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.
H0: b1 = b2 = ... = bp = 0
(No linear relationships)
H1: At least one bi != 0 (beta i not equal ...
This solution uses hypothesis testing at an alpha of .10 to prove whether or not an independent variable has a statistically significant impact on the dependent variable.