Homework Answers
Here option c will be answer that is null hypothesis will be
There is no relation and alternative will be there is relationship.
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HELP ASAP The following is Excel output from a fitted linear regression model relating the sale price of a home (y...
The following is Excel output from a fitted linear regression model relating the sale price of...
The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 - 1.207517218 Standard Error Stat P-value 1.450657792 147.0819 0 0.029970869 -40.2897 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148749195 Refer to the information above on the regression using age to predict selling price. Which of the following gives the 95% confidence interval for by...
The following information may be used in several questions. The following is Excel output from a...
The following information may be used in several questions. The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 -1.207517218 Standard Error Stat 1.450657792 147.0819 0.029970869 -40.2897 P-value 0 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148 749195 Refer to the information above on the regression using age to predict selling price. What is...
A real estate agent wants to use a multiple regression model to predict the selling price...
A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...
HELP ASAP An antiques dealer is interested in factors that might influence the final selling price...
HELP ASAP
An antiques dealer is interested in factors that might influence the final selling price of grandfather clocks at auction. Her data includes the age of the clock (Age), the number of bidders (Bidders) at the auction, and the final selling price (Price) of the clocks SUMMARY OUTPUT Regression Statistics Multiple R 0.944834723 R Square 0.892722653 Adjusted R Square 0.885313526 Standard Error 139.1365018 Observations 32 ANOVA Significance F 8.76907E-15 2 Regression Residual Total SS MS F 4277159.703 2138580 120.6511...
The tables below shows the results of a multiple linear regression analysis relating the sale price...
The tables below shows the results of a multiple linear regression analysis relating the sale price y of a house (in dollars) versus LandVal (in dollars), Improvement (in dollars) and Area (in square feet): MS Source DF 877,967292656 46.66 100,349 6,272 978,316 0.00001 Regression 3 Residual16 Total 19 Predictor CoeffStd. ErrorT-valueP-value Intercept 1470 5746 0.26 0801 LandVal08145 0.5122 1.59 Improvement 0.8204 0.2112 3.88 2.05 0.131 0.001 Area 13.529 6.856 0.057 At alpha, a - 0.01 we can say that the...
Excel Problem 2 - Chapter 12: PART B: The following data give the selling price, square...
Excel Problem 2 - Chapter 12: PART B: The following data give the selling price, square footage, and age of houses that have sold in a Bend, OR in the past 6 months (note that this is the same base data as Part A, above, with new variables added). Selling Price ($) Square Footage Age (Years) 84,000 1,670 30 79,000 1,339 25 91,500 1,712 30 120,000 1,840 40 127,500 2,300 18 132,500 2,234 30 145,000 2,311 19 164,000 2,377 7...
1. One Price Realty Company wants to develop a model to estimate the value of houses in its inventory The office manager has decided to develop a multiple regression model to help explain the va...
1. One Price Realty Company wants to develop a model to estimate the value of houses in its inventory The office manager has decided to develop a multiple regression model to help explain the variation in house values. (25 points) The office manager has chosen the following variables to develop the model: X1 square feet X2- age in years x3- dummy variable for house style (1 if ranch, 0 if not) X4-2d dummy variable for house style (I if split...
A real estate research firm has developed a regression model relating list price (Y in 1,000)...
A real estate research firm has developed a regression model relating list price (Y in 1,000) with two independent variables. The two independent variables are number of bedrooms and size of the property. Part of the regression results are shown below. ANOVA MS Regression 256881.37 128440.68 Residual 42 726699.96 17302.38 Coefficients Standard Error Star Intercept 54.298 # Bedrooms 53.634 71.326 5.271 33.630 Acres 21.458 1. What has been the sample size? (2 Points) 2. What is the value of the...
Hi I need help with these questions on Excel for linear regression! Gulf Home Data Price...
Hi I need help with these questions on Excel for linear regression! Gulf Home Data Price Size Number of Niceness Pool? Home ($1000s) (Square Feet) Bathrooms Rating yes=1; no=0 This information is taken from 80 homes recently sold 1 260.9 2666 2.5 7 0 along the Gulf of Mexico coast. You are to analyze 2 337.3 3418 3.5 6 1 the data to discover which of the variables have a 3 268.4 2945 2.0 5 1 statistically...
Need help with stats true or false questions Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% con...
Need help with stats true or false questions
Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% confidence interval for A In this case, a t-test with significance level 1% rejects the null hypothesis Ho : A-0 against a two sided alternative. b) Complicated models with a lot of parameters are better for prediction then simple models with just a few parameters c)...
The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 - 1.207517218 Standard Error Stat P-value 1.450657792 147.0819 0 0.029970869 -40.2897 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148749195 Refer to the information above on the regression using age to predict selling price. Which of the following gives the 95% confidence interval for by...
The following information may be used in several questions. The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 -1.207517218 Standard Error Stat 1.450657792 147.0819 0.029970869 -40.2897 P-value 0 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148 749195 Refer to the information above on the regression using age to predict selling price. What is...
A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...
HELP ASAP
An antiques dealer is interested in factors that might influence the final selling price of grandfather clocks at auction. Her data includes the age of the clock (Age), the number of bidders (Bidders) at the auction, and the final selling price (Price) of the clocks SUMMARY OUTPUT Regression Statistics Multiple R 0.944834723 R Square 0.892722653 Adjusted R Square 0.885313526 Standard Error 139.1365018 Observations 32 ANOVA Significance F 8.76907E-15 2 Regression Residual Total SS MS F 4277159.703 2138580 120.6511...
The tables below shows the results of a multiple linear regression analysis relating the sale price y of a house (in dollars) versus LandVal (in dollars), Improvement (in dollars) and Area (in square feet): MS Source DF 877,967292656 46.66 100,349 6,272 978,316 0.00001 Regression 3 Residual16 Total 19 Predictor CoeffStd. ErrorT-valueP-value Intercept 1470 5746 0.26 0801 LandVal08145 0.5122 1.59 Improvement 0.8204 0.2112 3.88 2.05 0.131 0.001 Area 13.529 6.856 0.057 At alpha, a - 0.01 we can say that the...
1. One Price Realty Company wants to develop a model to estimate the value of houses in its inventory The office manager has decided to develop a multiple regression model to help explain the variation in house values. (25 points) The office manager has chosen the following variables to develop the model: X1 square feet X2- age in years x3- dummy variable for house style (1 if ranch, 0 if not) X4-2d dummy variable for house style (I if split...
A real estate research firm has developed a regression model relating list price (Y in 1,000) with two independent variables. The two independent variables are number of bedrooms and size of the property. Part of the regression results are shown below. ANOVA MS Regression 256881.37 128440.68 Residual 42 726699.96 17302.38 Coefficients Standard Error Star Intercept 54.298 # Bedrooms 53.634 71.326 5.271 33.630 Acres 21.458 1. What has been the sample size? (2 Points) 2. What is the value of the...
Need help with stats true or false questions
Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% confidence interval for A In this case, a t-test with significance level 1% rejects the null hypothesis Ho : A-0 against a two sided alternative. b) Complicated models with a lot of parameters are better for prediction then simple models with just a few parameters c)...
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