What is the change in odds, for7 The following results were obtained from a regression of n = 14 housing prices (indollars) on median family income, size of house, and size of lot:%3DMeanSum ofdfsquaresquares4234Regression SS:Residual SS:3487Total SS:Coefficient (b)Standard error(sb)VIFMedian family1.570.341.3incomeSize of house (sq. ft)23.411.22.9Size of lot (sq. ft)-9.57.111.3Constant40,0001000(a) Fill in the blanks.(b) What is the value of 2?(c) What is the standard error of the estimate?(d) Test the null hypothesis that R = 0 by comparing the F-statistic from the tablewith its critical value.(e) Are the coefficients in the direction you would hypothesize?If not, which coefficients are opposite in sign from what you would expect?(f) Find the t-statistics associated with each coefficient, and test the nullhypotheses that the coefficients are equal to zero. Use a = 0.05, and be sureto give the critical value of t.(g) What do you conclude from the variance inflation factors (VIFS)? What (if any)modifications would you recommend in light of the VIFS?(h) What is the predicted sales price of a house that is 1500 square feet, on alot 60'x100', and in a neighborhood where the median family income is$40,000?F.3 一二

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Description: What is the change in odds, for7 The following results were obtained from a regression of n = 14 housing prices (indollars) on median family income, size of house, and size of lot:%3DMeanSum ofdfsquaresquares4234Regression SS:Residual SS:3487Total S

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The following results were obtained from a regresSion of n= 14 housing prices (in dollars) on median family income, size of house, and size of lot: Mean Sum of df square squares 4234 Regression SS: Residual SS: 3487 Total SS: Coefficient (b) Standard error(sb) VIF 1.57 0.34 1.3 Median family income 11.2 2.9 Size of house (sq. ft) 23.4 Size of lot (sq. ft) -9.5 7.1 11.3 Constant 40,000 1000 (a) Fill in the blanks. (b) What is the value of r? (c) What is the standard error of the estimate? (d) Test the null hypothesis that R² = 0 by comparing the F-statistic from the table with its critical value. %3D (e) Are the coefficients in the direction you would hypothesize? If not, which coefficients are opposite in sign from what you would expect? (f) Find the t-statistics associated with each coefficient, and test the null hypotheses that the coefficients are equal to zero. Use a = to give the critical value of t. 0.05, and be sure (g) What do you conclude from the variance inflation factors (VIFS)? What (if any)

The following results were obtained from a regresSion of n= 14 housing prices (in dollars) on median family income, size of house, and size of lot: Mean Sum of df square squares 4234 Regression SS: Residual SS: 3487 Total SS: Coefficient (b) Standard error(sb) VIF 1.57 0.34 1.3 Median family income 11.2 2.9 Size of house (sq. ft) 23.4 Size of lot (sq. ft) -9.5 7.1 11.3 Constant 40,000 1000 (a) Fill in the blanks. (b) What is the value of r? (c) What is the standard error of the estimate? (d) Test the null hypothesis that R² = 0 by comparing the F-statistic from the table with its critical value. %3D (e) Are the coefficients in the direction you would hypothesize? If not, which coefficients are opposite in sign from what you would expect? (f) Find the t-statistics associated with each coefficient, and test the null hypotheses that the coefficients are equal to zero. Use a = to give the critical value of t. 0.05, and be sure (g) What do you conclude from the variance inflation factors (VIFS)? What (if any)

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 study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.
Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 291.4 291.4 2.01 0.158 Residual Error 174 25230.0 145.0 Total 175 25521.4 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is =The p-value is =
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The regional manager of a franchise business is interested in understanding how income in a region affects sales. Below is a regression output for sales ($’000) regressed on the average household income of an area ($’000) Linear Fit Sales = 14.5774 + 2.9048*Income Summary of Fit RSquare 0.9683 RSquare Adj 0.9630 Root Mean Square Error 3.1083 Mean of Response 43.6250 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 1771.9048 1771.90 183.3946 Error 6 57.9702 9.66 Pro>F C. Total 7 1829.8750 ItI Intercept 14.5774 2.4101 6.05 0.0009* Income 2.9098 0.2145 13.54 < 0.0001* Answer the following questions: (i) What is the average sales across all regions? (ii) Interpret the slope of regression (iii) What is the prediction of the value of sales in a region with an average…
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.
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