1. Using the two data sets below: 1) Draw a scatterplot for each.
Test whether the correlations are significant. Use 0.01 for α
Give the values for t, p, and r. x y 31
107 68 78 14
125 21
112 60 87 17
124 76 77 49 92 20
118 97 x y
3.552
878
2.701
1162
3.112
1235
2.991
888
3.357
959
2.587
787
2.917
1227
3.649
835

2. Using the two data sets below: 1) Draw a scatterplot for each.x y
3.552
878
2.701
1162
3.112
1235
2.991
888
3.357
959
2.587
787
2.917
1227
3.649
835
I set my Window to:
x-min: 0 y-min: 750
x-max: 5 y-max: 1250
If you had a different window, your graph may look a little different.

3. Using the two data sets below:
Test whether the correlations are significant. Use 0.01 for α
Give the values for t, p, and r.
𝐻 0 :𝜌=0 𝐻 𝑎 :𝜌≠0
STAT–TEST–F
𝑡=−.713
𝑝=.503
𝑟=.279
Since 𝑝>𝛼, fail to reject the null.
The correlation is NOT significant.

4. Using the two data sets below: 1) Draw a scatterplot for each.x y 31
107 68 78 14
125 21
112 60 87 17
124 76 77 49 92 20
118 97
I set my Window to:
x-min: 10 y-min: 70
x-max: 80 y-max: 130
If you had a different window, your graph may look a little different.

5. Using the two data sets below: 1) Draw a scatterplot for each.
Test whether the correlations are significant. Use 0.01 for α
Give the values for t, p, and r. x y 31
107 68 78 14
125 21
112 60 87 17
124 76 77 49 92 20
118 97
𝐻 0 :𝜌=0 𝐻 𝑎 :𝜌≠0
STAT–TEST–F
𝑡=−10.447
𝑝=6.119𝐸−6
𝑟=.965
Since 𝑝≤𝛼, Reject the null.
The correlation is significant.

6. Section 9-2 – Equation of Best Fit for Linear Regression
The only thing in Section 9-2 that is new is to use the equation of the line of best fit to make predictions about y-values.
You can only use the equation to make predictions if the correlation is significant!!
That's why we ran the tests in 9-1 to determine whether the correlation is significant or not.
When you run STAT-TEST-F, you get the equation of the line of best fit, too.
Using the data from Example 7 in 9-1, we got the following:
𝑦=𝑎+𝑏𝑥; 𝑎= , and 𝑏=50.729, so the equation is
𝑦=50.729𝑥

7. Section 9-2 – Equation of Best Fit for Linear Regression
Example 3 - (Page 516)
Use the equation of best fit from Example 7 in 9-1 to predict the expected company sales for the following advertising expenses.
a) 1.5 thousand b) 1.8 thousand c) 2.5 thousand
Remember, we have already determined that the correlation is significant, so this equation can be used for making predictions.
1) Plug each value of x into the equation to find the y-value prediction.
𝑦= ≈ , 𝑜𝑟 $180,155
𝑦= ≈ , 𝑜𝑟 $195,373
𝑦= ≈ , 𝑜𝑟 $230,884
2) Enter the equation into y = ( 𝑦=50.729𝑥 )
Use 2nd Window to set beginning of table, then use 2nd Graph to see the y-value for each x.

8. Assignments:
Classwork: Pages # 9-12 All
Homework: Pages #14-28 Evens
Run the hypothesis test to prove significance, and then make predictions if possible.
QUIZ on 9-1 and 9-2 next class!!