The following multiple regression printout can be used to predict the number of items sold given the current price (in dollars), the competitor's current price (in dollars), and average income generated by the product (in dollars). Regression Analysis: Unit Sales (Q) Versus Price ($), Competitor's Price ($), Income ($) Coefficients Term Coef SE Coef T-Value P-Value Constant 1,322,758 359,134 3.683 0.001 Price ($) -79, 682 35,835 -2.224 0.031 Competitor Price ($) 31,481 31,392 1.003 0.321 Income ($) -17.71 5.84 -3.033 0.004 (a) Is the regression coefficient of competitor's price statistically significant? (Use a = 0.05.) The regression coefficient of competitor's price ---Select--- E statistically significant. (b) Does the variable competitor's price belong in the model? The variable competitor's pric v ---Select--- |belong in the model. may may not

The following multiple regression printout can be used to predict the number of items sold given the current price (in dollars), the competitor's current price (in dollars), and average income generated by the product (in dollars). Regression Analysis: Unit Sales (Q) Versus Price ($), Competitor's Price ($), Income ($) Coefficients Term Coef SE Coef T-Value P-Value Constant 1,322,758 359,134 3.683 0.001 Price ($) -79, 682 35,835 -2.224 0.031 Competitor Price ($) 31,481 31,392 1.003 0.321 Income ($) -17.71 5.84 -3.033 0.004 (a) Is the regression coefficient of competitor's price statistically significant? (Use a = 0.05.) The regression coefficient of competitor's price ---Select--- E statistically significant. (b) Does the variable competitor's price belong in the model? The variable competitor's pric v ---Select--- |belong in the model. may may not

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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10) Using advertised prices for used Ford Escorts a linear model for the relationship between a car's age and its price is found. The regression has an R2-87.4%. Write a sentence summarizing what R2 says about this regression. A) The age of the car explains 9.35% of the variation in price. B) The price of the car explains 12.6% of the variation in age. C) The age of the car explains 87.4% of the variation in price. D) The price of the car explains 87.4% of the variation in age. E) The age of the car explains 12.6% of the variation in price. Solve. Round to two decimal places if necessary. 11) At one college, 68% of students have credit cards. A credit card company, looking for new customers, sets up a booth at the college. If they pick students at random, how many students should they expect to talk to before finding one who does not have a credit card? A) 32 B) 3.13 D) 68 C) 0.32 E) 1.47
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
The following multiple regression printout can be used to predict the price (Price) of a used car given how many miles it has on it (Mileage), the size of the engine (Liter), and whether it has leather interior (Leather), where Leather = 1 for cars that have leather interior and 0 otherwise. Regression Analysis: Price Versus Mileage, Liter, Leather Coefficients Term Coef SE Coef T-Value P-Value Constant 6, 956 511 13.61 0.000 Mileage -0.0846 0.0179 -4.73 0.000 3,797 1,021 Liter 125 30.38 0.000 Leather 314 3.25 0.001 Regression Equation Price = 6,9se - 0.0846 Mileage + 3,797 Liter + 1,021 Leather (a) Given this Minitab printout, is the response variable in this multiple regression equation a categorical or a numerical variable? O numerical O categorical (b) Determine whether the following statement is true or false. Given this Minitab printout, the regression coefficient for miles on the car (Mileage) is statistically significant at a = 0.05. O True False (c) Determine whether the…
State the regression equation and use it to predict taxes for a house with lot size 10K.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Price/Book Value Ratio Return on Equity 13.032 1.405 8.305 2.113 6.654 1.239 3.262 2.449 5.291 2.398 7.719 0.353 2.569 7.593 5.104 2.012 4.797 2.182 4.129 1.918 1.549 1.951 5.046 2.417 2.159 3.011 1.725 5.582 4.698 Growth% 6.385 11.846 135.669 12.459 0.073 25.092 14.188 8.804 22.766 38.082 18.985 25.696 24.519 19.666 11.624 22.849 49.965 69.649 36.696 3.819 41.139 9.218 29.108 17.772 25.114 29.295 23.764 31.405 9.497 14.759 18.541 12.026 39.016 14.228 39.439 14.097 27.022 14.841 13.237 20.669 17.311 14.887 15.849 5.601 16.775 11.172 8.401 16.161 18.404 23.973 16.673 14.725 46.605 28.839 52.021
Bowling Corporation wants to know how closely its current cost driver is correlated with its monthly operating costs. The managerial accountant runs a regression analysis using a statistical software program and produces the following data: Intercept Coefficient = 12,200,567 X Variable 1 Coefficient = 95.65 R-square= 0.8574 What is the Bowling Corporation's monthly cost equation? OA. y $95.65x + $12,200,567 OB. y $95.65x + $8.574 O c. y = $0.8574x + $12,200,567 O D. y = $12,200,567x + $8,574 ---
Use the Financial database from “Excel Databases.xls” on Blackboard. Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company. Use Excel to develop the multiple linear regression model. Assume a 5% level of significance. Which independent variable is the strongest predictor of the average P/E ratio of a company? A. Total Revenues B. Average Yield C. Earnings Per Share D.Return on Equity E. Total Assets F.Dividends Per Share Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287…
A researcher that there is a linear association between the level of potassium content (y) in milligrams and the amount of fiber (x) in grams in cereal. The regression line for the data is computed to y=37+28x rate. It was also computed that r=.59 A. What does the value . 59 tell you? B. What does the value 37 tell you? C. What does the value 28 tell you ? 2. For the line in question 1 what percentage of variability in potassium can be explained by variability in fiber?
A real estate agent has developed a linear model for the price of a house, P, in dollars in terms of the area, A, in square feet for the homes in a certain neighborhood. The data set had areas ranging from 1,000 square feet to 4,500 square feet. Would predicting the prices of a home that is 4,900 square feet be interpolation or extrapolation? Explain. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is outside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is outside the range of the areas in the data.
Owners of Major League Baseball teams have been concerned with attendance in recent years as declines have been noticed across the board. There are a variety of factors that are causing this decline, but it is believed that a team's win-loss record, payroll, stadium age, and home run ability help to drive attendance. space What was the average attendance per team in 2018? Round your answer to 3 decimal places. SPACE What team had the highest and lowest total attendance in 2018? Enter the team names along with their attendance figures as they appear in the data file. Round your attendance numbers to 3 decimal places. SPACE Team Attendance Highest attendance mil Lowest attendance mil space What was the range of attendance in 2018? Round your answer to 3 decimal places.
Prices and mileage of 48 Dodge 2500 Pickup trucks with Cummins diesel engines. Prices ranged from $28,925 to $68,650. Mileage ranged from 315 to 186,466. While I did not share the trucks were 2013 to 2020 models. Please show all work. Interpret b0 in the context of this problem. Interpret b1 in the context of this problem. What percent of total variation in prices is explained by the regression model? Provide evidence to support your answer. Price Mileage 52293 34450 50987 23372 62988 21738 37500 81176 66000 23118 47601 71143 47750 18214 42000 32303 50987 23372 28925 143737 42050 58561 51738 9691 51500 23969 44988 48781 29500 114809 46975 16918 63999 15507 46975 16918 43222 50459 38386 87192 42000 68722 62250 14042 62998 21738 32130 103644 52293 34450 52900 68883 53172 3656 53494 315 39594 88441 63246 23631 58250 34974 59391 18383 50650 42737 54976 84200 68650 14401 51114 47433 43900 74106 37450…
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.