Spss Regression
Simple Linear Regression in SPSS
1.
STAT 314
Ten Corvettes between 1 and 6 years old were randomly selected from last year’s sales records in Virginia Beach, Virginia. The following data were obtained, where x denotes age, in years, and y denotes sales price, in hundreds of dollars. x y a. b. c. d. e. f. g. h. i. j. k. l. m. 6 125 6 115 6 130 4 160 2 219 5 150 4 190 5 163 1 260 2 260
Graph the data in a scatterplot to determine if there is a possible linear relationship. Compute and interpret the linear correlation coefficient, r. Determine the regression equation for the data. Graph the regression equation and the data points. Identify outliers and potential influential observations. Compute and interpret the coefficient of determination, r2. Obtain the residuals and create a residual plot. Decide whether it is reasonable to consider that the assumptions for regression analysis are met by the variables in questions. At the 5% significance level, do the data provide sufficient evidence to conclude that the slope of the population regression line is not 0 and, hence, that age is useful as a predictor of sales price for Corvettes? Obtain and interpret a 95% confidence interval for the slope, β, of the population regression line that relates age to sales price for Corvettes. Obtain a point estimate for the mean sales price of all 4-year-old Corvettes. Determine a 95% confidence interval for the mean sales price of all 4-year-old Corvettes. Find the predicted sales price of Jack Smith’s 4-year-old Corvette. Determine a 95% prediction interval for the sales price of Jack Smith’s 4-year-old Corvette.
Note that the following steps are not required for all analyses…only perform the necessary steps to complete your problem. Use the above steps as a guide to the correct SPSS steps.
1.
Enter the age values into one variable and the corresponding sales price values into another variable (see figure, below).
2.
Select Graphs Legacy Dialogs
1.
STAT 314
Ten Corvettes between 1 and 6 years old were randomly selected from last year’s sales records in Virginia Beach, Virginia. The following data were obtained, where x denotes age, in years, and y denotes sales price, in hundreds of dollars. x y a. b. c. d. e. f. g. h. i. j. k. l. m. 6 125 6 115 6 130 4 160 2 219 5 150 4 190 5 163 1 260 2 260
Graph the data in a scatterplot to determine if there is a possible linear relationship. Compute and interpret the linear correlation coefficient, r. Determine the regression equation for the data. Graph the regression equation and the data points. Identify outliers and potential influential observations. Compute and interpret the coefficient of determination, r2. Obtain the residuals and create a residual plot. Decide whether it is reasonable to consider that the assumptions for regression analysis are met by the variables in questions. At the 5% significance level, do the data provide sufficient evidence to conclude that the slope of the population regression line is not 0 and, hence, that age is useful as a predictor of sales price for Corvettes? Obtain and interpret a 95% confidence interval for the slope, β, of the population regression line that relates age to sales price for Corvettes. Obtain a point estimate for the mean sales price of all 4-year-old Corvettes. Determine a 95% confidence interval for the mean sales price of all 4-year-old Corvettes. Find the predicted sales price of Jack Smith’s 4-year-old Corvette. Determine a 95% prediction interval for the sales price of Jack Smith’s 4-year-old Corvette.
Note that the following steps are not required for all analyses…only perform the necessary steps to complete your problem. Use the above steps as a guide to the correct SPSS steps.
1.
Enter the age values into one variable and the corresponding sales price values into another variable (see figure, below).
2.
Select Graphs Legacy Dialogs