What is the significance of R and R2 and of a regression model?

What are the assumptions of a multiple linear regression model? What would you do to look for violations of the multiple linear regression model assumptions

A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y = 74.80 + 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with O rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…

What is basic regression? What is multiple regression? How do they both relate to Data Mining?

What do we mean when we say that a multiple regression model is a multiple linear regression model? What does “linear” mean here? Can we allow any “higher-order terms”? Explain.

Why was the variable “# of customer service representatives” dropped from the model?   Write the regression equation.   Sales of men’s clothing (predicted) =   Are the regression coefficients significantly different from 0?   If one mails 10,000 catalogs and has 15 phone lines open, what would the predicted sales of men’s clothing be?   How would you interpret the regression coefficient for number of catalogs mailed?   What is the final R2 of the model? How would you interpret this?   Which of the two independent variables is the most important predictor of the dependent variable? Why?

How does linear regression differ from analysis of variance?

You are analyzing a dataset with 932 datapoints. You decide to create a linear regression model with this dataset, using 18 predictor variables. Using this information, what is the degrees of freedom associated with the sum of squares total (total sum of squares) of your analysis?

What are the Standard Errors for Fixed Effects Regression?

what is the regression equation for all predictors? I