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# Identifying degrees of freedom and sum of squares

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A company that manufactures computer chips wants to use a multiple regression model to study the effect that the variables

x1 = daily production volume
x2 = daily amount of time involved in production

have on

y = total daily production cost

If a regression model is estimated using 188 observations on each of these variables, what are the degrees of freedom for the regression, the regression sum of squares, the error sum of squares, and the total sum of squares?

##### Solution Summary

The solution solves for the sum of squares and identifies the degrees of freedom for the outlined regression.

##### Solution Preview

Note that for p explanatory variables, the model degrees of freedom (DFM) are equal to p, the error degrees of ...

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###### Education
• BSc , Wuhan Univ. China
• MA, Shandong Univ.
###### Recent Feedback
• "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
• "excellent work"
• "Thank you so much for all of your help!!! I will be posting another assignment. Please let me know (once posted), if the credits I'm offering is enough or you ! Thanks again!"
• "Thank you"
• "Thank you very much for your valuable time and assistance!"

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