A regression function used to examine the factors affecting the chief executive officer salaryin financial, manufacturing, utilities and transportation sector is given as below. Figures inparentheses are standard errors.Ln Y = 4.59 +0.257 log(X) + 0.158 D₁ +0.181D21 -0.283D31se = (0.30) (0.032)(0.089)(0.085) (0.099)n = 209, R² = 0.357WhereY = salary (in thousands)X₁ = annual salesD₁-D₂=D₂=(1, financial sectorotherwiselo,manufacturing sectorotherwiseutililities sectorotherwiseInterpret the coefficient for D₂Compute the approximate percentage difference in estimated salary between the utilities andtransportation sector, holding annual sales fixed. Is the difference statistically significant at1% level?

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Answered: A regression function used to examine…

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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The mall has a set of data with employee age (X) and the corresponding number of annual on-the-job-accidents(Y). Analysis on the set finds that the regression equation is Y=100-3X. What is the likely number of accidents for someone aged 30? 97 100 10 none of the above
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and the number of sit-ups a person can do (y). The results were: y = a+bx b = -0.89 a = 23.49 r² = 0.6519 r = -0.8074 a. If a person watches 7 hours of television a day, predict how many sit-ups he can do. b. If a person can do 4 sit ups, predict how many hours of television a day they watch. hours
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-ax+b a=-1,263 b=21.291 12-0.5776 r=-0.76 Use this to predict the number of situps a person who watches 5 hours of TV can do (to one decimal place)
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=ax+b a=-0.698 b=37.226 r2=0.799236 r=-0.894 Use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place)
Data was recorded for the temperature, in degrees Celsius, of a cup of coffee over a 30-minute period. Given the regression equation, In(Temp) = 4.20 0.023(Time), what is the predicted temperature after 3 minutes? 33.45 °C 62.24 °C 65.17 °C 66.69 °C
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: ŷ = a + bx a = 36.694 b = 0.874 R = = 0.923521 a) Use this to predict the number of situps a person who watches 0.5 hours of TV can e do (to 3 decimal places) b) What is the value of the correlation coefficient (to 3 decimal places) ? Priv Terr ост 21 MacBook Pro esc F1 F2 F3 F4 F5
Consider the plot of the total number E of Ebola cases in West Africa reported to the Centers for Disease Control and Prevention from April 1, 2014, through December 1, 2015. D +|+|||||||||||||||| ► Ebola Cases in West Africa E = A logistic fit for these data is given by 27,841.42 1 + 134.65-0.646 where t is the time in months since April 1, 2014. Below, we have added the graph of this model. +-+|||||||||||||||||| ► Logistic Graph Added (a) Does the first plot, "Ebola Cases in West Africa," show a continuing epidemic or a health crisis that is under control by December 2015? Because the data points appear to be [leveling off ✔✔✔, they show a health crisis that is under control (b) According to the model, what was the total number of cases when the disease was spreading at the fastest rate? Round your answer to the nearest whole number. 27021.61 x cases (c) Use the crossing-graphs method to determine when the disease was growing at the fastest rate. Round your answer, in months after…
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=ax+b a= -0.889 b=32.071 r2=0.505521 r= -0.711 Use this to predict the number of situps a person who watches 5 hours of TV can do (to one decimal place)
Table 3 gives the market value of a luxury sedan (in dollars) xyears after its purchase. Find an exponential regression model of the form y= ab* for this data set. Estimate the purchase price of the sedan. Estimate the value of the sedan 10 years after its purchase. Round answers to the nearest dollar. TABLE 3 Value ($) 1 23,125 19,050 3 15,625 11,875 5 9,450 6 7,125 2.
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: ý=ax+b a=-0.873 b=26.236 r2=0.527076 r=-0.726 Use this to predict the number of situps a person who watches 11 hours of TV can do (to one decimal place)
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=ax+b a=-0.889 b=31.442 r²=0.3721 r=-0.61 Use this to predict the number of situps a person who watches 4 hours of TV can do (to one decimal place)

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