Observations are taken on sales of a certain mountain bike in 26 sporting goods stores. The regression model was Y= total sales (thousands of dollars), X1 display floor space (square meters), X2 = competitors' advertisingexpenditures (thousands of dollars), X3 = advertised price (dollars per unit).=(a) Fill in the values in the table given here. (Negative values should be indicated by a minus sign. Leave no cellsblank - be certain to enter "O" wherever required. Round your t-values to 3 decimal places and p-values to 4decimal places.)PredictorCoefficientSEtcalcp-valueIntercept1,246.6309.64.028FloorSpace11.4241.7306.599CompetingAdsPrice(6.120)(0.14722)3.9070.08380(1.567)(1.759)(b-1) What is the critical value of Student's t in Appendix D for a two-tailed test at a = .01? (Round your answer to 3decimal places.)t-value=

The given regression model is: Y=a+b1X1+b2X2+b3X3.Here the dependent variable is Y= total sales.And…

Answered: Observations are taken on sales 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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The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.984. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y = - 0.0066x + 43.3954. Complete parts (a) and (b) below. Click the icon to view the data table. (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? Data Table The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Full data set % of the variance in is by the linear model. Miles per Miles per Weight (pounds), x Weight (pounds), x Car Car (Round to one decimal place as needed.) Gallon, y Gallon, y…
The table shows the number of goals allowed and the total points earned (2 points for a win, and 1 point for an overtime or shootout loss) by 14 ice hockey teams over the course of a season. The equation of the regression line is y = - 0.673x +242.664. Use the data to answer the following questions. (a) Find the coefficient of determination, , and interpret the result. (b) Find the standard error of the estimate, s., and interpret the result. Goals Allowed, x Points, y 212 209 222 222 257 259 273 202 210 196 222 208 259 241 e 110 103 103 96 86 75 44 109 103 102 95 89 67 68 (2) ? =O (Round to three decimal places as needed.) Interpret the coefficient of determination. Select the correct choice below and fill in the answer box to complete your choice. (Round to one decimal place as needed.) O A. About % of the variation in number of goals allowed can be explained by the relationship between number of goals allowed and points earned. The remaining variation is unexplained. O B. About % of…
The data below is the amount of tar and nicotine in a sample of different brands of 100 mm, non-menthol, filtered cigarettes. a) Using the calculator, find a linear regression model that gives the amount of nicotine, N, as a function of the amount of tar, t. Write the function below. N(t)= b) use this model of nicotine to estimate the amount of nicotine in a cigarette if the amount of tar is listed as 21 mg. c) What is the correlation coefficient, r, for this model? r= Tar 5 16 17 13 13 14 9 15 2 15 13 14 15 16 7 17 Nicotine 0.4 1 1.2 0.8 0.8 1 0.8 1 0.2 1.1 0.8 1 0.9 1.1 0.6 1.3
State the regression identity for degrees of freedom in this case of three predictor variables.
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were:y=ax+ba=-0.743b=23.803r2=0.725904r=-0.852Use this to predict the number of sit ups a person who watches 3 hours of TV can do, and please round your answer to a whole number.
Interpret the regression coefficient of X3 in relation to the Y ( X3 unit is mile) Y = 58.286 + 13.7604 * X3
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY = -3.14 +0.816HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2740 lb, it has an engine displacement of 1.5 L, and its highway fuel consumption is 40 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. Regression Table The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) Predictor (x) Variables WT/DISP/HWY WT/DISP WT/HWY DISP/HWY WT DISP HWY P-Value R² 0.000 0.943 0.000 0.748 0.000 0.942…
The owner of Showtime Movie Theaters, Inc., used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x 1) and newspaper advertising (x 2). The estimated regression equation was Weekly Gross Revenue ($1000s) Televison Advertising ($1000s) Newspaper Advertising ($1000s) 97 6 1.5 91 3 2 95 5 2.5 93 3.5 2.5 96 4 4.3 94 4.5 2.3 95 3.5 4.2 95 4 3.5 ŷ = 82.5 + 2.01 x 1 + 1.26 x 2The computer solution provided SST = 24 and SSR = 22.876. Compute R 2 and R a 2 (to 3 decimals). R 2 R a 2 When television advertising was the only independent variable, R 2 = 0.551 and R a 2 = 0.476. Are the multiple regression analysis results preferable?
Data were collected on monthly sales revenues (in $1,000s) and monthly advertising expenditures ($100s) for a sample of drug stores. The regression line relating revenues (Y) to advertising expenditure (X) is estimated to be y=−48.3+9.00x. What is the correct interpretation of the slope?
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y-ax+b a=-0.656 b=36.681 r2-0.877969 r=-0.937 Use this to predict the number of situps a person who watches 10 hours of TV can do (to one decimal place) Question Help: Message instructh Submit Question to search
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY = -3.14 +0.818HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2760 lb, it has an engine displacement of 2.2 L, and its highway fuel consumption is 34 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) Regression Table Predictor (x) Variables P-Value R² Adjusted R² WT/DISP/HWY WT/DISP WT/HWY DISP/HWY 0.000 0.943 0.000 0.747 0.000 0.941…
Draw the regression line for the following equation: Y = -3 + (1) X Show your calculation for a low and high value on the predictor value.

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