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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7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…
I need help with these questions 1. do the covariates and factors interact? 2. can you conclude a homogeneity of regression slopes? 3. can you conclude homogeneity of variance?
Explain the findings of this crosstab, including a description of the table, a calculation of the epsilons, and a discussion of the 10% rule. Explain what the output of the measure of association means in terms of strength and direction of the relationship. Interpret Proportional Reduction of Error (PRE) using the following statement: Knowing the IV will reduce error in predicting the DV by *%.
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 =
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
6. The following results were obtained from a multiple regression analysis. How many observations were involved in this study? A. 25 B. None C. 9 D. 12 E. 30
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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…
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? 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.) p-value =
The following regression model was fitted to sample data with 12 observations: = 30 +4.50x. What is the residual for an observation (x = 2, y = 40)? 0.50 -1 1 -0.50
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