This data file consists of indicators of economic development recorded for 94 countries.

LIFE measures average life expectancy at birth in years.

FOOD measures per capita daily calories consumed.

ENERGY measures per capita energy consumption in kilograms of oil

URBAN measures the percent of the population that lives in an urban area.

(a) Construct a scatterplot using the variables FOOD and ENERGY. Based on the scatterplot, would you describe the relationship between these two variables as positive, negative or neither? Would you describe the relationship between these two variables as linear, nonlinear, or nonexistent?

(b) Calculate the correlation coefficient (Pearson) for these two variables. Interpret the sign and the magnitude.

(c) Construct and conduct an appropriate hypothesis test to determine whether these two variables have a linear relationship within the population. Use a 1% level of significance

(d) Estimate a simple linear regression model where LIFE is the dependent variable and URBAN is the independent variable. Write down the estimated regression equation.

(e) Suppose a certain country experienced a 5 percentage point decline in its urbanization rate. Using your results, what is the expected impact on life expectancy?

(f) Construct and conduct an appropriate hypothesis test to determine whether the slope coefficient associated with URBAN is "statistically significant." Use a 5% level of significance.

(g) Find the observation in the data for USA. Calculate the residual using its characteristics.