0.12 Ch 12: linear regression and correlation

Have the students plot a line by eye using the following data. The independent variable $x$ represents the size of a color television screen in inches at Anderson's and $\begin{array}{}y\\ \end{array}$ represents the sales price in dollars.

Ask them what they got for the slope and for the y-intercept. Make comparisons. This exercise should point out how difficult it is to get an accurate line of best fit and how many lines "seem" to fit the data. (This data is taken from the exercises.)

Have the students follow the "outlier" example in the text and (just once!) do the calculations for finding an outlier. Have them fill in the table below.

Find: ${\sum }_7(\mid y-\text{yhat}\mid {)}^2=\text{SSE}$

Find $s=\sqrt{\frac{\text{SSE}}{n-2}}$

$n=$ the total number of data values (7 for this problem)

$s$ is the standard deviation of the $\mid y-\text{yhat}\mid$ values

Multiply $s$ by 1.9: $(1\text{.}9)(s)=$ _______

Compare each $\mid y-\text{yhat}\mid$ to $(1\text{.}9)(s)$ .

If any $\mid y-\text{yhat}\mid$ is at least $(1\text{.}9)(s)$ , then the corresponding point is an outlier. (None of the points is an outlier.)