Analyse the following regression modelRegression Statistics Multiple R0.7958395 R Square0.63336051 Adjusted R Square0.63139987 Standard Error198.684728 Observations377 ANOVA dfSSMSFSignificance F Regression225504235.612752117.8323.0378013.2564E-82 Residual37414763882.339475.6211 Total37640268117.9 CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept6.1060625935.93704140.169910.86517279-64.55791976.770044-64.55791976.770044EquivArea3.623479020.143798225.19836221.3118E-823.340724713.906233323.340724713.90623332Years1.904280340.303174786.281130369.3418E-101.308139522.500421161.308139522.50042116

The regression equation is of the form Y= b0+b1x1+b2x2 Here Y is the dependent variable. x1 and x2…

Answered: Analyse the following regression model…

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 multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept 624.5369 78.49712 7.956176 6.88E-06x 8.569122 1.652255 5.186319 0.000301x 4.736515 0.699194 6.774248 3.06E-05Source df SS MS F p-valueRegression2 1660914 830457.1 58.319561.4E06Residual 11 156637.5 14239.77Total 13 1817552The adjusted R is ____________.0.91380.88910.88510.8981
Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 9090 total observations. Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size)Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size) ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 306,443.7975306,443.7975 153,221.8988153,221.8988 21.727421.7274 2.2211E-082.2211E-08 Residual 8787 613,525.5190613,525.5190 7052.01757052.0175 Total 8989 919,969.3165919,969.3165 What percent of variation in housing prices is explained by the number of bedrooms and lot size? Round your answer to two decimal places
Consider the following ANOVA table for a simple linear regression. Source Sum Degrees of freedom Mean Square F Regression 6789.5 1 6789.5 181.6 Error 336.5 Total 7126.0 10 What is the value of the mean square error for this regression analysis?
Based on the ANOVA table given, is there enough evidence at the 0.050.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? ANOVA Source df SS MS F Significance F Regression 3 212.987150 70.995717 1.092713 0.448523 Residual 4 259.887850 64.971963 Total 7 472.875000
Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS MS F Significance F Regression 3 1453.960807 484.653602 6.860011 0.006054 Residual 12 847.789193 70.649099 Total 15 2301.750000 A) Yes B) No
Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 7979 total observations. Estimated Price=20,206.07+2567.28(Bedrooms)+0.2110(Lot Size)Estimated Price=20,206.07+2567.28(Bedrooms)+0.2110(Lot Size) ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 347,853.9414347,853.9414 173,926.9707173,926.9707 19.665619.6656 1.3090E-071.3090E-07 Residual 7676 672,159.9040672,159.9040 8844.20938844.2093 Total 7878 1,020,013.84541,020,013.8454 Compute the adjusted coefficient of determination for this regression model. Round your answer to four decimal places
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 1. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. 2. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234. 3. Do the sample data provide evidence that the model is useful for predicting average monthly sales for employees based on their sales experience using α=0.05? The test statistic is (Type an integer or decimal rounded to two decimal places as…
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The predicted sales is 8.6 cars. The coefficient of determination is 0.234 Test Statistic is 7.04 P-value=
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is _________ cars. (Type an integer or decimal rounded to one decimal place as needed.)
Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 3401.4 3401.4 19.6 0.000 Residual Error 174 30189.0 173.5 Total 175 33590.4 Give the F-statistic and p-value.

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Regression Statistics
Multiple R	0.7958395
R Square	0.63336051
Adjusted R Square	0.63139987
Standard Error	198.684728
Observations	377

ANOVA
	df	SS	MS	F	Significance F
Regression	2	25504235.6	12752117.8	323.037801	3.2564E-82
Residual	374	14763882.3	39475.6211
Total	376	40268117.9

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	6.10606259	35.9370414	0.16991	0.86517279	-64.557919	76.770044	-64.557919	76.770044
EquivArea	3.62347902	0.1437982	25.1983622	1.3118E-82	3.34072471	3.90623332	3.34072471	3.90623332
Years	1.90428034	0.30317478	6.28113036	9.3418E-10	1.30813952	2.50042116	1.30813952	2.50042116

Analyse the following regression model Regression Statistics Multiple R 0.7958395 R Square 0.63336051 Adjusted R Square 0.63139987 Standard Error 198.684728 Observations 377