26. Here is a regression of the 15-year mortgage rate as Y, and the 10-year U.S. governmentTreasury note as XSUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotal0.9806072840.9615906450.9615636150.341004018df1423114211422CoefficientsInterceptX Variable 1What is the regression equation?1.11.0SS4136.816752165.23919484302.055946Standard Error0.0247443970.005273156FMS4136.816752 35575.195170.11628374How much of the variability in Y is explained by X?t StatP-value46.01353137 8.7328E-284188.61387850Which interest rate is higher, the 15-year mortgage rate or the 10-year U.S. governmernote rate?State whether the slope (and X-variable on which it is based) is statistically significaand show why

given the regression line of 15-year mortgage as Y and 10-year US govt. treasury note as X. y=β0+β1x…

Answered: Regression Statistics Multiple R R…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate 1 .919 887 64259 ANOVA Model Sum of Squares df Mean Square F Sig 1 Regression 11.718 002 Residual Total 25.500 7 Coefficients Standardized Unstandardized Coefficients Coefficients 4 Model B Std. Error Beta t Sig. 1 (Constant) 83.230 1.574 52.882 000 TVAdv .304 1.153 7.532 001 NewsAdv .321 .621 4.057 010 What is SSE? O.605 1.065 O-2.506 O 2.605
Assuming that you are the actuary, help this insurance company to make policy decisions on insurance premiums. Your suggestions should be based on the below findings. You can propose to them to gather more evidence if there are important variables omitted in the above model. Regression Statistics Multiple R 0.88 R Square 0.78 Adjusted R Square 0.77 Standard Error 2.52 Observations 80.00 ANOVA df SS MS F Significance F Regression 3 1704.90 568.30 89.50 0.00 Residual 76 482.59 6.35 Total 79 2187.49 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 14.44 5.10 2.83 0.01 4.28 24.60 4.28 24.60 Mother's age at death 0.43 0.05 8.28…
SUMMARY OUTPUT Regression Statistics Multiple R 0.664798 R Square 0.441957 Adjusted R Square 0.376305 Standard Error 6.412199 Observations 20 ANOVA df SS MS F Significance F Regression 2 553.5729 276.7864 6.731793 0.007025498 Residual 17 698.9771 41.1163 Total 19 1252.55 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 26.6651 13.92768 1.91454 0.072535 -2.71974085 56.04995 X1 4.00929 1.1224 3.572068 0.002347 1.641232912 6.377348 X2 0.810165 0.477768 1.69573 0.108172 -0.19783686 1.818168 a. What can you say about the strength of this relationship for the model using the F test? Use α = .05. b. Is y significantly related to each independent variable? Use α = .05. c. Would your answer to b change if α = .001? If so, how? (3+4+3)
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept DISTANCE PAX SWA=YES VACATION=YES HI 0.8567 0.7339 0.7318 39.3716 638 df 5 632 637 86.67589 0.07751 -0.00011 -61.42324 -52.21338 0.00735 t Stat P-value 7.08935 12.2262 5.2751E-31 0.00269 28.8351 2.4522E-117 0.00012 -0.9381 0.3485 3.54152 -17.3438 2.0809E-55 3.57526 -14.6041 7.8163E-42 0.00099 7.3950 4.5121E-13 a) What percent of total variation in FARE does the model overall explain/determine SS MS 2701807.711 540361.542 979676.999 1550.122 3681484.709 Coefficients Standard Error F 348.5929 Significance F 5.5794E-179 Upper 95% 100.5974 72.7544 0.0722 0.0828 -0.0004 0.0001 -68.3778 -54.4687 -59.2342 0.0054 -45.1926 0.0093 Lower 95% b) Does the model overall fit the dat? NO YES c) Should one interpret the estimated value for the intercept? NO 0.25 pt) d) Interpret the value for the estimated coefficient for DISTANCE e) Which explanatory variable has the least…
What percentage of the variability in the number of delivery days for a car is NOT explained by knowing the number of factory options? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Intercept Factory Options 22.31% 72.16% 88.14% 77.69% O 4.64% 0.8814 0.7769 0.7216 4.6400 10 Coefficients 29.73333 3.284848 Standard Error 2.991435587 0.482113498
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The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square X36899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
Summary Output Regression Statistics Mutliple R 0.882871 R Square 0.779461 Adjusted R Square 0.72437 Standard Erro 94.22223 Observation 6 ANOVA How many data points are there in the data set?
Model Summary Adjusted R Square Std. Error of Model R R Square the Estimate 1 .772 .596 .516 215.1509 a. Predictors: (Constant), Porosity ANOVA Sum of Model Squares df Mean Square F Sig. Regression 341943.282 1 341943.282 7.387 .042b Residual 231449.575 46289.915 Total 573392.857 a. Dependent Variable: Runoff b. Predictors: (Constant), Porosity Coefficients Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. (Constant) 1863.761 237.546 7.846 .001 Porosity -80.729 29.703 -.772 -2.718 .042 a. Dependent Variable: Runoff A geomorphologist is building a model to understand variation in the amount of runoff observed in various streams within a large drainage basin. Linear regression is used to determine whether runoff is inversely related to the porosity of the soil. The variables here are thus runoff and porosity. The SPSS output from the analysis is shown above. One of the cases has a runoff value of 900 and a porosity value of 8. Calculate the value of the…
Using results from the regression model, discuss whether you would have confidence in this multiple regression model to predict expected salary based on GPA, age, and gender?
SUMMARY OUTPUT Regression Statistics Multiple R 0.674982 R Square 0.455601 Adjusted R Square 0.442639 Standard Error 2.10391 Observations 44 ANOVA df SS MS F Significance F Regression 1 155.5859 155.5859 35.14924 5.03E-07 Residual 42 185.9104 4.426439 Total 43 341.4964 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4.68476 0.353424 13.25536 1.33E-16 3.971522 5.397998 3.971522 5.397998 Apparel 0.77764 0.131166 5.928679 5.03E-07 0.512937 1.042343 0.512937 1.042343 Obtain the regression equation. (Negative values should be indicated by a minus sign. Round your answers to 4 decimal places.) Y=______X + ______ Calculate R2. (Round your answer to 4 decimal places.) Calculate the degrees of freedom and…
ANOVA ▾ ANOVA Heart Rate Y Cases Gender Group Gender Group Sum of Squares 45030.005 168432.080 1794.005 192729.830 Descriptives Descriptives - Heart Rate Gender Group Female Male df 1 1 1 796 Mean Square Mean SD Control 148.000 16.271 Runners 115.985 15.972 Control 130.000 17.100 Runners 103.975 12.499 45030.005 168432.080 1794.005 242.123 N 200 200 200 200 F Assumption Checks ▾ Test for Equality of Variances (Levene's) F df2 VS-MPR* df1 P 5.562 3.000 796.000 <.001 59.104 *Vovk-Sellike Maximum p-Ratio: Based on the p-value, the maximum possible odds in favor of H, over He equals 1/(-e p log(pl) for p s.37 (Sellke, Bayarri, & Berger, 2001). 185.980 695.647 7.409 Residuals Note. Type III Sum of Squares Vovk-Sellike Maximum p-Ratio: Based on the p-value, the maximum possible odds in favor of H, over H, equals 1/(-e p log(p)) for p s.37 (Sellke, Bayarri, & Berger, 2001). Post Hoc Tests D < .001 <.001 0.007 Female Control VS-MPR* Standard Post Hoc Comparisons - Gender Group Male Control…

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Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total 0.980607284 0.961590645 0.961563615 0.341004018 df 1423 1 1421 1422 SS 4136.816752 165.2391948 4302.055946 MS 4136.8 0.11