Government of Canada.”Canada.ca, / Gouvernement Du Canada, 17
Sept. 2021,
https://www.canada.ca/en/public-health/services/suicide-prevention/suicide-canada.html.
Centers for Disease Control and Prevention. “Facts about Suicide.”Centers for Disease Control and
Prevention, Centers for Disease Control and Prevention,30 Aug. 2021,
https://www.cdc.gov/suicide/facts/index.html.
Global Health Data Exchange. “Global Health Data Exchange.”GHDx,http://ghdx.healthdata.org/.
Global Partnership for Financial Inclusion.WorldBank Enterprise Surveys: Methodology.
http://datatopics.worldbank.org/g20fidata/help/GPFI%20Methodology_11212012.pdf.
Institute for Work & Health. “Unemployment and Mental Health.”Institute for Work & Health,
https://www.iwh.on.ca/summaries/issue-briefing/unemployment-and-mental-health#:~:text=found
%20that%20becoming%20 unemployed%2C%20
inadequately,being%20highly%20educated%20increased%20it.
International Labour Organization. “About the Ilo.”About The,
https://www.ilo.org/global/about-the-ilo/lang–en/index.htm.
Olsen, Robert. “Men and Suicide.”Centre for SuicidePrevention, 14 Feb. 2017,
https://www.suicideinfo.ca/resource/menandsuicide/.
Our World in Data. “Suicide Rates by Age.”Our Worldin Data,
https://ourworldindata.org/grapher/suicide-rates-by-age-detailed?time=earliest..latest&country=~
CAN.
Our World in Data. “Unemployment Rate, 1991-2020.”OurWorld in Data,
https://ourworldindata.org/grapher/unemployment-rate?tab=chart&time=1991..latest&country=~
CAN.
21
https://www.thebalance.com/cyclical-unemployment-3305520#:~:text=The%20cyclical%20unemployment%20rate%20is,why%20each%20person%20is%20unemployed
https://www.thebalance.com/cyclical-unemployment-3305520#:~:text=The%20cyclical%20unemployment%20rate%20is,why%20each%20person%20is%20unemployed
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1732539/
https://www.canada.ca/en/public-health/services/suicide-prevention/suicide-canada.html
https://www.cdc.gov/suicide/facts/index.html
http://ghdx.healthdata.org/
http://datatopics.worldbank.org/g20fidata/help/GPFI%20Methodology_11212012.pdf
https://www.iwh.on.ca/summaries/issue-briefing/unemployment-and-mental-health#:~:text=found%20that%20becoming%20unemployed%2C%20inadequately,being%20highly%20educated%20increased%20it
https://www.iwh.on.ca/summaries/issue-briefing/unemployment-and-mental-health#:~:text=found%20that%20becoming%20unemployed%2C%20inadequately,being%20highly%20educated%20increased%20it
https://www.iwh.on.ca/summaries/issue-briefing/unemployment-and-mental-health#:~:text=found%20that%20becoming%20unemployed%2C%20inadequately,being%20highly%20educated%20increased%20it
https://www.ilo.org/global/about-the-ilo/lang–en/index.htm

Men and Suicide


https://ourworldindata.org/grapher/suicide-rates-by-age-detailed?time=earliest..latest&country=~CAN
https://ourworldindata.org/grapher/suicide-rates-by-age-detailed?time=earliest..latest&country=~CAN
https://ourworldindata.org/grapher/unemployment-rate?tab=chart&time=1991..latest&country=~CAN
https://ourworldindata.org/grapher/unemployment-rate?tab=chart&time=1991..latest&country=~CAN
MDM4U Project REPORT Evaluation

Strand E
E1
Design and carry out a culminating investigation that requires the integration and application of the
knowledge and skills related to the expectations of this course.

E2
Communicate the findings of a culminating investigation and provide constructive critiques of the
investigations of others.

Level
0 – R + – 1 + – 2 + – 3 + – 4 + ++
Introduction
● Background Information
Information to set the scene,
explain the reason for looking at
the variables chosen and a
summary of other reports looking
at the same concepts
No
evidence
Very little
evidence –
Mentioned but
no explanation
Topic mentioned
with brief
explanation
Level 1 +
reasoning for
looking at the
variables
Level 2 +
some
additional
details and
statistics

Level 3 + summary
of other reports
looking at the same
concepts

● Thesis Question Explained
Clear thesis title, hypothesis
stated andpurpose explained
No
evidence
Very little
evidence –
Mentioned but
no explanation
Mentioned with
Hypothesis
Level 1 + with
purpose
explained
Level 2+
reference to
variables
and trends
Level 3 + rationale
link with additional
details.
● Variables Defined
Main variables being investigated
are stated and explained, with
additional variables discussed
No
evidence
Very little
evidence –
Mentioned but
no explanation
Main variables
listed and
defined.
Level 1 +
additional details
explained
Level 2+
additional
variable
discussed
Level 3 + with links
between main
variables and
additional variables

● Causation Discussed
Causal link between the main
variables discussed, with
extraneous variables highlighted
No
evidence
Very little
evidence –
Mentioned but
no explanation
causal link
between
variables
discussed
Level 1 + valid
reasoning
Level 2+
extraneous
variables
highlighted
and linked to
thesis using
proper
terminology
Level 3 + additional
rationale and
evidence
Analysis of Data
● Collection of Effective Data
No
evidence
Very little
evidence –
Mentioned but
no explanation
Data Collection
covering less
than 30 linked
data points
Data Collection
covering 30 sets
but somes were
not linked
Data
Collection
covering 30
sets of
linked data
points
Data Collection
covering more than
30 sets of linked
data points
● Clear Organization of Data
No
evidence unorganized
organized but
not broken into
sections
Level 1 +
organized with
titles but flows is
hard to follow
Level 2 +
organized
with titles,
flows nicely
and is easy
to read
Level 3 + with a
professional look
● Validity and Authenticity of
Data
No
evidence
Very little
evidence –
Mentioned but
no explanation
Sources integrity
are discussed
Level 1 + with
evidence
Level 2+
Data
integrity
discussed
Level 3 + sources
sampling
techniques
discussed
● Bias
No
evidence
Very little
evidence –
Mentioned but
no explanation
Bias within Data
discussed
Level 1 +
discussion on
how Data Bias
was overcome
Level 1 +
Bias of
Sources
discussed
3 + Bias of
sources sampling
techniques
discussed
● 1-variable graphs
No
evidence
Very little
evidence –
Mentioned but
no explanation
one type of
graph per
variable
Level 1 + lines
and bar graphs
properly labeled
and easy to read
Level 2+
frequency
histograms
& box plots
properly
labelled and
easy to read
Level 3 + a variety
of graphs showing
various statistics
● Measures of Central
Tendency
No
evidence
Very little
evidence –
Mentioned but
no explanation
Calculations
complete with
comparison
analysis
Level 1 +
significance to
variable
discussed.
Level 2+
link thesis
discussed
Level 3 + detailed
analysis of the
statistical relevance
to your thesis and
trends.
● Measures of Spread
No
evidence
Very little
evidence –
Mentioned but
no explanation
Calculations
complete with
comparison
analysis
Level 1 +
significance to
variable
discussed.
Level 2+
link thesis
discussed
Level 3 + detailed
analysis of the
statistical relevance
to your thesis and
trends.
● Distribution of Data
No
evidence
Very little
evidence –
Mentioned but
no explanation
Distribution
stated with
justification
Level 1 +
additional
justification
Level 2+
link thesis
discussed
Level 3 + detailed
analysis of the
statistical relevance
to your thesis and
trends.
● 2-variable graphs
No
evidence
Very little
evidence –
Mentioned but
no explanation
Scatter plots
correctly done
by using each
main variable
Level 1 + with
trendline, shown

Level 2 +,
equation of
LOBF and R
values
shown
Level 3 + multiple
significant graphs
done to level 3
● Correlation
No
evidence
Very little
evidence –
Mentioned but
no explanation
Correlation value
discussed
Level 1 + linked
to both variables
Level 2+
significance
to variables
discussed
Level 3 + linked to
thesis and trends
● Trends
No
evidence
Very little
evidence –
Mentioned but
no explanation
Trends
discussed
Level 1 + linked
to both variables
Level 2+
linked to
thesis of
project
Level 3 + additional
significant details
discussed.
Conclusions
● Problems and Limitations
No
evidence
Very little
evidence –
Mentioned but
no explanation
Listed a few
issues
Level 1 + impact
on overall results
Level 2+
effect on
overall
project
discussed
Level 3 +
discussion of
remedies
● Other Factors and
Extension
No
evidence
Very little
evidence –
Mentioned but
no explanation
Mentioned
Factors or
Extensions with
little explanation
Mentioned
Factors and
Extensions with
little explanation
Mentioned
ways the
project could
be extended
with great
detail
Level 3 + additional
factors affecting
your project in
great detail
● Project Factors Tied
Together
No
evidence
Very little
evidence –
Mentioned but
no explanation
at times shows
the links
between
different
statistics
Level 1 + shows
continuing
trends
throughout
different stages
of the project
Level 2+
links multiple
statistics
together
while
evolving
justifications
Level 3 + links all
statistics and
factors together
while formulating
justifications.
● Valid Conclusions Drawn
No
evidence
Very little
evidence –
Mentioned no
explanation
conclusion
drawn without
justification
Level 1 +
appropriate
conclusion
discussed
Level 2+
links back to
details
discovered
through the
project
Level 3 + additional
detailed used when
formulating
conclusion
● Bibliography and Citations
No
evidence
Very little
evidence –
Mentioned but
no explanation
one source
listed correctly
Level 1 +
additional
sources used
Level 2+
several
sources used
and cited
throughout
the project
Level 3 +several
sources used and
cited throughout.
Report Organisation and
Readability
No
evidence
project flow is
difficult to
follow
project has a
smooth flow
Level 1 + split
into sections and
labelled
appropriately
Level 2+
easy to read
with a nice
flow
Level 3 +
professional looking

✔MDM4U Summative REPORT Evaluation

Content:
E1
Design and carry out a culminating investigation that requires the integration and application of the
knowledge and skills related to the expectations of this course.
4-
E2
Communicate the findings of a culminating investigation and provide constructive critiques of the
investigations of others.

Level
0 – R + – 1 + – 2 + – 3 + – 4 + ++
Introduction
● Background Information
Information to set the scene, explain
the reason for looking at the variables
chosen and a summary of other reports
looking at the same concepts
No
evidence
Very little
evidence –
Mentioned but
no explanation
Limited
evidence
Some
evidence
Considerable
evidence
Thorough evidence
+
● Thesis Question Explained
Clear thesis title, hypothesis stated and
purpose explained
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Variables Defined
Main variables being investigated are
stated and explained, with additional
variables discussed
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Causation Discussed
Causal link between the main variables
discussed, with extraneous variables
highlighted
No
evidence
Very little
evidence –
Mentioned but
no explanation

Analysis of Data
● Collection of Effective Data
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Clear Organization of Data
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Validity and Authenticity of
Data
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Bias
No
evidence
Very little
evidence –
Mentioned but
no explanation

● 1-variable graphs
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Measures of Central Tendency
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Measures of Spread
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Distribution of Data
No
evidence
Very little
evidence –
Mentioned but
no explanation

● 2-variable graphs
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Correlation
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Trends
No
evidence
Very little
evidence –
Mentioned but
no explanation

Conclusions
● Problems and Limitations
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Other Factors and Extension
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Project Factors Tied Together
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Valid Conclusions Drawn
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Bibliography and Citations
No
evidence
Very little
evidence –
Mentioned but
no explanation

Report Organisation and
Readability
No
evidence
Very little
evidence –
Mentioned but
no explanation

Analyzing and Identifying the Effects of
Immigration Rates on Unemployment Rates,
in Canada

MDM4U

NAME
Teacher
Date
Introduction
Immigration and Unemployment rates in Canada are two large factors that are
continuously monitored to track the country’s stability and well-being in all aspects.
However, it is important to note whether these two variables have any bearing on one
another in order to accurately monitor and predict the well-being of Canada’s economy
more thoroughly. To do this, it is critical that a large set of data and other extraneous
factors are analyzed in order to gather more accurate conclusions.

Background Information
In any case, background information is necessary in order to fully understand the
two variables at hand. One can not gather accurate conclusions on how these variables
work together without knowing how they function and affect Canada independently.

As we begin with Immigration in Canada, it is important to note that immigrants
come to Canada for a variety of reasons. The main categories that Immigrants fall under are
as follows, Family-Class, Economic, Refugees, and Other. Family-Class Immigrants are those
that are related to Canadian Citizens. Typically, their main reason for immigrating is that
they wish to be reunited with their families. Economic Immigrants are those who come to
Canada and bring certain skills to further themselves career-wise. They are needed
especially to boost the economy, bring new ideas, as well as to fill and create new positions.
Finally, Refugees are considered to be those who are trying to leave their countries due to
unsafe circumstances/living conditions.
Furthermore, immigrants typically have to endure a screening process to see if they
fit the criteria and qualifications of living in Canada. For instance, when Economic
Immigrants go through a screening process, they are sorted based on how well they can
contribute to Canada’s economy. This includes job offers, work experience, education level,
etc.As time has passed, immigrants have become more and more of an essential staple of
Canada’s economy. A fairly significant reason is due to the lack of children being born over
time to support Canada’s growing economy. Immigration, Refugees, and Citizenship Canada
has mentioned that as time passes, Canadians are having less and less children or none at
all. As such, immigrants have become more important than ever to keep jobs filled with
those who have the proper qualifications.

In addition to Immigration in Canada, Unemployment rates are continuously
monitored in order to keep the economy prospering. It can be noted that unemployment
rates depend on a variety of factors. Many are unable to find work, do not have the right
experience, do not have the necessary level of education, etc. In addition, unemployment
rates can be affected rapidly by economic crises such as The Great Depression, when many
around the country are left unemployed. We can see that during The Great Depression, the
unemployment rate skyrocketed because of this. A key factor in determining how well or
healthy the economy is, is by taking a look at the unemployment rate and how fast or slow
it is increasing or decreasing. The unemployment rates in Canada are measured and
tracked constantly to ensure that the economy is monitored and stable.

Commented [1]: what specifically defines
“Unemployment Rate”?
Purpose
The purpose of this study is to discover whether or not Immigration Rates have any
bearing on Unemployment Rates in Canada. This will help us to discover whether
Immigration Rates are a good indicator in the monitoring of the economy’s stability.

Thesis
Immigration rates in Canada have a significant impact on the Unemployment rates
and help to indicate how well the economy is doing. Among many other things, immigrants
offer many new ideas and skill sets, aiding Canada in the creation of new jobs and keeping
the population stable. These traits will aid Canada in the future and will help to keep
unemployment rates under control.

Hypothesis
As we look at Immigration Rates in Canada compared to its Unemployment Rates, it
can be inferred that as Immigration Rates increase, the Rate of Unemployment decreases.
This inference can be made because an increase in immigrants in Canada will ensure that
there is a more diverse range of skill sets, ideas, ethnicities, etc. that have a large impact on
the creation of new job positions. Immigrants also hold a very important role of keeping the
population stable and thus helping the country prosper.

Defining Variables
Throughout this study, it is important to note that our independent variable in this
case is Immigration in Canada. Immigration is a factor which can be easily monitored and
controlled when only allowing those who have been screened in to enter the country. This
dataset will cover all immigrants as a whole, regardless of their age, gender, or where they
immigrated from.
Unemployment rates on the other hand can be noted as our dependent variable
because it is not as easily controlled and it relies on many other factors like, economic
crises, job demand, qualifications, education level, etc. It is a variable that can be
unpredictable at times and thus why we are analyzing the effects that Immigration may or
may not have on it. In this study, Unemployment Rates will be monitored for everyone 15
years or older, regardless of gender.

Extraneous Variables
It should be noted that both of these variables rely on an abundance of other outside
factors and that is why a true cause and effect relationship may be difficult to identify
based on the data set alone. A list of main factors/points of discussion can be found below
as well as in the Mind Map Section.
Factors Affecting Immigration Rates in Canada
Many of these outside factors include but are not limited to…
❖ Age
❖ Gender
❖ Family
Commented [2]: you should show some of these stats
and see if there are common trends
➢ Do they have a large family? Small family? Do they have kids?
❖ Marital Status
➢ Are they married? Single?
❖ Prior Living conditions
➢ Were they living in safe, stable conditions before?
❖ Occupation/degree
➢ Do they have a prior occupation that they wish to continue to
pursue?
➢ Are they looking for a new position?
➢ Do they already have a position lined up?
❖ Education
➢ Have they already finished school?
➢ Are they looking to finish school here?
➢ What level of education have they completed?
❖ Income
➢ Do they have a stable income?
➢ What is their income? Is it enough to support themselves?

Factors Affecting Unemployment Rates in Canada
Many of these outside factors include but are not limited to…

❖ Economic Crisis
➢ Is the region experiencing loss of jobs because of extreme
circumstances?
➢ Are there any unusual, rare circumstances?
❖ Education
➢ Do they have the required level of education for the available
position?
➢ What level of education have they achieved?
❖ Job opportunities
➢ Are there jobs available in their area?
➢ Are there jobs available in their chosen occupation or field of
interest?
❖ Job demand
➢ Is the job in high demand?
➢ Is the occupation one that is competitive and hard to achieve after
having finished school?
❖ Age
➢ Are they the legal working age in Canada?
➢ Are they able and healthy to work?
❖ Qualifications/experience
➢ Do they meet the required qualifications of the job they are seeking?
➢ Do they have the needed experience?
➢ Is that experience easily attainable?

Mind Maps/Brainstorming
Plan of Action
1. Once the thesis statement has been finalized, I will begin finding a larger data set for the
same time period for both Immigration and Unemployment in Canada.
2. By looking at the raw data, I will start to identify whether there is any sort of correlation
during this time period.
3. Create both One-Variable and Two-Variable graphs to compare and contrast the rise and fall
of both rates.
4. Do some research to see if during that time period, there were any unique cases or outside
factors that need to be accounted for.
5. Take note of sources used that helped make any conclusions.
6. Calculate and examine the Central Tendency, the Measures of Spread and Distribution Type
on the One- Variable graphs to further explore the correlation.
7. Calculate and examine any correlation or trends seen on the Two-Variable graphs to
strengthen my findings.
8. Determine and conclude whether or not there is a correlation between Immigration and
Unemployment in Canada.
9. Determine why that correlation exists by doing some additional research.
10. Put together all of the research, raw data, graphs, etc. to create a report that proves or
disproves my original thesis.

Problems Encountered
As I began my research, I stumbled across many obstacles along the way. For
instance, near the very beginning, I had only used a data set spanning 10 years. However, I
later came to realize that this data set was too small for my two unpredictable variables. I
knew that I would not get as accurate results this way so I broadened my time frame to
cover a much larger time period. In addition, I found it difficult to make accurate
conclusions due to the amount of different factors, groupings, sub-groupings, etc. To
combat this, I made sure that both data sets for Immigration and Unemployment were
inclusive of all ages, genders, etc. This way, we could make more generalized statements
with less confusion.
Another issue I tried to avoid, was bias. Most people have preconceived notions
going into research with an idea of how the results will turn out based on opinion and
background knowledge. In my case, I did not know much about Unemployment in Canada,
but was fairly familiar with most details regarding Immigration in Canada and the
screening process that goes with it. However, in order to avoid any bias, I made sure to find
data from reliable, non-bias sources like Statistics Canada and Immigration, Refugees and
Citizenship Canada as well as to do a lot of background research for both variables.
Government Websites like Statistics Canada, serve as a great, non-bias resource because
they continuously monitor and track information regarding the population by having
people answer surveys, take part in the Census every ten years, etc. They do not typically
skew the data in one way or another, but simply provide raw values and standard
background information.
Commented [3]: did you look into how these numbers
were acquired?was there any bias with the collection
methods?
Data
Chart 1- Number of Immigrants in Canada between 1985 and 2005
(Data retrieved from https://www150.statcan.gc.ca/n1/pub/11-630-x/11-630-x2016006-
eng.htm)
Year # of Immigrants landing in
Canada
1985 84 300
1986 99 400
1987 152 100
1988 161 600
1989 191 600
1990 216 500
1991 232 800
1992 254 800
1993 256 600
1994 224 400
1995 212 900
1996 226 100
1997 216 000
1998 174 200
1999 190 000
2000 227 500
2001 250 600
2002 229 000
2003 221 300
2004 235 800
2005 262 200

https://www150.statcan.gc.ca/n1/pub/11-630-x/11-630-x2016006-eng.htm
https://www150.statcan.gc.ca/n1/pub/11-630-x/11-630-x2016006-eng.htm
Chart 2- Number of People Unemployed in Canada between 1985 and 2005
(Data retrieved from https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1410005701)

***It can be noted that this accounts for everyone that is 15 years of age and older and
has been unemployed for any number of weeks, regardless of gender***
Year
# of people unemployed
(x1000)
1985 1368.1
1986 1274.2
1987 1193
1988 1069.5
1989 1060.8
1990 1158.3
1991 1479
1992 1605.2
1993 1642.3
1994 1515
1995 1393.8
1996 1428.4
1997 1372.4
1998 1267.8
1999 1181.7
2000 1081.8
2001 1161.8
2002 1269.3
2003 1283.3
2004 1232.1
2005 1168.6

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1410005701
Chart 3- Comparing Immigration and Unemployment in Canada, Between
1985 to 2005

Year
# of
Immigrants
landing in
Canada
# of people
unemployed
1985 84 300 1368100
1986 99 400 1274200
1987 152 100 1193000
1988 161 600 1069500
1989 191 600 1060800
1990 216 500 1158300
1991 232 800 1479000
1992 254 800 1605200
1993 256 600 1642300
1994 224 400 1515000
1995 212 900 1393800
1996 226 100 1428400
1997 216 000 1372400
1998 174 200 1267800
1999 190 000 1181700
2000 227 500 1081800
2001 250 600 1161800
2002 229 000 1269300
2003 221 300 1283300
2004 235 800 1232100
2005 262 200 1168600

1-Variable Graphs
Graph 1- Unemployment in Canada, between 1985 and 2005 (Scatterplot)

Graph 2- Analyzing the Number of Unemployed People in Canada, per Year
(Histogram)

Graph 3- Immigration in Canada, between 1985 and 2005 (Scatterplot)
Graph 4- Analyzing the Number of Immigrants Landing in Canada Between
1985 and 2005 (Histogram)

1-Variable Analysis
❖ Measure of Central Tendency
**It can be noted that a mode could not be found in both datasets, as no two
values ever repeat over the 21 years observed**

➢ Immigration Rates in Canada
Mean 205700
Median 221300
Mode N/A
While examining the Measures of Central Tendencyin the case of
Immigration Rates in Canada, many conclusions can be made. Firstly, it can
be noted that the average number of Immigrants landing in Canada is 205
700. Despite the amount that Immigration Rates fluctuate, the Mean helps to
get a better understanding of just how many Immigrants come to Canada, on
average. When examining the Median of 221 300, one can get an idea of how
large the number of Immigrants reached as time went on. Since this is the
mid-value of the data set and it is quite large, we can get a better
understanding of just how much the number of Immigrants landing in
Canada grew over time.
➢ Unemployment Rates in Canada
** Please note that all values of Unemployment in Canada must be multiplied by
1000 to get their true values. Values as is were too large.**

Mean 1295.542857
Median 1269.3
Mode N/A
Commented [4]: per year?Did you look at this per
province?Were there consistent trends?
As we examine the Measures of Central Tendency with
Unemployment Rates in Canada, it can be noted that they give us some
helpful information regarding our dataset. To begin, the Mean of 1 295 543 is
very high compared to Immigration in Canada. This shows just how
widespread the two data ranges are. The Mean being so high is an indicator
that the number of Unemployed people in Canada remained fairly high over
the 21 years we have studied. It can be inferred that beyond this dataset, the
same trends are seen but it is difficult to know for sure without expanding
our timeframe. The Median, noted above as 1 269 300, is also quite high like
the Mean. This is to no surprise as the dataset as a whole is fairly large.
Similarly to the case of Immigration Rates, this Median helps to picture what
the entire range looks like. If the Median is really high, it can be noted that
the number of Unemployed people in Canada still reached a much higher
amount during the time frame studied.

❖ Measure of spread
➢ Immigration Rates in Canada
Range 177900
Standard Dev. 47028.45137
Variance 2211675238

When taking a look at the Standard Deviation of (approx.) 47 028 in
the case of Immigration, it can be noted that it is quite large. In any case, the
Standard Deviation tells us how spread out or how much the values in a
Commented [5]: per year?
Commented [6]: approx 4% (of the range) from one
year to the next
given data set vary. In this case, because the Standard Deviation is so high,
we know that our data set varies quite a bit. This just goes to show how much
Immigration Rates change each year due to the abundance of different
variables mentioned earlier. Outside factors like, occupation, prior living
conditions, age, income, etc. can have a significant impact on how many
immigrants will end up coming to Canada each year.

➢ Unemployment Rates in Canada
** Please note that all values of Unemployment in Canada must be multiplied by
1000 to get their true values**

Range 581.5
Standard Devi. 164.8534546
Variance 27176.6615

Similarly to the Immigration Standard Deviation, the Standard
Deviation in this case is even larger. This makes sense since the number of
unemployed people each year always outweighs the number of immigrants.
The gap in both variables was quite large. The standard deviation of
(approx.) 164 853 in this case, is even more spread out than in the case of
Immigration. This shows just how much the number of unemployed people
fluctuates each year due to all of the extraneous variables that come into
play. Extraneous variables like any sort of economic crises going on, job
demand, opportunities available, etc. have a lot of bearing on how many
people will end up unemployed that year.

❖ Distribution of data
➢ Immigration Rates in Canada
As we take a look at the histogram displaying the number of
Immigrants landing in Canada each year (See Graph 4), it can be noted that
the graph is left-skewed. Just by examining the graph itself, we can see that
the peak is found shifted to the right side of the graph. By looking at the mean
and median, we can see that the Mean of205 700 is left of the Median, 221
300. These are key characteristics of a left-skewed graph and so it is clear
that the Immigration histogram is left-skewed. This type of skewed graph
tells us that there are more values found in the higher set of the data than the
lower. In this case, this means that over the course of the 21 years observed,
the majority of the waves of immigrants are on the higher side being between
150 000 and 300 000. This type of conclusion could only be seen by
analyzing the distribution type from a histogram.

➢ Unemployment Rates in Canada
After examining the histogram displaying the Number of Unemployed
People in Canada each year (See Graph 2), we can see that it portrays a right-
skewed graph. In a right-skewed graph, the Median is characterized as being
left of the Mean on the graph. As we look at our Measures of Central
Tendency Charts above, we can see that the Median (1 269 300) is less than
the Mean (1 295 543), meaning that the Median would be to the left of the
Mean. This positioning of the Mean and Median is a key characteristic of a
right-skewed graph and thus why it is so easy to spot. This type of skew is the
exact opposite case of Immigration Rates. Unlike Immigration Rates in
Canada, this histogram shows that the vast majority of values are found to
the left of the graph, in the lower end; meaning that most of the time, the
number of unemployed people stays on the lower end of the scale between 1
050 000 and 1 290 000.

2-Variable Graphs
Graph 5- Comparing Immigration and Unemployment in Canada (Scatterplot)

Graph 6- Comparing Immigration and Unemployment in Canada (Line Graph)

2-Variable Analysis
❖ Correlation Coefficient
r2= 0.108
r = √ 0.108
r = ∼ 0.3286
∴ Correlation Coefficient is 0.3286 (approx.) which displays a
positive, linear, and moderate-weak relationship.

Based on the 2-variable scatter plot above (Graph 5), it is evident that the
correlation between Immigration and Unemployment is positive but weak. When
simply using the one-variable graphs to make our analysis, it is much more difficult
to see if both variables have any sort of correlation to begin with. As the number of
immigrants increases, there is an ever so slight increase in the number of
unemployed people. If we take a look at the Correlation Coefficient (taking the
square root of r2 that’s listed on the graph), we get 0.3286. In order for there to be a
strong correlation, the coefficient would have to be as close to 1 as possible.
However, this correlation coefficient is closer to 0 if anything, indicating a
relationship on the weaker side.

Now if we link this back to our original thesis from the very beginning, I had
inferred that as the immigration rates increase, the employment rate would have to
decrease. This thesis statement was based on general knowledge and facts
regarding both variables. Now that there is actual statistics that disprove this thesis,
it is easier to understand why. Based on the Correlation Coefficient, it seems to show
that the two variables have very little bearing on one another. It is difficult to tell if
there is much of a correlation to begin with, when the Correlation Coefficient is so
low. The graph itself fluctuates a lot over time which also makes it more challenging.
Therefore, it can be said that based on the short time frame analyzed, an increase in
Immigrants will only have a very small effect, if any, on the Unemployment Rate
each year in Canada.

❖ Trends
Based on solely the line of best fit on the 2-variable graph, we can see how
the line itself is not very steep at all, indicating a weak trend between both variables.
However, as mentioned above, we can still see a slight increase in the number of
unemployed people as the number of Immigrants increases. This slight trend could
simply be coincidental based on the short time period analyzed, however this is
difficult to identify based on the little information we know. At the very least, the
graph shows a slight trend when we move closer to the right of the graph (as the
number of immigrants increases) where the number of unemployed people
increases ever so slightly.This proves that yes- Immigration may cause a slight
increase in the number of unemployed people. However, this trend is ever so slight
and may also be due to other outside factors. In order to make valid conclusions and
to uncover a more solid trend, more data and information is needed.

Conclusion
All in all, it can be said that Immigration Rates have very little, if any, bearing on
Unemployment rates in Canada. My findings have disproved my original hypothesis stating
that as Immigration Rates increase, the Rate of Unemployment decreases. This original
statement was based off of background information and general inferences regarding the
two topics. However, as the two variables were compared side by side, it was evident that
the relationship between the two was close to non-existent. Based on the Standard
Deviations in both cases, it was clear that both datasets fluctuated quite a bit. This relates
back to the fact that there are an abundance of extraneous variables that can affect them
both, as explored earlier on. Based on the Correlation Coefficient, which was explored in
the comparison of both variables, it was noted that it was extremely low at approximately
0.3. This lends itself to my final conclusions, because it solidifies the notion that the
correlation between both variables is very weak or moderate at best. This shows how little
both variables affect one another. At the very least, the Two-Variable graphs demonstrated
a slight trend where the number of unemployed people increased ever so slightly as the
number of immigrants increased.

Just based on the range of data that was covered throughout my research, it is
difficult to truly know the effect that these two variables have on one another. This
research was solely based on 21 years of data so it is entirely possible that this slight trend
was just coincidental or that there were other extraneous variables that came into play
during the time frame analyzed. Variables like economic crises, job demand, prior living
conditions, age, income, and so many more are just some of the few extraneous factors that
could have played a role.

Simply based on the known background information as well as the small dataset
examined, it is evident that contrary to my original hypothesis, Immigration Rates have
very little, if any, effect on Unemployment Rates in Canada. Therefore, using Immigration
Rates in Canada to predict and track Unemployment Rates would not be beneficial nor
accurate due to the lack of a strong correlation.

Bibliography
Immigration, Refugees, and Citizenship Canada. (2019, August 13). Canada’s immigration
track record. Retrieved from https://www.canada.ca/en/immigration-refugees-
citizenship/campaigns/immigration-matters/track-record.html#economy
Immigration, Refugees, and Citizenship Canada. (2020, March 12). #ImmigrationMatters.
Retrieved from https://www.canada.ca/en/immigration-refugees-
citizenship/campaigns/immigration-matters/system.html
Statistics Canada. (2018, May 17). 150 years of immigration in Canada. Retrieved from
https://www150.statcan.gc.ca/n1/pub/11-630-x/11-630-x2016006-eng.htm
Statistics Canada. (2020, January 10). Duration of unemployment, annual. Retrieved from
https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1410005701
Yarhi, E. (n.d.). Unemployment in Canada. Retrieved from
https://www.thecanadianencyclopedia.ca/en/article/unemployment

MDM4U Summative REPORT Evaluation

Content:
E1
Design and carry out a culminating investigation that requires the integration and application of the
knowledge and skills related to the expectations of this course.
4
E2
Communicate the findings of a culminating investigation and provide constructive critiques of the
investigations of others.


Level
0 – R + – 1 + – 2 + – 3 + – 4 + ++
Introduction
● Background Information
Information to set the scene, explain
the reason for looking at the variables
chosen and a summary of other reports
looking at the same concepts
No
evidence
Very little
evidence –
Mentioned but
no explanation
Limited
evidence
Some
evidence
Considerable
evidence
Thorough evidence
+
● Thesis Question Explained
Clear thesis title, hypothesis stated and
purpose explained
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Variables Defined
Main variables being investigated are
stated and explained, with additional
variables discussed
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Causation Discussed
Causal link between the main variables
discussed, with extraneous variables
highlighted
No
evidence
Very little
evidence –
Mentioned but
no explanation

Analysis of Data
● Collection of Effective Data
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Clear Organization of Data
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Validity and Authenticity of
Data
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Bias
No
evidence
Very little
evidence –
Mentioned but
no explanation

● 1-variable graphs
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Measures of Central Tendency
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Measures of Spread
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Distribution of Data
No
evidence
Very little
evidence –

Mentioned but
no explanation
● 2-variable graphs
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Correlation
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Trends
No
evidence
Very little
evidence –
Mentioned but
no explanation

Conclusions
● Problems and Limitations
No
evidence
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evidence –
Mentioned but
no explanation

● Other Factors and Extension
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Project Factors Tied Together
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Valid Conclusions Drawn
No
evidence
Very little
evidence –
Mentioned but
no explanation

● Bibliography and Citations
No
evidence
Very little
evidence –
Mentioned but
no explanation

Report Organisation and
Readability
No
evidence
Very little
evidence –
Mentioned but
no explanation

Commented [1]: conclusions are valid, but you did not
tie the one variable and two variable stats together to
provide continuity of trends.
Population Density vs. Life expectancy at birth

A high population density is associated with poor sanitation, lack of public facilities and
environmental degradation. However it is also associated with the increase in physical activity
and a less car use [1]. Rising population density has also been linked with a more stimulated
economy via increased demand in jobs among other factors [2] leading to an increased quality
of life. Studies have also been conducted concluding in a positive correlation with maternal
health and population density.[3] Maternal health will without a doubt affect infant health
therefore affecting their lifespan as a whole. On the second variable, life expectancy, such a
measure is calculated depending on many different factors [4] so I concluded that taking
samples from different countries in different situations around the world would give the best view
of how it compares to population density. One of these factors is the details of a person’s death
(age, gender, race, cause, ect..)which is collected by census and vital statistics data in the
country being evaluated. Thus the accuracy of the calculated life expectancy heavily depends
on the accuracy of the census and vital statistics data. Multiple mistakes or intentional
tampering could lead to extremely skewed results. Because of the many variables in calculating
life expectancy cannot be used to determine an individual’s age of death however it can be used
to estimate roughly how long they might live [4-5]. For this reason and to limit variability I have
chosen to use life expectancy at birth. On top of obvious variability such as age, this eliminates
factors such as unhealthy habits.

After researching my topic, I found a lot of evidence that points towards there being a link
between Population density and Life expectancy however these links were always through
another variable such as pollution. There was evidence to suggest that an increase in
population density resulted in an increase of factors that would increase life expectancy such as
quality of life and the opposite was true as well.These two variables seem to have a lot of
variables in between them so finding a direct link is unlikely. So the question becomes does
increasing population density in different countries cause the factors that affect life expectancy
change positively or negatively in those countries.

Hypothesis: Heavily depending on the target country’s economic status, an increase in
population density will result in a decrease in life expectancy

I have already found a few reputable sites that have compiled raw data about my two variables.
World-O-Meter : for population density
https://www.worldometers.info/world-population/
WHO: Has compiled data on life expectancy around the world
https://www.who.int/gho/publications/world_health_statistics/2016/Annex_B/en/
Our World in Data: Have graphs that have been analyzed as well as the raw data behind the
graphs about life expectancies
https://ourworldindata.org/life-expectancy
OECD: Raw data about life expectancy
https://data.oecd.org/healthstat/life-expectancy-at-birth.htm

https://www.worldometers.info/world-population/
https://www.who.int/gho/publications/world_health_statistics/2016/Annex_B/en/
https://ourworldindata.org/life-expectancy
https://data.oecd.org/healthstat/life-expectancy-at-birth.htm
Example of data
Life expectancy by country source: (WHO)
Country Life expectancy in years
Afghanistan 60.5
Albania 77.8
Algeria 75.6
Andorra –
Angola 52.4
Antigua and Barbuda 76.4
Argentina 76.3
Armenia 74.8
Australia 82.8
Austria 81.5
Azerbaijan 72.7
Bahamas 76.1
Bahrain 76.9
Bangladesh 71.8
Barbados 75.5
Belarus 72.3
Belgium 81.1
Belize 70.1
Benin 60.0
Bhutan 69.8
…(actual table too large) …(actual table too large)

https://www.who.int/gho/publications/world_health_statistics/2016/Annex_B/en/
The goal is to explore the two variables of population density per country and human life
expectancy at birth and how these two variables may or may not relate to each other.
There are a lot of possibilities of how these two could be related which were talked
about in phase one.

Possible Thesis: For an independent nation. Controlling its population should be among
that nation’s top priorities as this impacts how long/heatly that same nation’s citizens’
lives will be affected.

Population density: This variable is the measure of some living entity per a given area.
In this case it would be humans living in a given area per nation. I will be attempting to
use the most recent year’s data. Usually the area will be in km2. However this changes
depending on what category is involved. For countries km2is the most appropriate.
Population density is to be the independent variable in the thesis.

Life expectancy: This is the average number of years a living being will be alive for. In
this case it would be the average human lifespan for a given nation. Life expectancy at
birth will be used because this will remove the number of personal life choices that may
affect life expectancy entirely. There will be no separation between men and women as
it does not factor into the thesis. This variable is calculated based on multiple factors
such as air pollution and quality of life. Life expectancy is to be the dependent variable
in the thesis.

In order to prove the thesis will be proven when a significant correlation relationship is
found between the two variables, however it is more likely that the variables that control
the life expectancy calculation therefore if a significant amount of these variables also
correlate with population density such as pollution then the thesis will also be proven.

The most likely type relationship to exist is that of correlation on many levels. Population
density is likely to affect eating habits, exercise, pollution, and disease among many
other and these variables also affect life expectancy because they affect an individual’s
health which affects how quickly someone will die and that is one of the factors that go
into calculating population density. If the link is causation, in order to prove this the
extraneous variables involved would have to be random and the results no matter what
the extraneous variables were would need to be constant. The variables here would be
,for example, violent crime rate in the country if the change in this variable does not
affect the manner in which the dependent variable affects the independent variable. If
the variable did in fact affect how the dependent and independent variables affected
each other then this would mean their relationship is not that of cause-effect.

Some possible extraneous variables other than the ones already mentioned would be:
– The country’s GDP
– Active deadly diseases at the time of evaluation
– Average living conditions of citizens
– Average Living expenses in the country

On the other hand, if an unknown extraneous variable were to exist then that variable
could affect the conclusions drawn from the data. If a variable x was increasing life
expectancy as x went up and but also decreasing population density at the same time
the false conclusion of if population density goes down than life expectancy goes up
could occur. Incorrect or biased data could also very much lead to incorrect conclusions
as well. For example, if certain data was omitted because that better fit the conclusions
that the collector was looking for or if the data was incorrectly collected than any
conclusions drawn from that data would be invalid.

World-O-Meter : for population density
https://www.worldometers.info/world-population/
Raw data is available however you need to compile it yourself

WHO data sheet shared.

Background research on sources:

World-o-meter: This website is a trusted authority and is used by many governments
and organizations including the UK government and the BBC. The site has also been
cited in over 10000 published books

W.H.O: The World Health Organization is a branch of the UN. It works in collaboration
with member nations.

Knoema: A team of around 60 data engineers, economists, developers, and
entrepreneurs. They have 3 PhDs, 52 Advanced degrees, and years of software and
data engineering experience.

CIA: The Central Intelligence Agency is a United States government agency. Their main
mission is to collect what they call intelligence to the President of the United States, the
National Security Council and any other government agency that may need that
intelligence. When it is deemed possible they also make their data public.

Indexmundi: A website that collects raw data from published articles and turns them
into visual data while also displaying the raw data itself

Commented [2]: when evaluating your sources, did
you look into how they obtained their data?sampling
techniques?
https://www.worldometers.info/world-population/
World Air Quality Index: A website that covers 100 countries, covering more than
12,000 air quality stations in 1000 major cities. They display historical and real time data
about the air quality of specific areas around the world.

Variables

2.5 PM: These are particles in the air that are 2.5 microns in width. Usually measured in
dedicated stations for a local area. Also a popular measure of air quality and pollution,
Units are μg/cm³ of air. This will be taken as the average for the country in a given year.

Life expectancy at birth for both males and females: This is the average lifespan of
a person in years. This depends on multiple factors, one of which is air pollution.

Population density: This is the measure of a country’s population/an area usually
km^2.
Countries compared to each other

1-variable analysis
Country
Life expectancy
at birth for both
males and
females (Years)
Population
Density P/Km²
PM 2.5 Air
Pollution(μg/cm
³)
Bhutan 69.8 19 40
Canada 82.2 4 7
China 76.1 150 52
Viet Nam 76 299 32
New Zealand 81.6 18 6
Italy 82.5 206 18
India 68.3 441 89
Japan 83.7 351 13
Australia 82.8 3 9
United States
of America 79.3 35 8
Egypt 70.9 93 88
Greece 81 83 17
Russian
Federation 70.5 9 17
Sri Lanka 74.9 333 25
Switzerland 83.4 210 11

High Population Density
Monaco 89.4 25,314 9
Macau 83.7 20,070 70
Singapore 83.1 7,989 21
Gibraltar 79.6 3,374 25
Bahrain 76.9 1,805 73
Maldives 78.6 1,516 9
Malta 81.7 1,355 15
Bermuda 81.4 1,274 12
Bangladesh 71.8 1,200 67
Lebanon 74.9 639 31

The best central tendency to observe here would be the mean because there are no
outliers. The values are relatively close together however there exists variance that
cannot be ignored. The data is skewed to the left mean < median. Life expectancy at birth for both males and females - Random 15 Mean: 77.53333333 Median: 79.3 Mode: N/A St Dev: 5.342741701 Range: 15.4 Q1: 72.9 Q3: 82.35 IQR: 9.45 Commented [3]: good statistic, but it should go with a frequency histogram not a bar graph Mean again would be the best central tendency to analyze because even if there are extreme values those values are not outliers. The data is a right skew. median < mean. The standard deviation is extremely large so the values are very far apart from the mean. Population Density - Random 15 Mean: 150.2666667 Median: 93 Mode: N/A St Dev: 142.7961095 Range: 438 Q1: 18.5 Q3: 254.5 IQR: 236 Commented [4]: what does this mean in regards to your variable and thesis? The median would be the best way to find the central value because outliers present in the data make it so the mean is influenced a lot by those values. The standard deviation is very large so the values are very far from the mean. The graph is skewed right because the mean > median.
PM 2.5 Air Pollution – Random 15
Mean: 28.8
Median: 17
Mode: 17
St Dev: 26.58119636
Range: 83
Q1: 10
Q3: 36
IQR: 26

The mean and the median are almost even so either one would work when finding the
central value. The standard deviation is very low so the values are very similar to each
other. The data almost follows a perfect normal curve, there is a slight skew to the left
but it is barely noticeable.

Life expectancy at birth for both
males and females – High
Density
Mean: 80.11
Median: 80.5
Mode: N/A
St Dev: 4.704986716
Range: 17.6
Q1: 77.325
Q3: 82.75
IQR: 5.425

The median would best indicate the central value because this graph has outliers that
will greatly affect the mean. The standard deviation is extremely large so the data is
very spread out as is the IQR which indicates the same thing as the standard deviation.
This data set is heavily skewed right because the mean is far greater than the median.
Population Density – High Density
Mean: 6,454
Median: 1,661
Mode: N/A
St Dev: 8445.032069
Range: 24,675
Q1: 1294.25
Q3: 6835.25
IQR: 5541
Commented [5]: It would have been great to see this
as a box plot

Median would be the best central tendency to look at because there are a few outliers
that would affect the mean. Standard deviation is quite large so the values tend to stray
from the mean. The data set has a right skew because the mean > median.

PM 2.5 Air Pollution – High
Density
Mean: 33.2
Median: 23
Mode: 9
St Dev: 25.00719896
Range: 64
Q1: 12.75
Q3: 58
IQR: 45.25

The data seems to show no real trends between population density and life expectancy.
In some cases such as India which has quite a low life expectancy compared to other
countries in the group and it also has a considerably higher population density than
other countries in that same group(Graph-1, Graph-2). However it is also important to
observe that air pollution in India is greater than the other countries in that group
(Graph-3), in fact life expectancy correlates a lot better with air pollution than it does
with population density. Examples of this would be Macau, Bahrain, and Bangladesh.
These countries have a considerable drop in their life expectancy and have the highest
air pollution in the High Density group. In that same group life expectancy(Graph-4) is
skewed slightly left meanwhile population density(Graph-5) is heavily skewed right
which may suggest that as population density decreases, life expectancy increases.
However the skew left (Graph-4) is barely even a skew meaning that this might have
been the work of an extraneous variable. One final thing to keep in mind is that the
central tendencies for the random 15 countries group is lower than the central
tendencies of the High Density group.

2-variable analysis

The following scatterplots are for 15 randomly selected countries for the year 2015

The r values for the graphs in order from top to bottom are : -0.14, -0.75, 0.38, 0.23.
There seems to be a very weak negative correlation betweenpopulation density and
life expectancy, an r value of -0.14 proves the correlation exists however weak it may
be. However air pollution also correlates negatively with life expectancy, and the
correlation is far stronger than that of population density. One may assume that the
negative correlation observed in the first graph is present because a higher population
density will lead to higher air pollution levels. This again may be true as there is a
positive correlation between population density and air pollution. There are two outliers
present on the graph and once removed the already weak correlation of 0.38 becomes
even weaker, 0.23. The next set of data will clarify the trends and relations. For now it
seems as if a higher population density will lead to higher levels of air pollution which
will lead to lower life expectancies. This also makes logical sense because high levels
of air pollution are linked to an increased rate of illness.

The following scatterplots are for the top 10 highest population density countries for the
year 2015

The r values for the graphs in order from top to bottom are : 0.78, -0.46, 0.01
The same trends seen previously do not reappear on the population density vs life
expectancy graph, in fact the trend is completely opposite from the previous trend.
Instead of being a weak negative correlation the trend is a strong positive correlation.
Air pollution and population do not correlate at all which is not very different from the
previous trend of an extremely weak positive correlation. The only big consistency is
that life expectancy does in fact negatively correlate with the level of air pollution
however the strength is not as intense.

The data here leads to the conclusion that population density correlating with life
expectancy was a coincidence because of the two drastically different correlations, one
strong positive and one weak negative. The theory that an increase in population
density also results in an increase in the level of air pollution which results in a decrease
in life expectancy is also busted because of the fact that population density and life
expectancy seem not to correlate at all. This does not mean that air pollution is not
linked with life expectancy, the data shows that an increase in the level of air pollution in
a country will in fact decrease the life expectancy of that country. My thesis, that
controlling a nation’s population should be a top priority because that impacts how long
that nation’s people live and how healthy they are, seems to be incorrect. Instead
controlling a nation’s level of air pollution should be a nation’s top priority as this in fact
does affect the life expectancy of its people.

References

1. Ruoyu W., Zhixin F., Desheng X., Ye L.,Rong W. (2019) Exploring the links between population
density, lifestyle, and being overweight: secondary data analyses of middle -aged and older
Chinese adults. doi: 10.1186/s12955-019-1172-3
2. Tejvan P. (11 jul 2017) Population density. EconomicsHelp
3. Hanlon, M., Burstein, R., Masters, S.H. et al. (2012).Exploring the relationship between
population density and maternal health coverage. BMC Health Serv Res 12, 416.
https://doi.org/10.1186/1472-6963-12-416
4. Judith B. (2020) Life expectancy. Encyclopedia Britannica
5. OECD (2020), Life expectancy at birth (indicator). doi: 10.1787/27e0fc9d-e
6. (2018) Statistics Canada, Population Density
7. Christopher H. Correlation Between Population Density and Life Expectancy. Esri

https://dx.doi.org/10.1186%2Fs12955-019-1172-3
https://doi.org/10.1186/1472-6963-12-416
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©2007,RobertHarris&AndrewSpinks Duplicatefreelyfornon­profiteducationaluse.
Formoreresearchhelp,seewww.virtualsalt.comandwww.andyspinks.com
The C.A.R.S. Checklist for Evaluating Internet Sources
You should evaluate every web site you use for research or for
personal information. The CARS checklist for evaluating internet
sourcesis listedbelow.Askyourselfthefollowingquestionsabout
eachsiteandtrytouseonlythosethathavethebestevidenceof
credibility,accuracy,reasonableness,andsupport.
FormoredetailedinformationonusingtheCARSchecklist,see:
http://www.virtualsalt.com/evalu8it.htm
Credibility Goal:Asourcethatiscreatedbyapersonororganizationwhoknowsthesubjectandwhocaresaboutitsquality
• Isthereapublishingorsponsoringorganization?Istheorganizationanauthorityonthesubject?
• Istheauthorlisted? Istheauthoranauthorityonthesubject?Howdoyouknow?
• Aretherespellingerrors,grammarerrors,deadlinks,orotherproblemsthatindicatealackof
qualitycontrol?
Accuracy Goal:Asourcewithinformationthatiscurrent,complete,andcorrect
• Doestheinformationonthesiteagreewithothersources?
• Doesthesitecontradictitself?
• Whatisthedateofpublicationorcopyright?
• Howrecentlyhasthesitebeenupdated?
Reasonableness Goal:Asourcethatistruthfulandunbiased
• Doestheauthor,host,publisher,orsponsorhaveabias?
• Whatisthemotivationorpurposeforcreatingthesite?
(Tosellaproduct?Toadvanceaviewpointorbelief?Toeducate?)
Support Goal:Asourcewithverifiablesourcesofinformation
• Arethesourceslisted?Cantheybechecked?
• Isthereawaytocontacttheauthorororganization?
Where should you look to find this information?
Ideally,informationsuchastheauthor,hostorganization,andpublicationdatewillbeeasilylocatedat
eitherthetoporbottomofthepage.However,youmayneedtodigdeeper:
□ YoucanfindoutaboutthehostorganizationbylookingattheURL,especiallythedomainname
(i.e.,cnn.com,harvard.edu,cdc.gov).Therearenouniversalrulesforwhichdomainsaregood
orbad,butthedomainnamecanhelpyouidentifythehostorganization.
□ Theinformationyouneedmightevenbeonadifferentpage.Tryclickingon“About…”or
“ContactUs”tofindmoreinformation.Youcanalsojustenterthedomainnamewithout
anythingpastthefirstslash(i.e.,shorten“virtualsalt.com/evalu8it.htm”to“virtualsalt.com”)and
seewhatinformationyoufind.
Adapted with permission from: Harris, Robert. “Evaluating Internet Research Sources.” http://www.virtualsalt.com/evalu8it.htm
Tip:Savesomeworkbycreatingyourbibliographiccitationwhileyouevaluate.Manyoftheelements
youneedtociteawebpageinMLAStyle(author,publisher,date,etc.)arethesameonesyouneedto
evaluateitsquality.Ifmorethanafewofthesearemissing,thesiteisprobablynotagoodone!

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