Model SummaryAdjusted RSquareStd. Error ofModelRR Squarethe Estimate1.772.596.516215.1509a. Predictors: (Constant), PorosityANOVASum ofModelSquaresdfMean SquareFSig.1Regression341943.282341943.2827.387.042bResidual231449.57546289.915Total573392.857a. Dependent Variable: Runoffb. Predictors: (Constant), PorosityCoefficientsStandardizedUnstandardized CoefficientsCoefficientsModelBStd. ErrorBetaSig.(Constant)1863.761237.5467.846.001Porosity-80.72929.703-.772-2.718.042a. Dependent Variable: RunoffA geomorphologist is building a model to understand variation in the amount ofrunoff observed in various streams within a large drainage basin. Linear regression isused to determine whether runoff is inversely related to the porosity of the soil. Thevariables here are thus runoff and porosity. The SPSS output from the analysis isshown above. Use the output to identify the equation of the best fit trend line

Answered: Image /qna-images/answer/76382a8f-a8f3-49ef-9e6e-a4f3f1b1587e.jpg

Answered: Model Summary Adjusted R Square Std.…

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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How do I explain why this is a good outcome?
A regression model relating x, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following regression output. Where ntotal = 40. ANOVA df SS MS F Significance F Regression 6,759.5 Residual Total 9,031.4 Coefficients Standard Error t Stat P-value Intercept 75.0 10.408 Number of 40.0 5.323 Salespersons a. Write the estimated regression equation (to whole number). ŷ = + b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) F-value p-value is - Select your answer - we - Select your answer - v Ho c. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) t Stat p-value is - Select your answer - we - Select your answer - v Ho: B, d. Predict the annual sales at the Memphis branch office. This branch employs 10 salespersons. $ thousand (to whole number) %24
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression model
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.
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
Please interpret clearly and how to do if based on the tables and charts presented below, interpret the results and report your answer with an academic style. How should interpret assumptions of the technique used.
Using the previous regression model, is there a statistically significant relationship between GPA and Salary Expectation at the 5% significance level? Enter the word "relationship" if a relationship exists and "no relationship" if no relationship exists
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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