Analyse the following regression model. Please include explation for all the elements including T-stat, coefficent, siginificance f etc. Regression Statistics Multiple R 0.79716916 R Square 0.63547867 Adjusted R Square 0.63254686 Standard Error 198.375358 Observations 377 ANOVA df SS MS F Significance F Regression 3 25589530 8529843.34 216.753245 2.2501E-81 Residual 373 14678587.9 39352.7826 Total 376 40268117.9 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -97.981174 79.2847437 -1.2358137 0.21730553 -253.88228 57.9199296 -253.88228 57.9199296 EquivArea 3.5523258 0.15149067 23.4491399 2.2549E-75 3.254443 3.85020861 3.254443 3.85020861 Years 2.04629486 0.31770115 6.44094253 3.6636E-10 1.42158501 2.67100471 1.42158501 2.67100471 Condition 15.0379067 10.2144479 1.47221924 0.14180492 -5.0472147 35.1230282 -5.0472147 35.1230282

Analyse the following regression model. Please include explation for all the elements including T-stat, coefficent, siginificance f etc. Regression Statistics Multiple R 0.79716916 R Square 0.63547867 Adjusted R Square 0.63254686 Standard Error 198.375358 Observations 377 ANOVA df SS MS F Significance F Regression 3 25589530 8529843.34 216.753245 2.2501E-81 Residual 373 14678587.9 39352.7826 Total 376 40268117.9 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -97.981174 79.2847437 -1.2358137 0.21730553 -253.88228 57.9199296 -253.88228 57.9199296 EquivArea 3.5523258 0.15149067 23.4491399 2.2549E-75 3.254443 3.85020861 3.254443 3.85020861 Years 2.04629486 0.31770115 6.44094253 3.6636E-10 1.42158501 2.67100471 1.42158501 2.67100471 Condition 15.0379067 10.2144479 1.47221924 0.14180492 -5.0472147 35.1230282 -5.0472147 35.1230282

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

See similar textbooks

Similar questions

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 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…
Which of the following assumptions is not necessary for unbiasedness of a slope coefficient in a multiple regression model? MLR1 MLR 4 Homoskedasticity Random sampling
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=
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?
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.)
using the following ANOVA Table for regression analysis to compute the adjusted R^2 value
For a linear regression for a sample of n=20 pairs of X and Y values. What is the value of the degrees of freedom for the predicted portion of the Y-score variance, MSregression?
Define the ADL and GLS Estimators of Regression.
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 The predicted sales is 8.6 cars. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is (Type an integer or decimal rounded to three decimal places as needed.)
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 researcher interested in explaining the level of foreign reserves for the country of Barbadosestimated the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+????Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained:Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491OIL 85.39 18.46 4.626 0.0006EXP -377.08 112.19 * 0.0057FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3%Analysis of VarianceSource DF SS MS F pRegression 3 1991.31 663.77 ? ??Error 12 77.4 6.45Total 15a) What is dependent and independent variables? b) Fully write out the regression equation [1] c) Fill in the missing values ‘*’, ‘**’, ‘?’and ‘??’

SEE MORE QUESTIONS

Recommended textbooks for you

Glencoe Algebra 1, Student Edition, 9780079039897…

Algebra

ISBN:

9780079039897

Author:

Carter

Publisher:

McGraw Hill

Glencoe Algebra 1, Student Edition, 9780079039897…

Algebra

ISBN:

9780079039897

Author:

Carter

Publisher:

McGraw Hill

SEE MORE TEXTBOOKS

GET THE APP

About FAQ Academic Integrity Sitemap Document Sitemap

Contact Bartleby Contact Research (Essays)High School Textbooks Literature Guides Concept Explainers by Subject Essay Help Mobile App

GET THE APP

Privacy

Your CA Privacy Rights

Your NV Privacy Rights

About Ads

Manage My Data

bartleby, a Learneo, Inc. business

Regression Statistics
Multiple R	0.79716916
R Square	0.63547867
Adjusted R Square	0.63254686
Standard Error	198.375358
Observations	377

ANOVA
	df	SS	MS	F	Significance F
Regression	3	25589530	8529843.34	216.753245	2.2501E-81
Residual	373	14678587.9	39352.7826
Total	376	40268117.9

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-97.981174	79.2847437	-1.2358137	0.21730553	-253.88228	57.9199296	-253.88228	57.9199296
EquivArea	3.5523258	0.15149067	23.4491399	2.2549E-75	3.254443	3.85020861	3.254443	3.85020861
Years	2.04629486	0.31770115	6.44094253	3.6636E-10	1.42158501	2.67100471	1.42158501	2.67100471
Condition	15.0379067	10.2144479	1.47221924	0.14180492	-5.0472147	35.1230282	-5.0472147	35.1230282