# Question: Based on data on 2 679 high school basketball players the

Based on data on 2,679 high school basketball players, the following model was fitted:

y = b0 + b1x1 + b2x2 + g + b9x9 + e

where

y = minutes played in season

x1 = field@goal percentage

x2 = free@throw percentage

x3 = rebounds per minute

x4 = points per minute

x5 = fouls per minute

x6 = steals per minute

x7 = blocked shots per minute

x8 = turnovers per minute

x9 = assists per minute

The least squares parameter estimates (with standard

errors in parentheses) were as follows:

The coefficient of determination was as follows:

R2 = 0.5239

a. Find and interpret a 90% confidence interval for β6.

b. Find and interpret a 99% confidence interval for β7.

c. Test, against the alternative that it is negative, the null hypothesis that β8 is 0. Interpret your result.

d. Test, against the alternative that it is positive, the null hypothesis that β9 is 0. Interpret your result.

e. Interpret the coefficient of determination.

f. Find and interpret the coefficient of multiple correlation.

y = b0 + b1x1 + b2x2 + g + b9x9 + e

where

y = minutes played in season

x1 = field@goal percentage

x2 = free@throw percentage

x3 = rebounds per minute

x4 = points per minute

x5 = fouls per minute

x6 = steals per minute

x7 = blocked shots per minute

x8 = turnovers per minute

x9 = assists per minute

The least squares parameter estimates (with standard

errors in parentheses) were as follows:

The coefficient of determination was as follows:

R2 = 0.5239

a. Find and interpret a 90% confidence interval for β6.

b. Find and interpret a 99% confidence interval for β7.

c. Test, against the alternative that it is negative, the null hypothesis that β8 is 0. Interpret your result.

d. Test, against the alternative that it is positive, the null hypothesis that β9 is 0. Interpret your result.

e. Interpret the coefficient of determination.

f. Find and interpret the coefficient of multiple correlation.

## Answer to relevant Questions

Based on data from 63 counties, the following model was estimated by least squares: where y` = growth rate in real gross domestic product x1 = real income per capita x2 = average tax rate, as a proportion of gross national ...Consider the following models estimated using regression analysis applied to time-series data. What is the long-term effect of a 1-unit increase in x in period t? a. yt = 10 + 2xt + 0.34yt-1 b. yt = 10 + 2.5xt + 0.24yt-1 c. ...The data file Thailand Consumption shows 29 annual observations on private consumption (Y) and disposable income (X) in Thailand. Fit the regression model log yt = β0 + β1log x1t + γlog yt-1 + εt and write a report on ...In Chapter 11, the regression of retail sales per household on disposable income per household was estimated by least squares. The data are given in Table 11.1, and Table 11.2 shows the residuals and the predicted values of ...In a regression based on 30 annual observations, U.S. farm income was related to four independent variables-grain exports, federal government subsidies, population, and a dummy variable for bad weather years. The model was ...Post your question