Model SummaryAdjusted RStd. Error of theModelRR SquareSquareEstimate1.91988764259ANOVAModelSum of SquaresdfMean SquareFSig1Regression11.718002ResidualTotal25.5007CoefficientsStandardizedUnstandardized CoefficientsCoefficients4ModelBStd. ErrorBetatSig.1(Constant)83.2301.57452.882000TVAdv.3041.1537.532001NewsAdv.321.6214.057010What is SSE?O.6051.065O-2.506O 2.605

R-squared (R²) is defined as the proportion of the variance in the dependent variable that is…

Answered: Model Summary Adjusted R Std. Error of…

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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A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
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)
Using the regression model from the previous problem, what is the interpretation of the slope coefficient associated with Age? Assume Salary is measured in $1,000's.
The data are the ages of criminals and their victims. The regression output is shown in a separate tab from the data. Do the data support that a prediction of victim age can be obtained given the data provided in the file? Cite the elements of the output you used to draw your conclusion.
21. Least-squares OK? Following is a residual plot produced by MINITAB. Was it appropriate to compute the least-squares regression line? Explain. Residuals Versus x -2 -3 5.0 5.5 6.0 6.5 7.0 7.5 8.0 Residual
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
If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error Total
Data for 34 results are given below: Source SS Regression Residual Total 2,306,075.92 1,217,176.18 3,823,252.10 ABC df 1 32 33 MS 2,306,075.92 47,411.76 Based on the results what do you conclude and why using a = .05? F
What is the least-squares regression line with the point (9,13) included in the data set? Data Set x y 3 6 4 5 5 7 7 6 8 9 8 8 10 8 11 9 11 7 12 10 13 12 13 10 14 11 This is a reading assessment question. ..... y hat = ______x + ______ Type integers or decimals rounded to 4 decimal places as needed
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…
explain on the strength and the variation of the model.
Are the heights of individuals affected by the heights of their parents. Regression Statistics Multiple R R Square Adjusted R Squar 0.631071992 0.398251859 0.365724932 Standard Error 2.914527039 Observations 40 ANOVA df MS F Significance F Regression Residual 2 208.0084392 104.0042 12.24376 8.30181E-05 37 314.2953108 8.494468 Total 39 522.30375 Coefficients Standard Error t Stat P-value Intercept Mother's Height Father's Height 9.804326378 12.39987353 0.79068 0.43417 0.657952815 0.147476295 4.461414 7.34E-05 0.200358437 0.138223638 1.449524 0.155615 1. Write the regression equation that represents the above equation. 2. Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)? 3. Use the equation to predict the height of someone whose mother is 52 inches tall and whose father is 70 inches tall.

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