Which statement best describes this regression where Y= highway miles per gallon (MPG) in 91 cars?R2Adjusted R2RStandard ErrorSourceRegressionResidualTotalMultiple Choice0.4990.4440.7074.019 Dependent VariableSS1,305.72511,308.38482,614.1099nkdf98190MS145.080616.152991HwyMPGF8.98Regression is statistically significant but with large error in the MPG predictions.Regression is not quite significant, but predictions should be very good.Regression is statistically significant and has quite small MPG prediction errors.p-value.0000

Here we have given that the summary table table of regression.Regression analysis is done for Y =…

Answered: Which statement best describes this…

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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The regression equation for predicting a woman's muscle mass (a quantitative metric, Y) from age (in years, X) is given as ý = 156.35 - 1.19. The ANOVA table for the linear regression of Y vs X is given below. Source Df SS MS p-value Age 11627.5 11627,5 174.06 <,0001 Error 58 3874.4 66.8 Total 59 15501.9 Fill in the blanks below that demonstrate how to construct an interval estimate for the muscle mass of an individual 60-year-old woman. Use a confidence level of 95%; note that the multiplier is t = 2.002 and the standard error of the mean response is s(i,) = 1.055. 95% Interval: ( Select) + ( Select ) [ Select]
Consider the following table: Source SS DF MS Test Statistic Regression 6 745.77 Error 788.51 Total 12359.72 16 How much of the variation in the dependent variable is explained by the regression? Round your answer to two decimal places, if necessary.
You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per month
Listed below is the multiple regression equation for predicting Y by X₁ through Xs. Y is the number of sales per month. X₁ is the number of sales calls made X2 is the number of hours on the showroom floor X3 is the amount spent on radio ads X is the number of text messages sent Xs is the number of email messages sent Regression Statistics R Square 0.634 Standard Error 7.6796 Observations 45 Intercept X₁ X₂ X3 XA Xs Coefficients Standard Error 6.8163 0.1005 -0.3207 0.2084 0.1221 0.0943 77.1211 1.9439 -11.123 0.1144 11.34 1.23 What is the computed value of the F statistic you would use to test the significance of the entire model? Round your answer to two decimal places.
The following multiple regression printout can be used to predict the price (Price) of a used car given how many miles it has on it (Mileage), the size of the engine (Liter), and whether it has leather interior (Leather), where Leather = 1 for cars that have leather interior and 0 otherwise. %3D Regression Analysis: Price Versus Mileage, Liter, Leather Coefficients Term Coef SE Coef T-Value P-Value Constant 6,959 511 13.62 0.000 Mileage -0.0848 0.0178 -4.76 0.000 Liter 3,795 126 30.12 0.000 Leather 1,028 311 3.31 0.001 Regression Equation Price = 6,959 - 0.0848 Mileage + 3, 795 Liter + 1,028 Leather (a) Given this Minitab printout, is the response variable in this multiple regression equation a categorical or a numerical variable? O numerical O categorical (b) Determine whether the following statement is true or false. Given this Minitab printout, the regression coefficient for miles on the car (Mileage) is statistically significant at a = 0.05. O True O False (c) Determine whether the…
J 1 The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response why variable is city fuel consumption in miles/gallons. The predictor X variables are weight weight in pounds, DISP engine displacement leaders NHW Y Highway fuel consumption in miles/gallons. It's only one predictor X variable is used to predict the city fuel consumption, which single variable is best? Why? The best variable is (WT/HWY/DISP) because it has the best combination of (large/small) p-value, (answer) and (large/small) adjusted R^2 (answer) (type integers are decimals. Do not round)
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
The following multiple regression printout can be used to predict a person's average annual salary given his or her years of employment and number of years of education beyond high school. Regression Analysis: Salary Versus YrsEm, Educ Coefficients Term Constant YrsEm Educ Coef 23,171 678 1,918 SE Coef 1,771 142 376 T-Value 13.08 4.77 5.10 P-Value 0.000 0.000 0.000 (a) Is the regression coefficient of education (Educ) statistically significant? (Use a = 0.05.) Given this output, the regression coefficient for education ---Select--- statistically significant. (b) Does the variable education belong in the model? Given this output, the variable education ---Select--- --Select--- belong in the model. (c) Given this output, which of the following is the correct interpretation for Education in this model? O An individual with 4 years of education beyond high school can expect an annual salary of $7,672. O The estimated change in the average value of annual salary of getting an additional…
Question. Using the following regression result, make your answers to the questions. Call: Im(formula - wage - educ, data - data) Residuals: 10 Median -5.1579 -2.5066 0.3816 2.4539 4.4737 Min 30 Max Coefficients: Estimate Std. Error t value Pr(>1tl) (Intercept) 9.0526 educ 5.1420 1.761 0.11635 2.1842 0.5994 3.644 0.00655 ** --- Signif. codes: 0 ***** 0.001 *** 0.01 ** 0.05 . 0.1' 1 Residual standard error: 3.305 on 8 degrees of freedom Multiple R-squared: 0.6241, F-statistic: 13.28 on 1 and 8 DF, p-value: 0.006549 Adjusted R-squared: 0.5771 1) Using the estimate and standard error of educ variable, perform the following hypothesis test. Ho:Beduc = 0 H1: Beduc # 0 2) Interpret the above hypothesis test result.
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Predictor Coef SE Coef t-ratio Constant Arm span -7.611 2.567 2.965 0.046 0.186 0.035 5.377 0.000 S = 1.61 R-Sq = 63.0% R-Sq (Adj) = 64.9% Which of the following is the best interpretation of the standard deviation of the residuals? The typical arm span is 161 centimeters. The typical foot length is 16.1 centimeters. The typical distance between the observed and predicted arm spans is 1.61 centimeters. The typical distance between the observed and predicted foot lengths is 1.61 centimeters. Op
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…

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