EP BUSINESS STATISTICS:FIRST COURSE-ACC
8th Edition
ISBN: 9780135179802
Author: Levine
Publisher: PEARSON CO
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In simple linear regression, at what value of the independent variable, X, will the 95% confidence interval for the average value of Y be narrowest? At what value will the 95% prediction interval for the value of Y for a single n ew observation be narrowest?
Import the data from the Hill City Excel file into Minitab.You are trying to predict Price.Note: Mtn View=1 if there is a mountain view, 0 otherwise. The rest of the variables should be self explanatory.
1.Perform an F Test for overall significance of the model
2.Perform a T test for slope for the age variable
3. Find a 95% confidence interval for the slope of the SqFeet variable4. Create a prediction, CI, and PI for 1 new set of x values (any valid numbers you want), and interpret each.5. Run the residual plots and indicate if they show any problems with the model.
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).
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29
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Altitude
31
Temperature
60
40
-41
a. Find the explained variation.
(Round to two decimal places as needed.)
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardThe manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Attached below is the regression output. What can be said about the 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan application? Question content area bottom Part 1 A. The 90% confidence interval is narrower than [0.0109, 0.0143]. B. The 90% confidence interval is wider than [0.1492, 0.6555]. C. The 90% confidence interval is wider than [0.0109, 0.0143].arrow_forwardThe table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. 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 90% confidence level with a redshift of 0.0126. Redshift Distance 0.0237 0.34 a. Find the explained variation. 0.448262 (Round to six decimal places as needed.) b. Find the unexplained variation. (Round to six decimal places as needed.) 0.0541 0.75 0.0723 0.98 C 0.0397 0.57 0.0444 0.62 0.0103 0.13 Darrow_forward
- The table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. 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 90% confidence level with a redshift of 0.0126. Redshift Distance 0.0238 0.31 a. Find the explained variation. 0.0543 0.74 (Round to six decimal places as needed.) b. Find the unexplained variation. (Round to six decimal places as needed.) c. Find the indicated prediction interval. 0.0722 1.02 billion light-yearsarrow_forwardThe table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. 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 90% confidence level with a redshift of 0.0126. Redshift Distance a. Find the explained variation. 0.0237 0.34 (Round to six decimal places as needed.) 0.0541 0.75 0.0723 0.98 C 0.0397 0.57 0.0444 0.62 0.0103 0.13arrow_forwardFor a given set of x and y data values, assume that the regression model assumptions are valid and that a 90% confidence interval for ₁ is given by (-2.2, -0.1). Which of the following statements are true? i) At a = 0.10, there is a significant linear relationship between x and y. ii) In the scatterplot of x and y, the values of y tend to decrease as the values of x increase. iii) Based on this confidence interval, we would reject the null hypothesis of no linear relationship at any significance level a ≤ 0.1. Select one: a. i) O b. i) and ii) O c. ii) d. i), ii), and iii)arrow_forwardListed 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 an altitude of 6327 ft (or 6.327 thousand feet). Altitude 2 8 13 23 28 31 32 Temperature 56 40 27 −1 −34 −41 −50 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<______°Farrow_forwardListed 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 an altitude of 6327 ft (or 6.327 thousand feet). Altitude Temperature3 598 3413 2219 -429 -2931 -4134 -58 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. __<y<__ (Round to four decimal places as needed.)arrow_forward8.3 Question 8) Maintaining your balance may get harder as you grow older. A study was conducted to see how steady the elderly is on their feet. They had the subjects stand on a force platform and have them react to a noise. The force platform then measured how much they swayed forward and backward, and the data is in table #8.3.11 ("Maintaining balance while," 2013). Find a 99% confidence interval for the mean sway of elderly people 19 30 20 19 29 25 21 24 50arrow_forwardStephen Stigler determined in 1977 that the speed of light is 299,710.5 km/sec. In 1882, Albert Michelson had collected measurements on the speed of light ("Student t-distribution," 2013). His measurements are given in table #7.3.6. Is there evidence to show that Michelson’s data is different from Stigler’s value of the speed of light? Test at the 5% level. Table #7.3.6: Speed of Light Measurements in (km/sec) 299883 299816 299778 299796 299682 299711 299611 299599 300051 299781 299578 299796 299774 299820 299772 299696 299573 299748 299748 299797 299851 299809 299723arrow_forwardState the Odds Ratio for the slope and find 95% confidence interval of the slope. (do not forget to include conclusions and show work)arrow_forwardListed 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 Temperature a. Find the explained variation. (Round to two decimal places as needed.) 2 55 8 40 13 25 20 - 3 28 - 26 31 - 41 34 - 53arrow_forwardarrow_back_iosSEE MORE QUESTIONSarrow_forward_ios
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