An advertising firm wishes to demonstrate to its clients the effectiveness of the advertising campaigns it has conducted. The following bivariate data on twelverecent campaigns, including the cost of each campaign (in millions of dollars) and the resulting percentage increase in sales following the campaign, werepresented by the firm. Based on these data, we would compute the least-squares regression line to be y=6.19+0.17x, with x representing campaign cost andy representing the resulting percentage increase in sales. (This line is shown below, along with a scatter plot of the data.)Campaign cost, x(in millions of dollars)2.942.123.263.861.691.222.181.683.462.032.734.01Send data to calculator>Increase in sales, y(percent)6.836.586.556.786.206.416.806.656.776.466.507.00Send data to ExcelIncrease in sales(percent)xXxCampaign cost(in millions of dollars)Espㄷ5LC Based on the firm's data and the regression line, complete the following.(a) For these data, values for percentage increase in sales that are greater than the mean of the values for percentageincrease in sales tend to be paired with values for campaign cost that are (Choose one) the mean of the valuesfor campaign cost.greater thanless than(b) According to the regression equation, for an increase of one million dollars in advertising campaign cost, there is acorresponding increase of how many percent in sales?0Aa

The given regression line equation is as follows: ŷ=6.19+0.17x where x represents campaign cost and…

Answered: Based on the firm's data and the…

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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Isla Esme, Inc., sells tea products to various customers. In recent years, profits have been declining. The CFO of the business investigated the reasons for the profit decline and performed regression analysis for sales and costs. He determined that sales depend on product price, delivery speed, customer services, and marketing expenses. He also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales.Question: Define the coefficient of determination and explain what it means in this scenario.
Q8The manager of IT department at a large government agency is investigating the relationship between the age of a PC in years (x) and its monthly maintenance cost (in Dhs). He conducted a regression analysis on a sample of 60 PCs (range from new to 10 years old) and reported the regression equation as y with hat on top equals 110 plus 20 space x Which of the following is the correct interpretation of the slope? For every additional year of age, the monthly maintenance costs increase on average by 110 Dhs. The slope is meaningless in this case. For every additional year of age, the expected monthly maintenance cost of PCs increases by 20 Dhs. The expected monthly maintenance cost of a new PC is 110 Dhs.
A researcher randomly assigns 118 critically ill patients to receive a drug and 234 to receive a placebo. Among those assigned to the Drug Arm of the study 47 died and among those assigned to the Placebo Arm 51 died. Determine the conditional probability of randomly selecting a study participant who died given that he or she was randomly assigned to the Drug Arm of the study. Round your final answer to the nearest thousandth.
7. The table below shows the round-trip fare for flights from one city on major airlines. These were the lowest prices found online in early 2010. The airlines varied, but the travel dates were all the same. Determine the linear regression for the cost of a round-trip ticket given the distance traveled, use your regression to estimate the cost of a 1500-mi flight, and state the correlation coefficient, r, and describe the trend, shape, and strength for the data contained in this problem. Distance (mi.) Cost ($) 205 183 968 180 3097 598 1939 277 1976 313 1033 318 2977 448 1752 408 1143 335
A linear model fit to predict weekly Sales of frozen pizza (in kilograms) from the average Price (in dollars per unit) charged by a sample of stores in 41 recent weeks is Sales = 141,870.25-24,445.32 Price. For the data used to create the model, the average Sales was 52,750 kilograms (SD = 10,282 kilograms) and the correlation between Price and Sales was r=-0.554. If the Price in a particular week was 2 SD higher than the mean Price, how much pizza would the model predict was sold that week? The model would predict that kilograms of frozen pizza were sold. (Round to the nearest integer as needed.)
Based on the analyst's data and regression line, complete the following. For these data, values for earnings per share that are greater than the mean of the values for earnings per share tend to be paired with current stock prices that are (greater than, lesser than) the mean of the current stock prices. According to the regression equation, for an increase of one dollar in earnings per share, there is a corresponding increase of how many dollars in current stock price?
ologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? Suppose the correlation coefficient was −0.12. How would you classify the correlation between temperature and amount of glucose?
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…
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)…
what is the answer?
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…
ABC, Inc., sells tea products to various customers. In recent years, profits have been declining. The CFO of the company investigated the reasons for the profit decline and performed regression analysis for sales and costs. She determined that sales depend on product price, delivery speed, customer services, and marketing expenses. She also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales.Question 1:1. Define regression analysis, simple regression, and multiple regression. Identify one example of each in this scenario.

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