MDM4U FINAL PROJECT
TWO-VARIABLE CO-RELATIONAL STUDY
Name: Caroline Wojnas Date: June 5, 2013
My Co-relational Study
Main variable:
Per capita total expenditure on health (PPP int. $)
My Co-relational Study
Variable 1:
Life Expectancy at Birth for Both Sexes (in years)
Variable 2:
Maternal Mortality Ratio (per 100,000 live births)
Interagency Estimates
Why Did I Choose This Topic?
3 REASONS:

My career aspirations are tied to Healthcare
This study allowed me to estimate the effectiveness of healthcare
systems worldwide


I’m passionate about world issues
History of my Topic and Variables
+ FUN FACTS!
Brief History
Healthcare has always been an essential determinant in promoting the well-being
of humans worldwide.


Primary
Secondary
Tertiary Industries
Advancements in science and technology has developed these industries, creating
more effective healthcare systems.

Brief History
Healthcare financing methods:
 general taxation to the municipality
 social health insurance
 private health insurance
 donations to health charities
 out-of-pocket payments

Brief History

The more an individual pays for health services...
the more benefits
 health is improved
 less risks are present

Brief History

These improvements over time have lead to healthier people:
lengthening life expectancies
 decreasing maternal mortality

Brief History
The success of healthcare systems varies around the
world due to:

social conditions
 economic conditions
 health policies in effect

FUN FACT:
Japan has the
highest life
expectancy
82.7 years
Economy & Health
Sierra Leone has the
lowest life expectancy
46.53 years
FUN FACTS:
Ranked as
Canada’s Stats
30th best healthcare system in the world
Per capita total expenditure on healthcare:
Life expectancy of
4520.0 (PPP int. $)
82 years (for both sexes)
Maternal mortality ratio is
12 (per 100,000 live births)
FUN FACT:
World Life Expectancy
70 years
was the average life expectancy at birth
of the global population in 2011!
Hypotheses
HYPOTHESIS #1


Per capita total expenditure on health (PPP int. $) vs. life
expectancy at birth for both sexes:
I expect to see a strong, positive linear
correlation.
HYPOTHESIS #2


Per capita total expenditure on health (PPP int. $) vs. maternal
mortality ratio (per 100,000 live births):
I expect to see a strong, negative linear
correlation.
1 Variable Analysis
Variable 1 (Main Variable):
Per capita total expenditure on health (PPP int. $)
Histogram: Per Capita Total Expenditure on Health
(PPP int. $) by Country in 2011
30
Mean: 1977.658
25
Median: 1239.5
Minimum: 32.1
Frequency
20
Maximum: 8607.9
Range: 8575.8
15
Standard Deviation:
2034.656368
10
Mode: N/A
5
0
50
1050
2050
3050
4050
5050
6050
Per Capita Total Expenditure on Health (PPP int. $)
More
Variable 2 (for comparison):
Life Expectancy at Birth for Both Sexes (years)
Histogram: Life Expectancy at Birth for Both Sexes (years) by
Country in 2011
25
20
Mean: 72.24
Frequency
Median: 76.5
Minimum: 49
15
Maximum: 83
Range: 34
10
Standard
Deviation:
10.64177367
5
Mode: 81
0
49
53.9
58.8
63.7
68.6
73.5
78.4
Life Expectancy at Birth for Both Sexes (years)
More
Variable 3 (for comparison):
Maternal Mortality Ratio
(per 100,000 live births) Interagency Estimates
Histogram: Maternal Mortality Ratio (per 100,000 live births)
Interagency Estimates by Country in 2010
35
30
15
Mean: 135.96
Median: 22.5
Minimum: 3
Maximum: 630
Range: 627
Standard Deviation:
191.2710397
10
Mode: 8
Frequency
25
20
5
0
3
93
183
273
363
453
543
Maternal Mortality Ratio (per 100,000 live births)
More
2 Variable Analysis
independent variable: per capita total expenditure on health (PPP int. $)
For this independent variable, a scatter plot versus each of the other two variables
(dependent variables) follows this slide.
SCATTER PLOT #1
Per Capita Total Expenditure on Health vs Life Expectancy at Birth for Both Sexes of Selected Countries
Life Expectancy for Both Sexes (years)
120
y = 0.0038x + 64.806
R² = 0.5166
R= 0.7187
100
80
60
40
20
0
0.0
1000.0
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3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
10000.0
SCATTER PLOT #2
Per Capita Total Expenditure on Health vs Maternal Mortality Ratio (per 100,000 live births)
Interagency Estimates of Selected Countries
Maternal Mortality Ratio (per 100,000 live births)
700
y = -0.0577x + 249.99
R² = 0.3762
R= -0.6134
600
500
400
300
200
100
0
0.0
-100
-200
-300
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
10000.0
Conclusions
HYPOTHESIS #1

Examining the first correlation I conducted in this
study, which was between per capita total
expenditure on health (PPP int. $) and life
expectancy (in years), it appears that my analysis
verifies my hypothesis.
HYPOTHESIS #1

It is, in fact, a strong, positive linear correlation.

This relation is cause and effect.

A linear model suits this relationship best.
HYPOTHESIS #2


The next correlation I conducted in this study was
per capita total expenditure on health (PPP int. $)
versus maternal mortality (per 100,000 live births).
My hypothesis needs to be modified.
HYPOTHESIS #2



This correlation is actually moderate and negative,
as r = -0.6134
This relation is cause and effect.
An exponential model would suit this relationship
best.
Sources of Error
EXAMINING SAMPLING TECHNIQUE


I sampled 50/196 countries in the world.
Therefore, this sampling method can result in
sample bias, not reflecting the exact characteristics
of the population (all countries in the world).
EXAMINING MEASUREMENT METHODS




How variables are measured contributes to
this bias:
(PPP int. $)
(years) for both sexes
(per 100,000 live births) interagency estimates
MY DATA SOURCE’S CREDIBILITY


I used the World Health Organization
website www.who.int.
Renowned for providing health-related statistics!
ANY OUTLIERS?


SCATTER PLOT #1: one
SCATTER PLOT #2:
unsuitable model
WAYS TO REMOVE SOURCES OF ERROR




Use the same data source
Use the same 50 countries for each variable
Obtain data for each variable from the same year
Use data from all countries
Further Analysis
OTHER VARIABLES I WOULD LIKE TO
COMPARE:
1)
Disease ratios in countries
Conditions such as:
Tuberculosis, HIV/AIDS, Type 1 Diabetes
2)
Nutrition-related stats such as:
Child Malnutrition ratios per country
3)
Immunization coverage percentages by country
for various illnesses such as:
Hepatitis B (HepB3), Polio (Pol3), and Measles (MCV).
Thanks for your Attention!
BIBLIOGRAPHY OF DATA
SOURCES
World Health Organization. N.p., May 2012. Web. 2 June 2013.
<http://www.who.int/mediacentre/factsheets/fs348/en/index.html>.
University of British Columbia Sauder School of Business Pacific Exchange Rate Service . N.p., 2011.
Web. 2
June 2013. <http://fx.sauder.ubc.ca/PPP.html>.
Health Financing: Health expenditure per capita by country. N.p., 2011. Web. 2 June 2013.
<http://apps.who.int/gho/data/node.main.78?lang=en>.
Life Expectancy by Country. N.p., 2011. Web. 2 June 2013.
<http://apps.who.int/gho/data/node.main.3?lang=en>.
Cause-specific mortality and morbidity: Maternal mortality ratio by country. N.p., 2010. Web. 2 June
2013.
<http://apps.who.int/gho/data/node.main.15?lang=en>.