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 = 6.14+0.19x, 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)Increase in sales, y(percent)2.896.483.817.02x3.466.863.326.551.586.59X1.666.182.536.671.386.422.076.523.716.771's353.006.94Campaign cost(in millions of dollars)2.036.70Send data to calculatorSend data to ExcelBased on the firm's data and the regression line, answer the following.(a) What was the observed percentage increase in sales when the advertising campaign cost was 1.58 million dollars?X?0(b) From the regression equation, what is the predicted percentage increase in sales when the advertising campaign costis 1.58 million dollars? Round your answer to two or more decimal places.0Increase in sales(percent)xxx

Answered: Image /qna-images/answer/87996c47-30ad-4d53-9ba4-1b46aa64b50e.jpg

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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Rail ridership during the summer months in the North-East is believed to beclosely related to the number of tourists visiting the region. The following tableshows the data on the number of tourists and North-East ridership for the past16 years. (Answer in Excel showing formulas for answers)Year Tourists( in millions) Rail Ridership (in millions) 2000 9 2.32001 4 1.9 2002 7 1.7 2003 5 1.8 2004 12 2.8 2005 13 2.9 2006 14 2.42007 20 4.8 2008 7 1.3 2009 5 1.12010…
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.
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.
Based on the firm's data and the regression line, answer the following. A. From the regression equation, what is the predicted percentage increase in sales when the advertising campaign cost is 1.50 million dollars? Round your answer to two or more decimal places. B. From the regression equation, what is the predicted percentage increase in sales when the advertising campaign cost is 1.62 million dollars? Round your answer to two or more decimal places.
In multiple regression, when all independent variables are considered but the order in which they are added to the model is determined by the amount it contributes to the dependent variable, it is called: a.Two step b.Stepwise c.Simultaneous d.Backward step
The following table shows the number of hours of safety training at a large industrial plant for each of 6 divisions within the plant and the number of work hours lost due to accidents. X: Hours of safety training 10 19 30 50 65 80 Y: Work hours lost 80 65 68 35 10 12 a. Determine the Pearson's Coefficient of Correlation for the given data. Answer rounded to two decimal places b. Which of the following is an apporpriate comment for the correlation between X and Y? There is a strong positive correlation between X and Y There is a moderate positive correlation between X and Y There is a weak positive correlation between X and Y There is a strong negative correlation between X and Y There is a moderate negative correlation between X and Y
For the data in the table, find the regression equation. Here, x gives the number of years of experience for nurses, and y is their annual salary (in $1000's). Round the slope and y-intercept to two decimals, then use your equation to answer the questions below. Years of Salary Experience ($1000's) 40 4 42 43 45 10 46 13 52 15 53 17 56 21 58 22 60 a. What is the expected salary (in $1000's) for a nurse with no work experience? b. By what amount (in $1000's) does an extra year of experience boost the annual salary?
2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?
For the given data below, Solve, a. Find the regression equation, y = b0 + b1x.b. If x = 170 orders, how many shipments are expected?
A. Develop an estimated regression equation that can be used to predict the price of the stock based on the number of shares of company's stocks sold and the volume of exchange. B. Interpret the coefficients of the estimated regression equation that you found in Part a. C. If in a given day, the number of shares of the company that were sold was 94,500 and the volume of exchange on the New York Stock Exchange was 16 million, what would you expect the price of the stock to be?
For 39 nations, a correlation of 0.887 was found between y = Internet use (%) and x = gross domestic product (GDP, in thousands of dollars per capita). The regression equation is y = -3.68 + 1.73x. Complete parts (a) through (c). a. Based on the correlation value, the slope had to be positive. Why? A. The slope and correlation are positive because gross domestic product could not be negative. B. The correlation and the slope are positive because the y-intercept is negative. C. That is a very unusual fact, because the slope and correlation usually have different signs. D. Although slope and correlation usually have different values, they always have the same sign.
A regression was run to determine if there is a relationship between hours of TV watched per day, xx, and number of sit-ups, yy, a person can do in a minute.The results of the regression were: y=ax+b a=-1.399 b=26.311Use this to predict the number of sit-ups a person who watches 12.512.5 hours of TV can do in a minute. Round to the nearest whole number.

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