2. For this problem you will need to use the data set titled: Chapter5Data.xIsx. This data explores overall life expectancy (Life expectancy) for 38 countries as well as Male and Female Life Expectancyes. People/TV gives an insight as to how many people per 1 TV for each country while People/physician gives an insight into how many people per 1 physician (or professional care provider). For the purpose of this project use Life expectancy as your response variable. You will have two explanatory variables: People/TV and People/physician. I. People/TV and Life Expectancy a. Construct a scatterplot for Life Expectancy as a response to People/TV, where People/TV is the explanatory variable. What does the scatter plot suggest about the linear correlation between Life Expectancy and People/TV? b. Add the line to your scatter plot using R and the function abline (). See page 4 of project or R user guide in Blackboard as a reference C. What is the regression line [ y= bx+a ] for People/TV? Use contextual variables. d. What is the slope and explain what it means in context. Does the y-intercept make sense? e. What is the correlation coefficient for People/TV and Life Expectancy? Is there a strong, moderate, or weak linear relationship between Life Expectancy and People/TV? Explain why the relationship is moderate, strong, or weak. Give possible explanations to support your results in the context of this problem. f . Calculate and interpret (in the context of the data) the value of the coefficient of determination. II. People/physician and Life Expectancy a. Construct a scatterplot for Life Expectancy as a response to People/physician, where People/physician is the explanatory variable. What does the scatter plot suggest about the linear correlation between Life Expectancy and People/physician? b. Add the line to your scatter plot using R and the function abline(). What is the regression line [ y=bx+a ]for People/physician? Use contextual variables. d. What is the slope and explain what it means in context. Does the y-intercept make sense? e. What is the correlation coefficient for People/physician and Life Expectancy? Is there a strong, moderate, or weak linear relationship between Life Expectancy and People/physician? Explain why the relationship is moderate, strong, or weak. Give possible explanations to support your results in the context of this