We ran a multiple regresssion model with three predictor variables with the partial Excel output shown above. To test for the usefulness of the model, the computed F-value and the P-value, respectively, are:
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- Please answerIn the method of regression, data sets are summarized in a useful form. The “independent” pieces of data are called inputs or regressors, whereas a quantity that is a function of the inputs, will be called a response. In an experiment, the effect of increasing storage temperature is related to the number of spoiled loaves of bread after 7 days. In this case, which variable will be the regressor?Ankit
- The following regression model was estimated. Q is the number of meals served, P is the average price per meal (customer ticket amount, in dollars), Rxis the average price charged by competitors (in dollars), Ad is the local advertising budget for each outlet (in dollars), and I is the average income per household in each outlet's immediate service area. Least squares estimation of the regression equation on the basis of the 25 data observations resulted in the estimated regression coefficients and other statistics given in Table below. Variable Coefficient Standard Error of Coefficient Intercept 128832.240 69974.818 Price (P) Competitor Price (Px) | Advertising (Ad) Income () -19875.954 4100.856 15467.936 459.280 0.261 0.094 8.780 1.017 Coefficient of determination R =83.3% (a) Interpret the coefficients of independent variables. (b) Test the significance of independent variables at 5% level of Significance. (c) Interpret R? with the help of adjusted R2. (d) Test for the overall…Interpret the following graphs for multiple linear regression and comment on the validity of model assumptionsA study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.
- This dataset continues our saga of modeling the price of this popular Honda automobile. The dataset has now been cleaned to remove the columns with the dealership where the car was offered for sale and specific trim. (a) write out your model in econometric notation. Be very precise! (b) using the 93 observations in the dataset, estimate a model where price is a function of age, mileage and trim of the car. Be sure to avoid the dummy variable trap!! Fully report the results of your model. In this case, interpretation of the coefficients on the dummy variables is particularly important. (c) test the hypothesis that the specific trim does not affect the price of a Civic. Be sure to do all parts of the hypothesis test. (please fully describe steps if you are using Excel) Price Years Old KM EX EXT SE Sport Touring 6555 9 290363 0 0 0 0 0 9999 9 142258 0 0 0 0 0 10281 6 132644 0 0 0 0 0 12480 5 167125 0 0 0 0 0 12991 7 57398 0 0 0 0 0 12991 6 93046 0 0 0 0 0 12991…We are given the following training examples: (1.2, 3.2), (2.8, 8.5), (2,4.7), (0.9, 2.9), (5.1, 11) We want to apply a 3-nearest neighbor rule in order to perform regression. (a) : Predict the label (real value) at each of the following two points: 1 = 1.5 and x2 = 4.5. time we want to perform distance-weighted nearest neighbor regression. What values do we predict now for x1 = 1.5 and x2 = 4.5? (b). Instead of weighing the contribution of each of the 3 nearest neighbors equally, thisA company that manufactures computer chips wants to use a multiple regression model to study the effect that 3 different variables have on y, the total daily production cost (in thousands of dollars). Let B,, B,, and B, denote the coefficients of the 3 variables in this model. Using 22 observations on each of the variables, the software program used to find the estimated regression model reports that the total sum of squares (SST) is 485.84 and the regression sum of squares (SSR) is 229.91. Using a significance level of 0.10, can you conclude that at least one of the independent variables in the model provides useful (i.e., statistically significant) information for predicting daily production costs? Perform a one-tailed test. Then complete the parts below. Carry your intermediate computations to three or more decimal places. (a) State the null hypothesis H, for the test. Note that the alternative hypothesis H, is given. H, :0 H, : at least one of the independent variables is useful…
- The following output is from a multiple regression analysis that was run on the variables FEARDTH (fear of death) IMPORTRE (importance of religion), AVOIDDTH (avoidance of death), LAS (meaning in life), and MATRLSM (materialistic attitudes). In the regression analysis, FEARDTH is the criterion variable (Y) and IMPORTRE,AVOIDDTH, LAS, and MATRLSM are the predictors (Xs). The SPSS output is provided below, followed by a number of questions. Descriptive Statistics Mean Std. Deviation N feardth 27.0798 8.08365 163 importre 5.8282 2.46104 163 avoiddth 18.5460 6.97633 163 Las 70.1288 9.89460 163 matrlsm 53.5552 10.21860 163 Model Variables Entered Variables Removed Method 1 matrlsm, avoiddth, importre, lasa . Enter a. All requested variables entered. b. Dependent Variable: feardth Model Summary Model R R Square Adjusted R Square…Use the following information to answer questions 6 and 7. In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The least-squares regression line was computed and added to a scatterplot of these data. On the plot, one data point is marked with an "X." The equation of the least-squares regression line is: Tread depth = 360.64 - 11.39 Miles The data value marked with "X" in the provided scatterplot has Tread Depth (Mils) 60 80 150 0 5 O a negative value for the residual. O a positive value for the residual. O a zero value for the residual. O a zero value for the correlation. 10 15 Miles (x 1000) 20 25 30A student takes a true-false test that has 10 questions and guesses randomly at each answer. LetX be the number of questions answered correctly. c)The mean and stand deviation of X (show work and round final answer to 2 decimal places) Mean: Standard deviation =