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

See similar textbooks

Similar questions

Suppose you wish to determine the degree to which annual earnings of professional golfers (Earnings) are related to driving distance (Yards Per Drive). The driving distance shows how far a golfer can hit the ball after it hits the ground. You were given the Excel output below. If the image below is not clear, dowload a copy here. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Yards Per Drive 0.216543528 0.046891099 0.042101608 1155621.367 201 df 1 199 200 $$ 1.30747E+13 2.65757E+14 2.78831E+14 Coefficients Standard Error -7,773,135.558 30,737.523 2,867,254.773 9,823.548 4 MS 1.30747E+13 1.33546E+12 O a. a $30,737.523 increase in annual earnings. O b. a $9,823.548 increase in annual earnings. O c. a $2,867,254.773 increase in annual earnings. O d. a $7,773,135.558 decrease in annual earnings. t Stat -2.711002745 3.128963403 Figure 4.1 F 9.790411975 P-value 0.007294364 0.00201776…
Proposed regression line: y ̂=5.4+(2.6)x n=4 Sum of squared deviations: ∑(??−?ℎ???)2∑(yi−yhati)2 = 25.2 Sums of squares: ∑(??−????????)2∑(yi−averagey)2 = 59 ∑?2?−?(????????)2∑xi2−n(averagex)2 = 5 Questions: (Enter answers rounded to one decimal place. If appropriate, include a zero before the decimal point) 1. What is ???Syx? 2. What is ??1Sb1? 3. What is SST? 4. What is SSE? 5. What is ?2R2?
Interpret the slope in this model.
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
1. What is R2 and the standard error for the regression? 1a. What is your point estimate enrollment for Spring 2022?
Please interpret this. Thank you!! (15 minutes)
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
A researcher's results are shown below using Femlab(labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 US states. What is the R2 for this regression? Source of variation df ss ms f Regression 1 5377.836 5377.836 5.228879 residual 48 49367.389 1028.487 total 49 54745.225
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 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
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.

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS