When comparing different multiple linear regression models to determine which is better for predicting a response variable, Y, why should we use the adjusted-R2 values instead of the plain R2 values?

Discuss the advantages of using data collected from a designed experiment in performing a regression analysis.

A standardized exam's scores are normally distributed. In a recent year, the mean test score was 1471 and the standard deviation was 318. The test scores of four students selected at random are 1910, 1210, 2210, and 1370. Find the z-scores that correspond to each value and determine whether any of the values are unusual. The z-score for 1910 is (Round to two decimal places as needed.)

Suppose there is a significant correlation between two variables. Describe a case under which it might be inappropriate to use the linear regression equation for prediction.

Help please!

If the regression line is not a "good fit" what would be better to use to approximate a response variable?

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?

The Conde Nast Traveler Gold List  for 2012 provided rating for the top 20 small cruise ships. The data from annual Readers’ Choice Survey are the overall scores(Y) each ship received based on several criteria, including Itineraries/Schedule (X1), Shore Excursions(X2), and   Food/Dinning(X3). The estimated regression equation to predict the overall scores is Y= 35.6184+0.1105 X1+0.2445 X2+0.2474 X3. Part of the regression results is shown below.                                                            Coefficients       Standard Error Intercept                                           35.6184            13.2308 Itineraries/Schedule(X1)                0.1105               0.1297 Shore Excursions(X2)                      0.2445               0.0434 Food/Dinning(X3)                            0.2474               0.0621     Use the T test to determine whether or not the coefficient of X1 is significant. Use Level of significance=.05? Be sure to state null and alternative hypotheses.…

The fish in my pond have mean length 12 inches and mean weight 5 pounds.  The correlation coefficient of length and weight is .8.  If the length of a particular randomly selected fish is reported to be 12 inches, then what should we predict for the weight of that fish using simple linear regression?