The accompanying table provides data for the sex, age, and weight of bears. For sex, let 0 represent female and let 1 represent male. Letting the response (y) variable represent weight, use the dummy variable of sex and the variable of age and to find the multiple regression equation. Use the equation to find the predicted weight of a bear with the characteristics given below. Does sex appear to have much of an effect on the weight of a bear? X a. Female bear that is 21 months of age b. Male bear that is 21 months of age Click the icon to view the bear measurement data. Find the multiple regression equation with weight as the response variable and the dumn Weight=+ Sex Sex + + ( Age (Round to one decimal place as needed.) Bear Measurement Data Sex (1=M) 1 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 0 Age (Months) 18 55 80 115 103 103 55 48 54 55 65 8 47 29 22 29 46 12 Weight 72 336 415 346 165 221 260 351 214 147 328 31 144 176 107 157 196 16 D -

Consumers are often interested in the fuel efficiency of the vehicles they choose to buy, so much so that they will research the various models they consider buying. Fuel efficiency can depend on a variety of variables. In this analysis, there are 73 automobiles that are popular with consumers. A regression analysis has been performed; the dependent variable is CityMPG (EPA miles per gallon in city driving), and independent variables are Length (vehicle length in inches), Width (vehicle width in inches), Weight (vehicle weight in pounds), and ManTran (1 if manual shift transmission, 0 otherwise). The level of significance is 0.05. Use the following MegaStat output to answer questions about this regression analysis. a. State the regression equation. b. How would CityMPG be affected if the width of a vehicle increased by an inch? c. Estimate the CityMPG for a vehicle with a length of 190 inches, a width of 75 inches, a weight of 4100 pounds, and a manual. Round your answer to the nearest…

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.…

For a two-variable linear regression, if the sample correlation between the depen- dent and independent variables is –0.7, then the independent variable explains 49% of the variation in the dependent variable. Is this statement true? Explain your answer.

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A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?

The adjusted R-squared accounts for the amount of variance explained while also adjusting for the number of independent variables in the linear regression equation.   True or False

Give an example of how a nonrepresentative sample can severely affect a regression analysis.

Which statement is not correct for multiple regression model? When we interpret this categorical variable, we would say the change we observe when one switches from the reference category to the specific category. The reference category will not be displayed in our model results. If we include a categorical variable with more than two values (e.g., religion) as an independent variable, we want to include a dummy variable for all categories except for two. We can include more than two categorical variables in the model. The value of the independent variable not included in the model is called "reference category."

Briefly describe what is meant by the problem of errors in measurement of the predictor variables and describe its effect on a regression analysis.