Briefly respond to the following. (a) Describe in your own words what the Pearson Product-Moment Correlation r measures. (b) Describe in your own words what the Coefficient of Determination R? represents. (c) Explain what the simple linear regression coefficient B1 represents, including its connection to the linear correlation coefficient p.

Briefly respond to the following. (a) Describe in your own words what the Pearson Product-Moment Correlation r measures. (b) Describe in your own words what the Coefficient of Determination R? represents. (c) Explain what the simple linear regression coefficient B1 represents, including its connection to the linear correlation coefficient p.

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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?
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
If a sample of 25 pairs of data yields a correlation coefficient, r, of 0.390 and the scatterplot displays a linear trend, can you use the regression equation to make predictions, assuming your x-values are within the domain of the data set? Choose your answer from the multiple choice answers below A.) Yes, because rcrit = 0.396 and the regression coefficient, r, is less than this value. B.) Yes, because rcrit = 0.381 and the regression coefficient, r, is greater than this value. C.) No, because rcrit = 0.381 and the regression coefficient, r, is greater than this value. D.) No, because rcrit = 0.396 and the regression coefficient, r, is less than this value.
If other factors are held constant and the Pearson correlation value between X and Y is r = 0.80, then the regression equation will tend to produce more accurate predictions than would be obtained if the Pearson correlation value was r = 0.60. Group of answer choices True False
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Which of the following statements is true with respect to a simple linear regression model? The percent of variation in the dependent variable that is explained by the regression model is equal to the square of the correlation coefficient between the x and y variables If the correlation coefficient between the x and y variables is negative, the sign on the regression slope coefficient will also be negative If the correlation between the dependent and the independent variable is determined to be significant, the regression model for y given x will also be significant All of the above are true
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1) Indicate whether the following statements are true or false. Explain why and show your work. c) In the regression Y= B1+ B2X + B3Z+u , if there is a strong linear correlation between X and Z, then it is more likely you fail to reject the null hypotheses that individual slope parameters are insignificant.
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