Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand fortheir line of company speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are themain drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market.Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the datathat follow:YEAR1234567B9101112UNIT SALES(THOUSANDS)398698898y bar1,3041,1631,1958981,2009821,235875811PRICE $ PERUNIT283209213220209200220209230213216243ADVERTISING($000)6218211,2001,4051,2101,3048751,200691875691691a. Perform a regression analysis based on these data using Excel.Note: Negative values should be indicated by a minus sign. Round your answers to 4 decimal places.Priceadvertising

Here dependent variable (y) is Unit sales.There are two independent variables.Price per unit (x1)…

Answered: a. Perform a regression analysis based…

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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Please solve for A or B and the missing values.
d. Using the regression results reported above, please calculate and interpret the separate effects of being an athlete on college GPA for men and women.
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=a+bx b=-0.66 a=25.376 r²=0.390625 r=-0.625 Use this to predict the number of situps a person who watches 2.5 hours of TV can do. place. Round to one decimal
Here are two sets of bivariate data with the same response variable. The first contains the variables x & y. The second contains the variables w & y | y w y 25.3 43.9 68.8 36.7 43.8 56.7 57 55 49.2 61.3 27.9 52 57.4 69.3 48 | 50.5 33.7 47.3 58.3 49.2 46.3 56 46.6 34.4 67.1 75.8 35.1 48.3 25.7 43.4 19 76.8 47.2 57.9 7.3 56.8 37.5 57.4 32.9 56.2 44.8 60.4 43.8 49 29.7 38.5 26.4 | 60.9 42.8 53.6 55.6 49.4 60.3 69.7 53.9 55 53.2 67.1 47.8 42.7 57.4 66.3 25 54.8 45.5 57.6 13.9 65 42 51.6 69.5 43.6 38.9 54.8 79.5 29
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Use StatKey or other technology to find the regression line to predict Y from X using the following data. Click here to access StatKey. Round your answers to three decimal places. The regression equation is Y = X 6 Y 3.5 10 8 + 4 2 14 12 12 8.5 X.
Use the given data set to answer parts (a) and (b). a. Find the regression equation for the data points. b. Graph the regression equation and the data points. a. Find the regression equation for the data points. G (Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) X 5 y - 1 4 1 1 -1 3 2 2 4 Q
How many independent variables are involved in a multiple regression equation? a. One b. Zero c. Two or more d. At least three
Use the regression capabilities of a graphing calculator to find the equation for the line of best fit for the following points. Round decimals off to the nearest hundredth. y 1 18 2 28 3 34 4 36 5 47 55 7 67 Note: "y =" is already written -- fill in the right side of the equation only y =
A table of data points is given below. a. Find the regression equation for the data points. Use the formulas Sxx=∑ xi−x2 and Sxy=∑ xi−x yi−y to obtain Sxx and Sxy. b. Graph the regression equation and data points. x 1 2 3 y 6 5 10 a. The regression equation is y= + () x. (Simplify your answers. Type integers or decimals. Do not round.)
Use the data below for the PHONES questions that follow. Data compiled from the CIA World Factbook, https://www.cia.gov/. The number of cell phones in use in a country can be approximated from the number of main lines in use in that country by using the regression equation yhat=25+3x which had an R² of about 90%. (The numbers here are millions. For example, "1.2" would represent 1.2 million or 1,200,000.) PHONES 1 At the time the data was collected, the USA had about 314 million cell phones in use and about 150 million main lines in use. What was the residual for the USA? Enter a number of millions. For example, if your answer is 2.85 million enter "2.85". Do not enter anything else such as symbols or words.
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.

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a. Perform a regression analysis based on these data using Excel. Note: Negative values should be indicated by a minus sign. Round your answers to 4 decimal places. y bar Price advertising