You are to conduct inferential procedures on the data set which represents a population of two hundred five cars produced last year by a car company. Column 1 represents their curb weight in lbs and column 2 represents the price of the car in dollars. A curb weight is the published weight of the vehicle as produced by the factory, with a full tank of fuel and all fluids filled.
1) What are the explanatory and response variables?
2) Determine the mean, standard deviation, and five-number summary for each variable.
3) What is the value of the linear correlation coefficient?
4) Based on the value of the linear correlation coefficient, is the correlation between CurbWeight and Price
strong or weak? Why?
5) Determine the equation of the linear regression line.
6) Use your regression equation to predict the Price of a car if the CurbWeight is 20,000 lbs.
7) Generate a scatterplot with a fitted regression line.
8) Interpret the meaning of the slope of the regression line in terms of CurbWeight and Price.
9) What is the y-intercept of the regression line and what does it mean CurbWeight and Price?
Is this realistic?
10) Use your regression equation to predict the Price if the CurbWeight is 1,000,000 lbs. Is this realistic?
11) What is the value of the coefficient of determination and what does it mean?