The analyst is interested in estimating effects of percentage change of individual work experience (x) on percentage change of salary (). She may run model(s):
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- Give me an example How Efficient are Regression analysis for Estimating Costs?Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9-0.00749x, where x represents weight. Complete parts (a) through (d). Click the icon to view the car data. The site to your captio V.UVITU. D. The slope is -0.00749 and the y-intercept is 58.9. c. What is the predictor variable? ... OA. The predictor variable is highway fuel consumption, which is represented by x. OB. The predictor variable is highway fuel consumption, which is represented by y. C. The predictor variable is weight, which is represented by x. OD. The predictor variable is weight, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2994 lb? The best predicted value of highway fuel consumption of a car that weighs 2994 lb is (Round to one decimal place as needed.) mi/gal.A pediatrician wants to determine the relation that exists between a child's height (x) and head circumference (y). She randomly selects 11 children from her practice and measures their height and head circumference in inches. She finds that the correlation is 0.694, and the regression equation is y = 0.294x + 2.02. What proportion of the variation in head circumference can be explained by the variation in the values of height? Round your answer to three decimal places. %
- Recently there has been a rise in fatal accidents caused by “distracted driving.” The United States National Highway Traffic Safety Administration (NHSTA) defines a distraction as anything that diverts attention from driving. The following model estimates the trend in distracted driving accidents over time based on data from the NHSTA:D = 0.015t - 29.97 In this model, D is the decimal to be turned into a percentage of all fatal accidents caused by distracted driving, and t is any calendar year between 2000 and 2020. Use the model to estimate the rate for 2005 and 2020. Note: this means to plug n 2005 for t and simplify and then turn your answer into a percent. Then plug in 2020 for t and simplify and then turn your answer into a percent.Data was collected for a regression analysis comparing car weight and fuel consumption. b0 was found to be 32.7, b1 was found to be -7.6, and R2 was found to be 0.86. Interpret the y-intercept of the line. On average, each one unit increase in the weight of a car decreases its ful consumption by 7.6 units. On average, when x=0, a car gets -7.6 miles per gallon. On average, when x=0, a car gets 32.7 miles per gallon. On average, each one unit increase in the weight of a car increases its fuel comsumption by 32.7 units. We should not interpret the y-intercept in this problem.The scatter plot shows the average monthly temperature, x, and a family's monthly heating cost, y, for 25 different months. (a) Write an approximate equation of the line of best fit for the data. It doesn't have to be the exact line of best fit. (b) Using your equation from part (a), predict the monthly heating cost for a month with an average temperature of 25 °F. Note that you can use the graphing tools to help you approximate the line. 100- y 90- x 80- × × xx 70- Monthly x x heating cost 60- × (in dollars) 50- × 40- 30- 20- 10- × x x x x x 0 10 20 30 40 50 60 70 80 90 100 Average monthly temperature (in °F) (a) Write an approximate equation of the line of best fit. y = (b) Using your equation from part (a), predict the monthly heating cost for a month with an average temperature of 25 °F. $ ☐ G
- The scatter plot shows the average monthly temperature, x, and a family's monthly heating cost, y, for 25 different months. (a) Write an approximate equation of the line of best fit for the data. It doesn't have to be the exact line of best fit. (b) Using your equation from part (a), predict the monthly heating cost for a month with an average temperature of 35 °F. Note that you can use the graphing tools to help you approximate the line. y 100- 90+ 80+ ? 70+ Monthly heating cost (in dollars) 60+ 50+ 40+ 30- 20+ 10+ X + 0 10 20 30 40 50 60 70 80 90 100 Average monthly temperature (in °F) X X X X X X x X xx X X X X X xx X X X X X A X Ś (a) Write an approximate equation of the line of best fit. y = 0 (b) Using your equation from part (a), predict the monthly heating cost for a month with an average temperature of 35 °F. $0 X Ś ?Explain the Regression Functions That Are Nonlinear in the Parameters?If the regression line showing the effect of education on income has a slope of 1000. a) the variables are not related b) the Y intercept would be 1.00 c) every change in education increases income d) every year of education increases income by 1000
- The following equation describes the relationship between output and labor input at a sample of work stations in a manufacturing plant ŷ = 2.35+2.20X. Suppose, for a selected workstation, the labor input is 5, the predicted output is?Statistics professor wants to use the number of hours (x) a student studies for a statistics final exam to predict the final exam score (y). A regression model was fit based on data collected from a class during the previous semester, with the following results: (y hat): y = 35.0 + 3X What is the slope? a. Yb. 35.0c. 3d. XSeveral surveys in the United States and Europe have asked people to rate their happiness on a scale of 3 = "very happy," 2 = "fairly happy," and 1 = "not too happy," and then tried to correlate the answer with the person's income. For those in one income group (making $25,000 to $55,000) it was found that their "happiness" was approximately given by y = 0.065x - 0.613, where x is in thousands of dollars. Find the reported "happiness" of a person with the following incomes (rounding your answers to one decimal place). (a) $30,000 (b) $50,000 (c) $55,000