1. The table shows the distances, in meters, that each player in a game tossed a ball, and the total number of earned points each player made for those tosses.
|
Distance (m) |
6.5 |
6 |
5 |
8.5 |
2 |
5.5 |
6.5 |
8 |
3 |
6 |
4.5 |
6 |
|
Total earned points |
15 |
15 |
22 |
21 |
9 |
8 |
14 |
14 |
19 |
27 |
14 |
16 |
(a) Create a scatter plot of the data set. Use the distance for the input variable and the total earned points for the output variable
(b) Are there any clusters or outliers in the data set? If so, identify them.
2. A linear model for the data in the table is shown in the scatter plot.
|
x |
1 |
2 |
4 |
5 |
6 |
7 |
8 |
9 |
|
y |
14 |
12 |
11 |
11 |
9 |
7 |
4 |
3.5 |
(a) Which two points should you use to find the equation of the model? Explain.
(B) Use the two points you chose in Part (a) to find the slope of the linear model, rounded to three decimal places. Show your work.
(c) What is the equation of the linear model in point-slope form?
(D) Rearrange the equation you wrote in Part (c) into slope-intercept form. Show your work.
(3) The data set shows the number of practice throws players in a basketball competition made and the number of free throws they made in a timed competition.
|
4 |
10 |
6 |
15 |
0 |
7 |
11 |
|
|
Free throws |
8 |
23 |
9 |
34 |
5 |
11 |
27 |
- Use technology to find the equation and coefficient of determination for each type of regression model. Use the number of practice throws for the input variable and the number of free throws for the output variable. Round all decimal values to three places.
- Which model best fits the data set? Explain.
|
Model |
Equation of regression model |
Coefficient of determination |
|
Linear |
||
|
Quadratic |
||
|
Exponential (standard form) |
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