6. Compute the two regression equations on the basis of the following information:YMean4045Standard deviation109.Karl Pearson's correlation coefficient between X and Y 0.50Also, estimate the value of Y for X= 48 using the appropriate regression equation.

Introduction - Regression equation y=a+bxb=r.sysxa=y-bxwhere , y=Mean of y values x=mean of x -…

Answered: 6. Compute the two regression equations…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4PPS

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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
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, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.
For the following exercises, consider the data in Table 5, which shows the percent of unemployed ina city of people 25 years or older who are college graduates is given below, by year. 40. Based on the set of data given in Table 6, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient to three decimal places.
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, ŷ = bỏ + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.01 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations 23 39 9 Average Temperature (°F) 36 29 Snow Accumulation (in.) 5 13 21 38 24 35 22 25 37 13 21 23 13 12 9 Copy Data
2. What is the use of the regression line? C. A student conducted a regression analysis between the math grades of his classmates and the number of times they were absent in the subject. He found that the regression line that will predict the grade (y) if the number of absences (x) is known =97.732 2.61x. ances? absences? 457 conces? 1533 ? 4. What is the predicted grade of a student who has 1 absence? 5. What is the predicted grade of a student who has 0 absences?
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, y = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.01 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations 32 22 44 Average Temperature (°F) 44 16 30 43 43 25 35 10 26 26 Snow Accumulation (in.) 8 18 29 9 19 16 7
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, ŷ = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperature (°F) Average Temperatures and Snow Accumulations 42 31 24 45 38 18 33 21 25 9 12 27 7 15 22 30 13 20 37 Snow Accumulation (in.) 8
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line,ŷ = bo+bix. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.05 level of significance. Critical Values of the Pearson Correlation Coefficient Answer Average Temperatures and Snow Accumulations Average Temperature (F) 35 28 15 43 42 23 31 23 28 44 Snow Accumulation (in.) 9 13 22 6 12 28 24 18 17 8 Regression equation: 9 W Is the equation appropriate? Yes No Tables Copy Data Keypad Keyboard Shortcuts
'1) Interpret the following regression line y = 10.50 – 0.18x 2) Interpret the following coefficient of determination r? = 0.69 3) Interpret the following coefficient of correlation r =-0.83
8. For the following data: a. Find the regression equation for predicting Y from X. b. Calculate the Pearson correlation for these data. Use r and SS, to compute SS standard error of estimate for the equation. and the residual Y 3 3 6. 9. 8 4 3 7 10 9.

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6. Compute the two regression equations on the basis of the following information: Mean 40 45 Standard deviation 10 9. Karl Pearson's correlation coefficient between X and Y 0.50 Also, estimate the value of Y for X 48 using the appropriate regression equation.