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- A website that rents movies online recorded the age and the number of movies rented during the past month for some of their customers. The data are shown below for a random sample of 25 of their customers.The regression line for the data, with number of movie rentals as the response variable, provides an intercept = 18.87, and slope = -0.228. The standard error of the slope SE(b1) = 0.0827. Margin of error ME for a 99% Confidence Interval for the slope of the Population regression line is: 0.1161 0.2322 0.4644 0.3483An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA=1.85+0.4743(High School GPA).Estimated College GPA=1.85+0.4743(High School GPA). GPAs College GPA High School GPA 3.843.84 2.562.56 3.573.57 3.903.90 2.072.07 3.143.14 4.004.00 3.223.22 3.873.87 2.882.88 2.212.21 2.082.08 Copy Data Step 1 of 3 : Compute the sum of squared errors (SSE) for the model. Round your answer to four decimal places.
- The dependent variable in the regression in our cost driver analysis is which of the following? Company sales Total overhead cost for the entire period of time Total overhead cost per monthAn example of a cubic regression model is Yi= 30 + B1X + 32x2 + 33x³ + ui Yi = 30 + B1X + 32x² + ui. Yi = 30 + ß1ln(X) + ui Yi= 30 + 31X + B2Y2 + ui.Consider the following regression model and corresponding output for a dataset with n = 104 observations: y=ß₁+ß2x2+ß³¸*¸+4 3 4x4+u Variable β Std. Error t P>|t| X2 -0.012 0.006 -2.289 0.022 X3 0.596 0.014 41.139 0.000 X4 0.52 1.06 Constant 8.860 1.766 5.017 0.000 What is the marginal effect of x4 on y? (approximate at least to 3 decimal places)
- Table 4.1 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.99794806 Missing 0.99513164 Standard Error 1.64839211 Observations 20 ANOVA Significance F af Missing 16 19 MS F Regression 10561.07486 Missing 1295.585 2.66E-19 Residual 43.47514498 2.717197 Total 10604.55 Coefficients 0.562 Standard Error t Stat P-Value Intercept X1 1.327 0.424 0.677 0.959 0.038 25.245 0.000 X2 1.117 0.125 8.916 0.000 X3 1.460 0.066 22.185 0.000 Consider the output shown in Table 4.1. Which of the predictors has the greatest impact on the dependent variable? X2 Intercept X1 X3A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x and xPredictor Coef SE Coef T PConstant -0.0626 0.2034 -0.31 0.762x 1.1003 0.5441 2.02 0.058x -0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SS MS F PRegression 2 4.7013 2.3506 73.00 0.000ResidualError18 0.5796 0.0322Total 20 5.2809These results indicate that____________The standard deviation of the error terms in an estimated regression equation is known as:
- Consider the linear regression of the variable "balance" as output and the variables "student" and "income" as input variables (from the dataset Default in ISLR2). What is the L1-norm of the regression coefficients, which is the sum of the absolute values of the coefficients?In regression analysis, a common metric used in assessing the quality of the model being used to fit the data is known as the R-squared coefficient. Explain the R-squared coefficient. What is the difference between the R-squared and adjusted R-squared coefficients?18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?