The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min Temperature ("F) 945 936 1166 836 80.8 78.9 92 793 1164 O 67 71.6 84,9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is F. (Round to one decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below. O A It is only an approximation. An unrounded value would be considered accurate. OB. It is unrealistically high. The value 3000 is far outside of the range of observed values. OC. The first variable should have been the dependent variable. O D. Nothing is wrong with this value. It can be treated as an accurate prediction.

The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min Temperature ("F) 945 936 1166 836 80.8 78.9 92 793 1164 O 67 71.6 84,9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is F. (Round to one decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below. O A It is only an approximation. An unrounded value would be considered accurate. OB. It is unrealistically high. The value 3000 is far outside of the range of observed values. OC. The first variable should have been the dependent variable. O D. Nothing is wrong with this value. It can be treated as an accurate prediction.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.CR: Chapter Review Exercises

Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...

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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?
If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4