Suppose in this semester, our Exam 1 average was about 86 with an SD of about 10. Suppose the correlation between our Exam 1 and Exam 2 scores will be similar to what it has been in the past, about 0.6, and finally, suppose our Exam 2 scores will be similar to previous semesters' Exam 2 scores with an average of 76 and a SD of 8.2. Use this information to answer the following questions: 1. What is the slope of the regression equation for predicting our Exam 2 scores from Exam 1 scores? Round to 3 decimal places.
2. What is the y-intercept of the regression equation for predicting our Exam 2 scores from Exam 1 scores? Round to 3 decimal places.

the slope of the regression equation for predicting our Exam 2 scores from Exam 1 scores is 0.492

And the y-intercept of the regression equation for predicting our Exam 2 scores from Exam 1 is 33.688

Step-by-step explanation:

Given the data in the question;  

mean X" = 86

SD σx = 10

Y" = 76

SD σy = 8.2

r = 0.6

Here, Exam 2 is dependent and Exam 1 is independent.

The Regression equation is

y - Y" = r × σy/σx  ( x - x" )

we substitute

y - 76 = 0.6 × 8.2/10  ( x - 86 )

y  - 76 = 0.492( x - 86 )

y  - 76 = 0.492x - 42.312

y = 0.492x - 42.312 + 76

y = 0.492x + 33.688

Hence, the slope of the regression equation for predicting our Exam 2 scores from Exam 1 scores is 0.492

And the y-intercept of the regression equation for predicting our Exam 2 scores from Exam 1 is 33.688

22 and 22

step-by-step explanation:

10 plus 12 equals 22.

12 plus 10 also equals 22 because regardless of what order they're in, they're gonna equal the same thing.

175

step-by-step explanation:

2 inches x 10 miles = 200 miles - 3 inches = 175