• Recognize when a linear regression model will fit a given data set.
• Use technology to create scatterplots, find the line of best fit, and find the correlation coefficient.
• Find the estimated slope and [latex]y[/latex]-intercept for a linear regression model.
• Use the line of best fit to predict values.

Midterm vs. Final Exam Score

George, a current student, got a [latex]36[/latex] out of [latex]50[/latex] on the first midterm (C-). He asked his instructor, “If I don’t change my study approach, how do you predict I will do on the final exam?”

One way to answer this question is to look at the bivariate data of student scores from a previous class. In this case, we choose a random sample of past students who did not seek out additional tutoring and/or support between the midterm and the final.

The following is a data set from a random sample of past students who did not seek out advice on study skills or additional tutoring between the midterm and the final exam. To protect their anonymity, only first names are shown.

Student First Name	Midterm Score (out of [latex]50[/latex] points)	Final Exam Score (out of [latex]100[/latex] points)
Joe	42	64
Barak	52	94
Hillary	44	87
Donald	25	46
Cher	41	73
Katy	39	73
Taylor	33	53
Miley	40	77
Justin	35	60
Snoop	31	62
Bruno	37	71
Kanye	49	95
Leonardo	38	70
Rosie	45	80
Maya	49	80
Tyra	48	82
Selena	50	81

Steps to create the LSR:

Step 1: Under Enter Data, select Enter Own.

Step 2: Name the X (explanatory) and Y (response) variables appropriately.

Step 3: Copy and paste the data set.

Dataset (name removed)

Midterm Score (out of [latex]50[/latex] points)	Final Exam Score (out of [latex]100[/latex] points)
42	64
52	94
44	87
25	46
41	73
39	73
33	53
40	77
35	60
31	62
37	71
49	95
38	70
45	80
49	80
48	82
50	81

Step 4: Under Plot Options, select Regression Line, and click the Submit Data button.

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