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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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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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.
What is the value of n (the number of observations)? What is the value of Se (standard error for the model) (2 decimals)? What is the average of the x variable (2 decimals)? What is the variance of the x variable (1 decimal)?
Using the following information: Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12,323.80 12,323.80 90.0481 Residual 8 1,454.602 136.8550 Total 9 13,778.4 What is the correlation coefficient? Multiple Choice 0.8944 0.9457 −0.9457 0.8878
For the house sales data in Table 9.4, Table 9.12 shows a regression analysis relating selling price to number of bedrooms. What is the linear relationship between selling price and number of bedrooms? Variable price bedrooms Sum of Source DF Squares Model 1.575534E+11 1 Error 98 8.575962E+11 Total 99 1.01515E+12 Variable Intercept bedrooms O positive 4 O negative N Mean 100 155331 100 3.000 O no relationship TABLE 9.12 Root MSE 93547 R-Square 0.1552 Parameter Estimate -28412 61248 Std Dev 101262.2 0.651339 Mean Square 1.575534E+11 8750981211 Standard Error 44303 14435 t -0.64 4.24 Sig. 0.5228 <.0001