6A

Student First Name Midterm Score

(out of 50 points)

 Final Exam Score

(out of 100 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
Body mass (g) TOV

(cm per second)
3640 334.5
2670 387.3
5600 410.8
4130 318.6
3020 368.7
2660 358.8
3240 344.6
5140 324.6
3690 301.4
3620 331.8
5310 312.6
5560 316.8
3970 375.6
3770 372.4
5100 314.3
2950 367.5
7930 286.3
Week Kai’s weight
0 173
1 171
2
3
4
5
6
Chirps per second Temperature in degrees Fahrenheit
20 88.6
16 71.6
19.8 93.3
18.4 84.3
17.1 80.6
15.5 75.2
14.7 69.7
17.1 82
15.4 69.4
16.2 83.3
15 79.6
17.2 82.6
16 80.6
17 83.5
14.4 76.3
Skill or Concept: I can . . . Questions to check your understanding Rating
from 1 to 5
Identify when a linear regression analysis might be appropriate. 3, 4, 5 (Part A)
Identify the explanatory and response variables in a given scenario. 1, 2, 5 (Parts B and C)
Calculate the line of best fit and write it using proper notation. 5 (Parts D through F)

Two people smiling and looking at a whiteboard Several scatterplots. Plot A shows points that are in a somewhat linear arrangement, plot B shows points that are close together in a upside-down U-shape, plot C shows points that are close together and flat near the bottom of the graph, then angle upwards, then flatten out again, plot D shows points that are close together in a linear arrangement, plot E shows points that are arranged close together in a curve, plot F shows points that are arranged somewhat close together in a linear fashion, plot G shows points that are arranged somewhat close together in a linear fashion, and plot H shows randomly arranged points. A grid A graph with several points and a line of best fit. Each point is connected to the line of best fit vertically. Beside one of the vertical lines, it reads "Residual = 4 - 10 = -6."

Glossary

Least Squares Regression (LSR) analysis
determining the equation of a line of best fit to make predictions based on an existing dataset, also be described as linear modeling.
residual
a representation of how far off a prediction calculated from the line is compared to the actual, observed 𝑦 value, illustrated by a vertical line; also called vertical error.