If the correlation between X and Y is r = 0.00, then the regression equation, Y = bx + a, will have b = 0. O True O False

The table shows the numbers of new-vehicle sales (in thousands) in the United States for Company A and Company B for 10 years. The equation of the regression line is y = 0.991x + 1,222.81. Complete parts (a) and (b) below. New-vehicle sales (Company A), x New-vehicle sales (Company B), y 4,149 3,923 3,566 3,400 3,266 3,076 2,868 2,485 1,952 2,066 4,912 4,871 4,827 4,721 4,672 4,474 4,684 3,822 2,956 2,754 (a) Find the coefficient of determination and interpret the result. r² = r2 = 0.821 (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? The coefficient of determination is the fraction of the variation in new-vehicle sales for Company B that can be 2 explained by the variation in new-vehicle sales for Company A and is represented by The remaining fraction of the variation, 1-2, is unexplained and is due to other factors or to sampling error. s (b) Find the standard error of estimates and interpret the result. Se O (Round to three decimal…

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1 2.5 3 4 5 5.5 Overall Grades 98 95 90 79 75 69 66   Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.   Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.   Step 3 of 6: Determine if the statement "Not all points predicted by the linear model fall on the…

The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins Click the icon to view the table of numbers of wins and earned run average. (b) x = 10 wins (c) x = 21 wins (d) x = 15 wins ERA 6- ERA 6- AERA 6- ERA 6- 4- 4- 4- 4- 2- 2- 2- 2- 0+ 6 0- 0- 0- 12 18 24 6. 12 18 24 12 18 24 6 12 18 24 Wins Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice below and, if necessary, fill in the answer box within your choice. A. ŷ= (Round to two decimal places as needed.) B. It is not meaningful to predict this value of y because…

If a correlation isr= 0.00, then SP = 0 and the regression equation is O Ý =X +0 O Ý =X + X O Ý =0 + X O Ý =Ÿ + 0

State whether the slope of a simple linear regression line is statistically significant, then the correlation will also always be significant?

If the Pearson correlation between X and Y is negative, then the regression equation will have a negative slope. true or false

Identify the correct statement concerning, r the correlation coefficie between X and Y and the linear regression, Y = aX+b. A. Both can be used to show cause and effect. B. Both quantify the strength of relationship between X and Y. C. X and Y are interchangeable in both relationships. D. Both can be used to make predictions.

What is the difference between the following two regression​ equations?                                                  y=b0+b1x                                                                          y=B0+B1x

The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 1 1.5 2 2.5 3 3.5 4.5 Midterm Grades 61 62 75 77 79 83 88 Table   Step 1 of 6 :  Find the estimated slope, y intercept and correlation cofficient. Round your answer to three decimal places.