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Mat 152 Project 5 Name ______________________

Global Health and Global Wealth

This project gives you the opportunity to use statistics to investigate the relationship between Global Health and Global Wealth. Although, anecdotally, wealthier countries are expected to have healthier populations, there is significant variability surrounding that relationship. Attached are some data for you to use to investigate the relationship for yourself. Within the data you will find 22 randomly selected countries from around the world. For each country you will find a variety of economic and healthcare indicators (see the data dictionary for a definition of each variable). Truthfully, there is not one single factor that explains overall health in a selected location. In fact, we would expect many different variables to contribute to the general health of a population. However, for this assignment, you are to determine the indicator that you believe best predicts general health, as measured by life expectancy. You must use the data given to you.

1. Investigation Results Using the given data, investigate the possible relationships between Life Expectancy as the response variable (y) and each of the three explanatory variables (x). For each x variable, you will complete the following steps. (Note: you will do parts a d three times!) (60 points)

a. Construct a scatter diagram displaying the relationship between x and y.

You can use graph paper and draw the diagram by hand, use your calculator and take a picture of the screen, or use the online site: https://www.desmos.com/, which will allow you to print or save your diagram. Whatever tool you use, be sure to choose an appropriate scale for the axes so that the relationship between the variables is easy to see.

b. Calculate and state the sample correlation coefficient r.

c. Describe the type of correlation, if any, and interpret the correlation in the context of the data.

d. Determine if the correlation is significant. Use = 0.05 and show all steps of this process.

2. Inferences – Write at least one paragraph for each of the following questions. In the paragraph, you should explain as though you are talking to someone that is not in a statistics class. In other words, give details. A paragraph is 3-5 well-developed sentences. (40 points)

a. Now that you have investigated the relationships between y and each of the three different x variables, which explanatory variable (x) is the best predictor for Life Expectancy (y)? To defend your choice, discuss your investigation results for each

(x, y) pairing from part 1, including correlation coefficients and their significance.

b. Calculate the regression equation for the relationship you determined to be the best in part a. What is the rate of change of your equation? Using this rate of change, describe the behavior of Life Expectancy (y) as a result of changes in the explanatory variable (x) that you chose.

c. Discuss the overall fitness of your regression line to the data set, by graphing the line on your scatter plot and by considering the correlation coefficient. Would you say that the explanatory variable you chose is reasonably predictive of Life Expectancy? Why or why not?

d.

You may have noticed that the United States was not included in the data set you were given. Below are the relevant statistics for the United States.

United States

Life Expectancy (y)

78.5

GDP per capita (x)

59531.7

% Spending versus GDP (x)

17.17

Inverted Corruptions Score (x)

24

Using the regression model that you calculated in part b, plug in the appropriate x-value for the United States to make a prediction of Life Expectancy. Also, calculate the residual value (difference between your prediction and the actual value). Does the United States seem to fit in your model or is it an outlier? Give the reason(s) for your answer.

e. Summarize your findings. If you were part of a United Nations team tasked with researching and making recommendations to member countries to improve life expectancy for their citizens, what would be your next steps?

1

2 MAT 152 Project 5 Data

Global Health Data Set

Country

Life Expectancy (y)

GDP per capita (x)

% Spending vs. GDP (x)

Corruptions Score (x)

Australia

82.7

53825

9.1

21

Austria

81.7

50023

10.3

24

Belgium

81.6

45176

10

23

Canada

81.9

46213

10.4

17

Chile

80

23667

8.1

30

Czech Republic

79

23214

7.1

44

France

82.7

41761

11.5

30

Germany

80.9

46564

11.3

19

Hungary

76.1

28328

7.2

49

Iceland

82.9

67037

8.5

21

Israel

82.8

42823

7.4

39

Japan

84.2

40847

10.7

25

Netherlands

81.8

52368

10.1

16

Norway

82.8

77975

10.4

12

Poland

77.6

29291

6.7

37

Portugal

81.3

23031

9

36

Slovak Republic

77.3

19548

7

49

Slovenia

81.4

26170

8

40

Sweden

82.6

51242

10.9

11

Switzerland

83.2

83717

12.3

14

Turkey

77.4

28242

4.2

58

United Kingdom

82.3

41030

9.6

19

Sources: Corruptions Perceptions Index 2015: http://www.transparency.org/cpi2015;

OECD Health Statistics: https://www.oecd-ilibrary.org/social-issues-migration-health/data/oecd-health-statistics_health-data-en;

Life expectancy at birth, total years: https://data.worldbank.org/indicator/SP.DYN.LE00.IN?end=2015&start=1960&view=chart

Data Dictionary:

Life Expectancy: the average time (in years) a constituent of each location is expected to live based upon the year of their birth.

GDP per capita: a measure of economic output found by dividing the location’s Gross Domestic Product by its total population (measured in US Dollars).

Health spending per capita: a measure of total resources spent on health care in a given location calculated by dividing total national health expenditures by the population (measured in US Dollars).

% Spending versus GDP: the percentage of a location’s Gross Domestic Product spent on health care.

Corruptions Score: a measure of the perceived corruption rated by global corruption experts (0 indicates no corruption, 100 would be most corrupt).

August 19, 2022

August 19, 2022

August 19, 2022

August 19, 2022