Correlations and Regression
Height & Weight: Erroneous Data
As we discussed in chapter 10, sometimes an outlier can make or break a correlation. Data from 11 people regarding height and weight is given in the table below.
|
x = height in inches |
y = weight inpounds |
|
60 |
120 |
|
72 |
200 |
|
64 |
130 |
|
71 |
205 |
|
68 |
180 |
|
69 |
180 |
|
69 |
193 |
|
70 |
195 |
|
62 |
115 |
|
62 |
140 |
|
5.5 |
160 |
Question 3:
Part A
- Use software to determine the regression equation for predicting weight from height, using α = 0.05.
- What is the regression equation? Make sure you show your work and/or copy and paste your output into the body of your post.
- State the software you used to produce the regression equation.
- Clearly identify the values for the slope and the y-intercept.
- What is the expected weight for a person who is 62 inches tall?
Part B
- Use software to calculate the regression equation for predicting weight from height, excluding the last data point corresponding to a height of 5.5 inches (as the entry of 5.5 inches seems to be an error),using α = 0.05.
- What is the regression equation without the last data point? Make sure you show your work and/or copy and paste your output into the body of your post.
- Clearly identify the values for the slope and the y-intercept.
- Using the regression equation with the last data point excluded, what is the expected weight for a person who is 62 inches tall?
- Compare your results to those obtained in part A. What can you say about how excluding the outlier affected your results?
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