A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained.     ANOVA       df SS Regression 3 45.9634 Residual 11 2.6218 Total         Coefficients Standard Error Intercept 0.0136   x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920   Write out the estimated regression equation for the relationship between the variables.  Compute coefficient of determination. What can you say about the strength of this relationship?  Carry out a test to determine whether y is significantly related to the independent variables. Use a 5% level of significant  Carry out a test to see if x3 and y are significantly related. Use a 5% level of significanc

You believe that the price of Zoom Videoconferencing stock and the price of American Airlines stock will move in opposite directions. In order to test this relationship, we do a simple regression with the following variables:A - dependent variable : month end price of American Airlines stockZ - independent variable: month end price of Zoom Videoconferencing stock Data from April 2019 through December 2020 (21 observations) is availableBased on the data, we compute the following:Var (Z) = 20927.702Cov (A,Z) = -899.153E(A) = 20.790E(Z) = 187.530Std Error of Estimate = 6.088TSS = 1476.830 Consider the equation At = b0 + b1 Zt + εtBased on the numbers given above, complete the following table Variable Estimate Std error t-statistic Slope b1   .00941      Constant b0   2.2088      R-square   N/A N/A F statistic   N/A N/A   Are the coefficients (slope and/or constant) significant at the .05 level?

Do movies of different types have different rates of return on their budgets? Here's a regression of USGross (SM) on Budget for comedies and action movies with an indicator variable. Complete parts (a) through (d). Dependent variable is: USGross ($M) Coefficient SE(Coeff) - 6.78278 16.95 1.00523 Variable Constant Budget ($M) Comedy 24.0373 0.1613 11.73 t-ratio P-value -0.400 0.6907 6.23 <0.0001 2.05 0.0451 a) Write out the regression model. USGross = + ( Budget + (Comedy R-squared = 32.8% R-squared (adjusted) = 31.0% s = 47.51 55 degrees of freedom

A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained.   ANOVA                                   df                  SS Regression                3                   45.9634 Residual                  11                   2.6218 Total                                 Coefficients     Standard Error Intercept                0.0136 x1                          0.7992                   0.074 x2                          0.2280                   0.190 x3                         -0.5796                   0.920   Write out the estimated regression equation for the relationship between the variables.  Compute coefficient of determination. What can you say about the strength of this relationship?  Carry out a test to determine whether y is…

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 13.45 + 4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.

The following data shows memory scores collected from adults of different ages. Age (X) Memory Score (Y) 25 10 32           10 39 9 48 9 56 7 Use the data to find the regression equation for predicting memory scores from age. The regression equation is: Ŷ = 4.33X + 0.11 Ŷ = -0.11X + 4.33 Ŷ = -0.11X + 13.26 Ŷ = -0.09X + 5.4 Ŷ = -0.09X + 12.6   Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 28 For the calculations, leave two places after the decimal point and do not round:   Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 43 For the calculations, leave two places after the decimal point and do not round:   Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 50 For the calculations, leave two places after the decimal point and do not round:

An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:.   Summary measures       Multiple R 0.9689       R-Square 0.9387       Adj R-Square 0.9212       StErr of Estimate 72.218                 Regression coefficients         Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.

A simple linear regression model was created to examine the relationship between the age of patients in years (i.e. the independent variable) and the patients' scores on a questionnaire that measures quality of life (i.e. the dependent variable). There were n=20 patients in the study, and some of the results of the regression are presented below: Estimated slope = -0.64 Standard error of the slope = 0.52 Estimated intercept = 42.0 Standard error of the intercept = 13.6 Based on these results, what would be the expected quality of life score for a patient who is 30 years old?   Question 3 options:   61.2   22.8   1260.64   29.2