answer the following using the information (part 2) a. what is the slope of the regression line? b. what is the y intercept of the regression line? c. sketch the residual plot L1 - 4,15,12,11,8,6,7,2,7,14,20,3,13 L2 - 120,200,140,110,120,80,190,100,120,190,190,110,120

Describe the explained variation about a regression line in words and in symbols. Choose the correct answer below. O A. The explained variation is the sum of the squares of the differences between the predicted y-values and the mean of the y-values of the ordered pairs. (Xị, y;). O B. The explained variation is the sum of the squares of the differences between the observed y-values and the predicted y-values. O c. The explained variation is the sum of the squares of the differences between the y-values of each ordered pair and the mean of the y-values of the ordered pairs. (x, y) The explained variation in symbols can be described by the expression y = ỹ

The figure for this exercise is a scatterplot of y with the following formula. Average math = 575 600 Average math SAT 575. 550 525ドル. 500- - = 1.11(Percent took) average math test score versus x = percent of graduating seniors who took the test for the 50 states. The regression line for these data is expressed 475 10 20 30 40 50 60 70 80 Percent taking test 90 (a) The slope of the equation is -1.11. Interpret this value in the context of how average math test changes when the percent of graduates taking the test changes. Average math test decreases 1.11 points per each 1-percent decrease in the percentage of high school graduates taking the test. Average math test decreases 575 points per each 1-percent increase in the percentage of high school graduates taking the test. Average math test decreases 575 points per each 1-percent decrease in the percentage of high school graduates taking the test. Average math test increases 1.11 points per each 1-percent increase in the percentage of...

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.)

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 following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, y = bo + b1x. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 75 83 76 75 64 69 89 88 84 62 82 72 76 100 Grades on Final 77 82 73 75 81 63

Refer to the data set: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 = 5.

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Fourteen hikers were surveyed at Algonquin Park, and asked for how many days have you been hikingand far did your travel in that time? The equation for the linear regression line is y+3x + 10.3 where x is the number of days and y is the distance travelled. Does the data include an outlier? and if so which point? Number of days hiked 1 1 2 3 3 5 5 6 7 7 9 10 11 12 Distance Traveled (km) 12 17 18 19 21 23 25 23 30 31 37 39 41 52

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.

1 Y 0/1 -2 12 1 3 0 4. -6 Identify the slope for the above data set 2.

The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 69 57 73 90 90 75 66 90 76 77 Grades on Final 84 74 72 98 76 72 60 75 76 60

Interpret the intercept in the context of the problem. Although there are no flights with a distance of zero, try to explain what might cause added time that the intercept represents. Last part of question I need help.