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Regression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable, hind the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.
14. Old Faithful Using the listed duration and interval after times, find the best predicted “interval after’’ time for an eruption with a duration of 253 seconds. How does it compare to an actual eruption with a duration of 253 seconds and an interval after time of 83 minutes?
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- A local church is interested in determining how length of residence in the present community relates to church attendance. Using a random sample of 15 individuals, they gathered data on how many times in the previous 5 weeks each individual attended church services. The data are provided below. Length of residence in the community Less than 2 years 2-5 years More than 5 years 0 0 1 1 2 3 3 3 3 4 4 4 4 5 4 Using the 5-step model, determine whether and how church attendance is related to length of residence in the community. Use 5% and 1% levels of statistical significance. What are the assumptions for this problem?arrow_forwardSection 10.2 Question #8 Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 450metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports 226 270 364 488 525 Crash Fatality Rate 16.1 15.9 15.6 15.5 15.1 Find the equation of the regression line. y= ___________+( ____________)x (Round the y-intercept to three decimal places as needed. Round the slope to four decimal places as needed.) The best predicted crash fatality rate for a year in which there are 450 metric tons of lemon imports is _________ fatalities per 100,000 population. (Round to one decimal place as needed.)arrow_forwardSection 10.3 Question #6 Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 11 14 20 28 31 34 Temperature 55 39 26 −2 −34 −41 −59 a. Find the explained variation. ______________ (Round to two decimal places as needed.) b. Find the unexplained variation. _______________ (Round to five decimal places as needed.) c. Find the indicated prediction interval. _____________°F < y < ____________ °F (Round to four decimal places as needed.)arrow_forward
- All of the following models would be appropriate for data comprised of randomness and trend except: a. Holt’s exponential smoothing b. Holt-winter’s exponential smoothing c. Average change model d. Linear trend regression (time series decomposition) e. All of the above would be appropriatearrow_forwardIf the general linear regression model is given by the equation: y = a + b?; considering the informationobtained in Figure 2 above, compute the value of a.arrow_forwardExplain the time series regression model assumptions?arrow_forward
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