Increase in sales(percent)An advertising firm wishes to demonstrate to its clients the effectiveness of the advertising campaigns it has conducted. The followingbivariate data on twelve recent campaigns, including the cost of each campaign (in millions of dollars) and the resulting percentageincrease in sales following the campaign, were presented by the firm. Based on these data, we would compute the least-squaresregression 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, yCampaign cost, x(in millions of dollars)(percent)3.026.917.2+1.926.803.836.856.8-1.406.376.6 -3.126.423.566.826.4-4.066.946.21.646.566-2.066.501.626.181.52.53.56.66Campaign cost(in millions of dollars)2.872.256.61Send data to calculatorBased on the firm's data and the regression line, complete the following.(a) For these data, values for campaign cost that are greater than the mean of the values for Based on the firm's data and the regression line, complete the following.(a) For these data, values for campaign cost that are greater than the mean of the values forcampaign cost tend to be paired with values for percentage increase in sales that are(Choose one)the mean of the values for percentage increase in sales.(b) According to the regression equation, for an increase of one million dollars in advertisingcampaign cost, there is a corresponding increase of how many percent in sales?

d on the firm's data and the regression line, complete the following. (a) For these data, values for campaign cost that are greater than the mean of the values for campaign cost tend to be paired with values for percentage increase in sales that are (Choose one) the mean of the values for percentage increase in sales. (b) According to the regression equation, for an increase of one million dollars in advertising campaign cost, there is a corresponding increase of how many percent in sales?

d on the firm's data and the regression line, complete the following. (a) For these data, values for campaign cost that are greater than the mean of the values for campaign cost tend to be paired with values for percentage increase in sales that are (Choose one) the mean of the values for percentage increase in sales. (b) According to the regression equation, for an increase of one million dollars in advertising campaign cost, there is a corresponding increase of how many percent in sales?

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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Suppose that you are interested in the relationship between Reading and Writing scores. (c) Compute and interpret the R2 value for the relationship between Reading and Writing scores. (d) Interpret the value of the slope in the regression model predicting Reading scores from Writing scores. RDG WRTG 34 44 47 36 42 59 39 41 36 49 50 46 63 65 44 52 47 41 44 50 50 62 44 41 47 40 42 59 42 41 47 41 60 65 39 49 57 54 73 65 47 46 39 44 35 39 39 41 48 49 31 41 52 63 47 54 36 44 47 44 34 46 52 57 42 40 37 44 44 33 71 58 47 46 42 33 42 36 47 41 52 54 52 54 39 39 31 41 60 54 47 31 39 28 42 36 47 57 42 49 36 41 50 33 34 34 55 55 28 46 42 42 44 44 60 52 39 57 36 37 44 44 39 42 39 34 42 39 42 44 52 54 65 65 42 26 47 62 52 62 44 54 52 54 47 44 65 57 63 65 60 59 47 44 65 57 53 61 68 63 55 56 39 53 52 52 65 67 52 44 57 67 63…
The correlation between hours of sleep and hours of study is r = 0.14. (a) Calculate r². (Round your answer to four decimal places.) ,2 = = (b) Choose a sentence that interprets value r². 1.96% of the variation in hours of sleep is not explained by hours of study. Variation in hours of study explains about 1.96% of the observed variation in hours of sleep. 1.96% of the variation in hours of sleep is not explained by the variation in hours of study. Hours of study explains about 1.96% of the observed variation in hours of sleep.
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.
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
am. 13.
For major league baseball teams, is there a relationship between player payrolls and gate money? Here are data for each of the American League teams for the year 2000. The variable x denotes the 2000 player payroll (in millions of dollars), and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the Figure 1 scatter plot. Also given is the product of the player payroll and the mean attendance for each of the fourteen teams. (These products, written in the column labelled "xy", may aid in calculations.) Anaheim Baltimore Boston Chicago White Sox Cleveland Detroit Kansas City Minnesota Mean Player payroll, x attendance, y (in $1,000,000s) thousands) New York Yankees Oakland Seattle Tampa Bay Texas Toronto Send data to calculator 59.2 80.5 97.0 42.3 90.5 68.6 31.8 23.5 114.3 43.0 69.9 65.2 72.7 66.8 25.56 40.74 31.98 24.07 42.72 31.23 20.74 13.09 39.88 21.36 38.89 18.27 34.57 22.47 xy x 1513.152 3279.57 3102.06…
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The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the number of bids an item will receive based on the list price. Price in Dollars 20 26 34 42 45 Number of Bids 1 7 6 10 8 Step 1 : Find the estimated slope (b1)? Round your answer to three decimal places.
For 50 students of a class the regression equation of marks in Statistics (X) on the marks in Accountancy (Y) is 3Y – 5 X + 180 = 0. The mean marks of Accountancy is 44 and variance of marks in Statistics is 9/16th of the variance of marks in Accountancy. Find the mean marks of Statistics and the coefficient of correlation between marks in two subjects.
Susan, a personal trainer, was interested in whether or not there was a linear relationship between the number of visits her clients made to the gym each week and the average amount of time her clients exercised per visit. She took the following data. Client 1 2 3 4 5 6 Number of visits per week 1 3 4 2 3 5 Average time spent exercising per visit (hours) 2 1.5 1 2 2 0.30 Using the best fit line, estimate the average time spent exercising per visit for 4 visits per week. Group of answer choices 1.03 hours 1 hour 10.3 hours 2 hours