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 df SS MS Regression 2 154881.37 77440.69 Residual 57 899723.61 15784.62 Coefficients Standard Error t Stat Intercept 74.298 91.326 # Bedrooms 73.634 25.271 Acres 41.458 38.630 What has been the sample size? What is the value of the F test statistic for testing whether the regression model is significant? What is the rejection rule for testing whether the regression model is significant at the 0.05 level of significance? What is the value of the t test statistic for testing whether the variable Acres is significant?

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 df SS MS Regression 2 154881.37 77440.69 Residual 57 899723.61 15784.62 Coefficients Standard Error t Stat Intercept 74.298 91.326 # Bedrooms 73.634 25.271 Acres 41.458 38.630 What has been the sample size? What is the value of the F test statistic for testing whether the regression model is significant? What is the rejection rule for testing whether the regression model is significant at the 0.05 level of significance? What is the value of the t test statistic for testing whether the variable Acres is significant?

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015

1st Edition

ISBN:9781680331141

Author:HOUGHTON MIFFLIN HARCOURT

Publisher:HOUGHTON MIFFLIN HARCOURT

Chapter4: Writing Linear Equations

Section: Chapter Questions

Problem 12CR

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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…
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A researcher was investigating variables that might be associated with the academic performance of high school students. The data included the average Math SAS score of all high school seniors in the city that took the exam (labeled as the variable SAT-M), the average number of dollars per pupil spent on education by the city (labeled as the variable $Per Pupil), and the percentage of high school seniors in the city that took the exam (labeled as the variable %Taking). The researcher ran the following multiple linear regression model as SAT-M=Beta0 + Beta1($Per Pupil) + Beta2(%Taking). This model was fit to the data using the method of least-squares, results shown inside of table within photo. If we want to test using ANOVA F-test with hypotheses Ho: Beta1=Beta2=0 versus H1: at least one of the Beta is not 0, what would the value of our F-statistic mean?

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ANOVA
	df	SS	MS
Regression	2	154881.37	77440.69
Residual	57	899723.61	15784.62

	Coefficients	Standard Error	t Stat
Intercept	74.298	91.326
# Bedrooms	73.634	25.271
Acres	41.458	38.630