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MATLAB: An Introduction with Applications

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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Question

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 = 6.15+0.19x, 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.)
Campaign cost, x
(in millions of dollars)
2.08
1.71
3.04
2.07
3.76
3.43
3.52
1.77
1.48
3.04
3.92
2.26
1921
Send data to calculator V
Increase in sales, y
(percent)
6.81
6.56
6.55
6.44
6.85
6.52
6.84
6.23
6.39
6.99
6.94
6.62
Increase in sales
(percent)
Based on the firm's data and the regression line, answer the following.
7.2-
7-
6.8-
6.6
6.4-
6.2
6-
X
+
1.5
X
X
X
X
2.5
X
X X
3
+
3.5
Campaign cost
(in millions of dollars)
4
(a) From the regression equation, what is the predicted percentage increase in sales when the advertising campaign cost
is 1.77 million dollars? Round your answer to two or more decimal places.
0
(b) From the regression equation, what is the predicted percentage increase in sales when the advertising campaign cost
is 3.73 million dollars? Round your answer to two or more decimal places.
0
X
S
Es

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r = -0.579 Calculate the coefficient of determination. (Round to three decimal places as needed.) What does this tell you about the explained variation of the data about the regression line? % of the variation can be explained by the regression line. (Round to one decimal place as needed.) About the unexplained variation? % of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)
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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.
consider the following points (1.3,2.4) (2.5,2.4) (3.3,2) (4.2,1.8) (5.1,1.6) find the equation of the regression line (round the values 2 decimal placed.
How does R treat those observations with missing values? In other words, what role do the observations with missing values play in the regression? No command is needed for this question. You must need to provide an answer or take a guess.
Choose the correct answer of each number. 1. An analyst hired by the multinational company to study the sales data of its more than 75 stores worldwide. Used regression analysis to predict $ sales (y) by using $ advertising (x1) and $ salary of sales representatives (x2) across all the branches. You obtained the following regression function: y = 7800 + 8.5x1-1.6x2. If the advertising budgets of one of the branches of the corporation is now $45,000 (which is 10% more than before) and the salary of sales representatives is now $8,500 (which is 20% less than before), then the predicted sales before in that branch is a. $417,750 b. $341,250 c. $376,700 d. $335,6502. An analyst hired by the multinational company to study the sales data of its more than 75 stores worldwide. Used regression analysis to predict $ sales (y) by using $ advertising (x1) and $ salary of sales representatives (x2) across all the branches. You obtained the following regression function: y = 7800 + 8.5x1-1.6x2. If…
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.
Find the regression equation, letting the first variable be the predictor (x) variable. Find the best predicted Nobel Laureate rate for a country that has 79.4 Internet users per 100 people. How does it compare to the country's actual Nobel Laureate rate of 1.2 per 10 million people? Internet Users Per 100 Nobel Laureates 79.3 5.6 80.5 24.2 78.8 8.7 45.2 0.1 82.5 6.2 38 0.1 90.3 25.4 88.6 7.6 80.3 9.1 83.1 12.6 53.3 1.9 76.3 12.7 56.6 3.3 78.5 1.5 91.6 11.5 93.4 25.2 65.3 3.1 57.3 1.9 68.2 1.7 94.8 31.6 85.4 31.3 86.4 19.1 78.3 10.9 2) Check Image
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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?
Write down the properties of R2 . If R2=35.7%, interpret your data. Distinguish between the correlation and regression with example.
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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