The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females. Regression Analysis: Height Versus Shoe Size, Gender Coefficients Term Coef SE Coef T-Value P-Value Constant 55.29 1.03 53.68 0.000 Shoe Size 1.165 0.17 0.000 Gender 2.579 0.489 5.27 0.000 (a) Find the value of the test statistic for shoe size. (Round your answer to two decimal places.) (b) Is the regression coefficient of shoe size statistically significant? (Use a = 0.05.) **...... The regression coefficient of shoe size ---Select--- O statistically significant. (c) Does the variable shoe size belong in the model? The variable shoe size ---Select--- belong in the model. (d) Interpret the regression coefficient of Gender. Males are inches taller than females, on average, controlling for shoe size.

The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females. Regression Analysis: Height Versus Shoe Size, Gender Coefficients Term Coef SE Coef T-Value P-Value Constant 55.29 1.03 53.68 0.000 Shoe Size 1.165 0.17 0.000 Gender 2.579 0.489 5.27 0.000 (a) Find the value of the test statistic for shoe size. (Round your answer to two decimal places.) (b) Is the regression coefficient of shoe size statistically significant? (Use a = 0.05.) **...... The regression coefficient of shoe size ---Select--- O statistically significant. (c) Does the variable shoe size belong in the model? The variable shoe size ---Select--- belong in the model. (d) Interpret the regression coefficient of Gender. Males are inches taller than females, on average, controlling for shoe size.

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 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 XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
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The following regression is fitted using variables identified that could be related to student requestedloan amount, LOAN-AMT($) for returning students of a certain University. ????−??? = ? + ? ?????? + ? ???? + ? ????Where ACCEPT = the percentage of applicants that was accepted by the university, PREV = previous loan amount and OUTS =outstanding loan amountThe data was processed using MNITAB and the following is an extract of the output obtained:EXHIBIT 2Predictor Coef StDev T PConstant -26780 6115 * 0.000ACCEPT 116.00 37.17 3.14 0.003PREV -4.21 14.12 -0.30 **OUTS 70.85 15.77 4.49 0.000S = 2685 R-Sq = 69.6% R-Sq(adj) = 67.7%Analysis of VarianceSource DF SS MS F PRegression 3 808139371 269379790 37.37 0.00Residual Error 49 353193051 7208021Total 52 1161332421a)What is dependent and independent variables? b)Fully write out the regression equation c)What is the sample size used in this investigation? d)Fill in the blanks identified by ‘*’ and ‘**’. e)Is β significant, at the 5% level of…
Do movies of different types have different rates of return on their budgets? Here's a regression of USGross (SM) on Budget for comedies and action movies with an indicator variable. Complete parts (a) through (d). Dependent variable is: USGross ($M) Coefficient SE(Coeff) - 6.78278 16.95 1.00523 Variable Constant Budget ($M) Comedy 24.0373 0.1613 11.73 t-ratio P-value -0.400 0.6907 6.23 <0.0001 2.05 0.0451 a) Write out the regression model. USGross = + ( Budget + (Comedy R-squared = 32.8% R-squared (adjusted) = 31.0% s = 47.51 55 degrees of freedom
The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 + 4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Сoef SE Coef т P Constant 37718 14177 2.66 ** Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF MS F P Regression 3 76501718347 25500572782 Residual Error 96 60109046053 626135896 Total 99 • Is y significantly different from -0.5? Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. Give an interpretation to the term “R-sq" and comment on its value.
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The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $6 million. Is the result close to the actual number of viewers, 8.9 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. 98 3.5 3 7 13 12 13 10 2 6.8 6.3 10.2 8.5 4.4 1.8 2.7 What is the regression equation? y=+x (Round to three decimal places as needed.) What is the best predicted number of viewers for a television star with a salary of $6 million? The best predicted number of viewers for a television star with a salary of $6 million is million. (Round to one decimal place as needed.) Is the result close to the actual number of viewers, 8.9 million? O A. The result is very close to the actual number of viewers of 8.9 million. O B. The…
Identify whether this statement is True or False and provide justification for your answer. “A positive correlation between attitude and intention will always result in a significant effect between the two variables in regression analysis.”
The accompanying table shows a portion of data that refers to the property taxes owed by a homeowner (in $) and the size of the home (in square feet) in an affluent suburb 30 miles outside New York City. Click here for the Excel Data File Taxes Size 21,987 2,403 17,353 2,451 29,238 2,866 a. Estimate the sample regression equation that enables us to predict property Taxes on the basis of the size of the home. (Round your answers to 2 decimal places.) Taxes = + Size. b. Interpret the slope coefficient. O As Property Taxes increase by 1 dollar, the size of the house increases by 6.71 ft. O As Size increases by 1 square foot, the property taxes are predicted to increase by $6.71. c. Predict the property Taxes for a 1,400-square-foot home. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) Taxes
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.
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 14177 2.66 ** Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq (adj) =54.6% Source DF MS F P Regression Residual 76501718347 25500572782 *** **** Error 96 60109046053 626135896 Total 99 d) Is y significantly different from -0.5? e) Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. f) Give an interpretation to the term “R-sq" and comment on its value.