Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 11 10 y 7.11 12 8. 6.44 8. 12 13 4 12.71 7.37 7.82 8.82 6.14 5.4 7.93 6.52 5.94 25 25 25 25- 20 20- 20 20- 15 151 15- 15: 10- 105 10- 10- 5. 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Find the equation of the regression line. (Round the y-intercept two decimal places as needed. Round the slope to three decimal places as needed.) Identify a characteristic of the data that is ignored by the regression line. O A. There is an influential point that strongly affects the graph of the regression line. O B. The data has a pattern that is not a straight line. O C. There is no characteristic of the data that is ignored by the regression line. O D. There is no trend in the data. Activate Windows Statcrunch P Type here to search 0 日口 C hp esc f3 f4 fs f6 f7 f8 12 IOI 144 inser & 2. 3 4. 5. E R tab 00 輯 T %24 米 %23

Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 11 10 y 7.11 12 8. 6.44 8. 12 13 4 12.71 7.37 7.82 8.82 6.14 5.4 7.93 6.52 5.94 25 25 25 25- 20 20- 20 20- 15 151 15- 15: 10- 105 10- 10- 5. 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Find the equation of the regression line. (Round the y-intercept two decimal places as needed. Round the slope to three decimal places as needed.) Identify a characteristic of the data that is ignored by the regression line. O A. There is an influential point that strongly affects the graph of the regression line. O B. The data has a pattern that is not a straight line. O C. There is no characteristic of the data that is ignored by the regression line. O D. There is no trend in the data. Activate Windows Statcrunch P Type here to search 0 日口 C hp esc f3 f4 fs f6 f7 f8 12 IOI 144 inser & 2. 3 4. 5. E R tab 00 輯 T %24 米 %23

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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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?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. Managers rate employees according to job performance and attitude. The results for several randomly selected employees are given below. Performance Attitute59 7263 6765 7869 8258 7577 8776 9269 8370 8764 78 A. y=11.7+1.02x B. y=2.81+1.35x C. y=−47.3+2.02x D. y=92.3−0.669x
The personnel director of a large hospital is interested in determining the relationship (if any) between an employee’s age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 9 10Age 30 50 40 55 30 28 60 25 30 45Sick Days 7 4 3 2 9 10 0 8 5 2 The estimated regression equation and the standard error are given. Sick Days=14.310162−0.236900(Age) Se=1.682207 Find the 95% prediction interval for the average number of sick days an employee will take per year, given the employee is 34 . Round your answer to two decimal places.
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. x 8 12 7 3 6 13 11 10 5 4 9 y 9.58 9.54 8.70 1.64 7.46 8.64 10.08 10.26 5.88 3.94 10.10 y=_+_x (Round to two decimal places as needed.)
A set of n = 15 pairs of X and Y values has a correlation of r = +0.80 with SSY = 75, and the regression equation for predicting Y is computed. Find the standard error of estimate for the regression equation. How big would the standard error be if the sample size were n = 30.
41. Which of the following is the multiple regression model for the data? (a) y = -0.11050 + 2.10797x1 + 0.40717x2 (b) y = -0.11050 + 0.40717x1 + 2.10797x2 (c) y = 0.23563 + 0.00062x1 + 0.23626x2 (d) none (e) y = -0.44187 + 2.42120x1 + 0.36135x2
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. x 6 5 11 4 9 10 7 12 8 3 13 y 7.16 5.68 9.28 3.84 9.50 9.56 8.30 8.64 9.08 1.64 7.64
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. x 7 4 12 3 6 10 8 13 11 5 9 y 14.61 7.56 18.76 4.45 12.64 18.24 16.21 18.45 18.70 10.30 17.41 y^=???+1.41.4x (Round to two decimal places as needed.)
In order to determine a realistic price for a new product that a company wants to market the company’s research department selected 10 sites thought to have essentially identical sales potential and offered the product in each at a different price. The resulting sales are recorded in the accompanying table: Price ($) Sales ($1,000s) 15.00 15 15.50 14 16.00 16 16.50 9 17.00 12 17.50 10 18.00 8 18.50 9 19.00 6 19.50 5 h). Estimate the slope of the actual equation of the regression line using a 95% confidence interval and interpret this interval.
In order to determine a realistic price for a new product that a company wants to market the company’s research department selected 10 sites thought to have essentially identical sales potential and offered the product in each at a different price. The resulting sales are recorded in the accompanying table: Price ($) Sales ($1,000s) 15.00 15 15.50 14 16.00 16 16.50 9 17.00 12 17.50 10 18.00 8 18.50 9 19.00 6 19.50 5 h). Estimate the slope of the actual equation of the regression line using a 95% confidence interval and interpret this interval using Minitab.
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line.x y10 7.469 6.8311 13.1110 6.8111 7.5813 8.787 6.164 5.1612 7.846 6.656 5.52