14. More Computation on Predicting GPA Computer output for a regression model to predict grade point average in college based on score on the Verbal SAT exam is shown below.   The regression equation is   GPA=2.03+0.00189⁢  VerbalSAT Analysis of Variance Source DF SS MS F P Regression 1 6.8029 6.8029 48.84 0.000 Residual Error 343 47.7760 0.1393     Total 344 54.5788         Use this output, together with any helpful summary statistics from the table below, to calculate the following regression quantities.   . Sample Size Mean Std.Dev. GPA 345 3.1579 0.3983 VerbalSAT 345 594.0 74.29 Table 1 Grade point average and Verbal SAT score   (a) The standard deviation of the error term, sεRound your answer to three decimal places. (b) The standard error of the slope, S⁢ERound your answer to five decimal places.

A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the​employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees.       ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12     Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854   1. Predict the sales next month for an employee with 2.5 years of experience.   The predicted sales is 8.6 cars.   2. Compute the coefficient of determination and interpret its meaning.   The coefficient of determination is 0.234.   3. Do the sample data provide evidence that the model is useful for predicting average monthly sales for employees based on their sales experience using α=0.05​?   The test statistic is ​(Type an integer or decimal rounded to two decimal places as​…

mean of x = 2.882, standard deviation = 1.634  Mean of y = 2.588, standard deviation = 0.246  The correlation coefficient is 0.159 slope coefficient for regression line = .0239 y intercept = 2.52 write the regression model

Data on 17 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. ski = 86 2.4 treadmill Coefficients Estimate (Intercept) Treadmill 86 -2.4 Std. Error What is the test statistic? -2.791 0.26 0.86 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? Round your answers to three decimal places. Using your answer from the previous question, find the p-value. Part 2 of 3