Determine the equation for the line of best fit for this data set and its correlation coefficient r .  You may use Desmos or your graphing calculator to determine this linear regression.  Round your slope to the nearest tenth and the y-intercept to the nearest whole number.

a) Determine a linear regression equation for the data.  Round the slope and y-intercept to the nearest whole number. b) Find the correlation coefficient, r-value, of the line.  Round your answer to the nearest thousandth. c) Predict the distance the driver would have traveled after 11 hours.  Round your answer to the nearest whole number.

Refer to the data in the table: x y -3 0 -2 0 -1 1 0 1 1 3 2 4 Part a: Make a scatter plot and determine which type of model best fits the data. Part b: Find the regression equation. Part c: Use the equation from Part b to determine y when x = 25.

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 (b) x = 10 wins (c) x = 19 wins (d) x = 15 wins E Click the icon to view the table of numbers of wins and earned run average. The equation of the regression line is y =x+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. OB. OC. OD. AERA 6- AERA AERA AERA 2- 2- 2- 0- 0- 12 18 24 12 18 24 12 18 24 12 18 24 Wins Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice below…

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 (b) x = 10 wins (c) x = 19 wins (d) x = 15 wins Click the icon to view the table of numbers of wins and earned run average. The equation of the regression line is y = x+. (Round to two decimal places as needed.)

please assist, the question states to "click on the datafile logo to reference the data" but the data is listed already in the question. when clicking that link, it opens up the same information in excel so please do not reject because the data is there already.

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 (c) x = 19 wins (d) x = 15 wins (b)x= 10 wins Click the icon to view the table of numbers of wins and earned run average. .. The equation of the regression line is y=x+ X+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. B. O C. O D. AERA AERA 6+ Q AERA 6+ 4- 4- 2- 2- 0- 6 12 18 24 0 12 18 24 6 12 18 24 Wins 12 18 24 Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct…

The chart below shows the correlation between degrees Fahrenheit and degrees Celsius. Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine ºC when ºF = 47.Part d: Does this answer seem reasonable? Explain.

Define the different ways to use linear regression?

Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 7.