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# F test for Multiple Regression

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Please help, I am going through questions in my textbook and am having difficulties with this particular question that deals with partial f-tests. I have attached the question, and even though it may not be needed the data set where the model is drawn from.

12.18

In exercise 12.18 we give MINITAB, Megastat and excel outputs of regression analyses of the Fresh data set. Above the output we give the regression model and the number of observations, n, used to perform the regression analysis under consideration. Using the appropriate model, sample size n, and output:

1. Calculate the F(model) statistic by using the explained variation, the unexplained variation, and other relevant quantities. Find F (model) on the output to check your answer.
2. Use the F (model) statistic and appropriate rejection point to test the significance of the linear regression model under consideration by setting  = 0.05
3. Use the F (model) statistic and appropriate rejection point to test the significance of the linear regression model under consideration by setting  = 0.01
4. Find the p-value related to F(model) on the output. Using the p-value, test the significance of the linear regression model by setting  = 0.10, 0.05, 0.01, and 0.001. What do you conclude?

See attachment.

https://brainmass.com/statistics/linear-regression/f-test-for-multiple-regression-157687

#### Solution Summary

This solution conducts statistical analysis using the F-model on the output for various significance levels. It also explains how the p-value is related to the F model.

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## F test of a multiple regression model

A company that manufactures computer chips wants to use a multiple regression model to study the effect that 4 different variables have on y, the total daily production cost (in thousands of dollars). Let denote the coefficients of the 4 variables in this model. Using 19 observations on each of the variables, the software program used to find the estimated regression model reports that the total sum of squares (SST) is 661.86 and the regression sum of squares (SSR) is 159.36 . Using a significance level of .10, can you conclude that at least one of the independent variables in the model provides useful (i.e., statistically significant) information for predicting daily production costs?

Perform a one-tailed test. Then fill in the table below.

The null hypothesis
The alternative hypothesis
The type of test statistic
The value of the test statistic (round to two decimal places)
The critical value at .10 level of significance.

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