Below is partial Excel output from a multiple regression, with some information missing: ANOVA             df SS MS F Significance F Regression   898   20.83   Residual   248       Total 54 1146         Determine the degrees of freedom for the regression and degrees of freedom for the error (you may need to round to the nearest integer): Degrees of Freedom for the Regression =  Degrees of Freedom for the Error =

Describe the difference between the visually fitted and the calculated regression lines you placed on the scatterplot.  Describe any discrepancy in the scatterplot of the raw data, and how it might explain the difference between your fitted lines.  Does the difference between the visual fit and the regression fit reveal a flaw or a strength in the regression prediction, and why?

The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill = 15.9 + 4.45*Size Coefficients Estimate Std. Error (Intercept) 15.9 0.3 Size 4.45 0.57 We are 98% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size.

See the attached image for the introduction.  Question: Fill in a blank ANOVA table.

The station manager of a local television station is interested in predicting the amount of television (in hours) that a person in the viewing area will watch. The explanatory variables are age (in years), education (highest level obtained, in years) and family size (number of people in household). The multiple regression output is shown below:   Summary measures         Multiple R           R-Square           Adj R-Square 0.6644         StErr of Estimate 0.5598                     ANOVA Table           Source df SS MS F p-value Explained 3 13.9682 4.6561   0.0000 Unexplained 18  5.6413 0.3134     Regression coefficients             Coefficient Std Err t-value p-value     Constant 1.683 1.1696  1.4389 0.1674     Age -0.0498 0.0199 -2.5018 0.0222     Education  0.2135 0.0503…

only HANDWRITTEN answer needed ( NOT TYPED)

please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly

Hello. How do you calculate this question on a TI-83? Thanks.

Which one of the following numbers for the standard error of estimate would indicate the best regression fit? 100 50 1000 10

What is the Fratio for these data?