To determine if the listing price of a house influences the selling price; a financial analyst sampled fifty houses andcollected data on sale price, Y,(in $'000) and listed price,X,(in$'000) and fitted a regression model to the data.(a) Calculate the value of the test statistic if the regression sum of squares of fitted model was 25818.5 and errorsum of squares was 1341 (round off answer to two decimal points)

It is given that Total observations, n = 50 No. of parameters, k = 2 SSR = 25818.5 SSE = 1341

Answered: To determine if the listing price of a…

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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Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable. Regression Statistics Multiple R 0.963 R Square Adjusted R Square Standard Error Observations 20 ANOVA df SS MS F Significance F Regression 24686354.49 6171589 47.8 2.3289E-08 Residual 129243 Total 26625000…
Why was the variable “# of customer service representatives” dropped from the model? Write the regression equation. Sales of men’s clothing (predicted) = Are the regression coefficients significantly different from 0? If one mails 10,000 catalogs and has 15 phone lines open, what would the predicted sales of men’s clothing be? How would you interpret the regression coefficient for number of catalogs mailed? What is the final R2 of the model? How would you interpret this? Which of the two independent variables is the most important predictor of the dependent variable? Why?
Increase in sales (percent) An advertising firm wishes to demonstrate to its clients the effectiveness of the advertising campaigns it has conducted. The following bivariate data on twelve recent campaigns, including the cost of each campaign (in millions of dollars) and the resulting percentage increase in sales following the campaign, were presented by the firm. Based on these data, we would compute the least-squares regression line to be y = 6.16+0.18x, with x representing campaign cost and y representing the resulting percentage increase in sales. (This line is shown below, along with a scatter plot of the data.) Increase in sales, y Campaign cost, x (in millions of dollars) (percent) 3.02 6.91 7.2+ 1.92 6.80 3.83 6.85 6.8- 1.40 6.37 6.6 - 3.12 6.42 3.56 6.82 6.4- 4.06 6.94 6.2 1.64 6.56 6- 2.06 6.50 1.62 6.18 1.5 2.5 3.5 6.66 Campaign cost (in millions of dollars) 2.87 2.25 6.61 Send data to calculator Based on the firm's data and the regression line, complete the following. (a)…
A study of 25 online jewelry retailers was done to find the statistical relationship between the price y in dollars of a diamond ring and the weight x in carats of the diamond. Based on this data the following least-squares regression line was found: ^y=−6047.75+11975.14x What is the predicted price of diamond ring a 2.2 carat diamond using this regression line. Round your answer to two decimal places.
The line of best fit through a set of data is y = 41.762 2.926x According to this equation, what is the predicted value of the dependent variable when the independent variable has value 50? y = Round to 1 decimal place.
The following regression equation is based on the analysis of four variables: SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads. RADIO_ADS is the number of radio advertisements aired weekly by the agency. WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads. The variable WEB_VISITS is the number of weekly visitors to their educational website. These data have been recorded every week for the past three years. WEB_VISITS (expected) = 109 + 0.2*SM_DOLLARS + 1.5*RADIO_ADS + 1.1*WS_DOLLARS The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $150 without changing the amount spent on radio ads or web search ads? (Round your answer to the nearest whole number.)
The line of best fit through a set of data is y = – 27.749 + 1.474x According to this equation, what is the predicted value of the dependent variable when the independent variable has value 30? y = Round to 1 decimal place.
The following regression equation is based on the analysis of four variables: SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads. RADIO_ADS is the number of radio advertisements aired weekly by the agency. WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads. The variable WEB_VISITS is the number of weekly visitors to their educational website. These data have been recorded every week for the past three years. WEB_VISITS (expected) = 208 + 1.5*SM_DOLLARS + 0.5*RADIO_ADS + 0.8*WS_DOLLARS The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $108 without changing the amount spent on radio ads or web search ads? (Round your answer to the nearest whole number.)
The adjusted R-squared accounts for the amount of variance explained while also adjusting for the number of independent variables in the linear regression equation. True or False
Describe the correlation coefficient and explain how it is used in regression analysis?
A frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20 frequent flyers of a certain airline. She collected the number of miles flown in the previous year and the total amount of money the flyer spent. A regression line of distance flown on money spent was fit to the data: \hat y = 24000 + 10xy^=24000+10x. A person who spent $1000 is predicted to have flown:
If you have 2 datasets on which you have conducted a regression analysis, how would you determine whether you can combine them into one dataset?

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