2. LSRLs: Interpreting r vs. r 2 r – “the correlation coefficient” tells you the strength and direction between two variables (x and y, for example, height v. weight, r = ?). -1 ≤ r ≤ 1 r will not work for non-linear relationships r does not have units (r ≠.30 pounds?) r is not resistant to outliers! Consider the effect of outliers when looking at r, report r with outliers and without r is same regardless of which is explanatory and which is response variable

3. Understanding what is expected with LSRLs Note: When finding LSRL the placement of the explanatory and response variables DOES matter! Y_hat = _x + _ (prediction equation, equation of line of best fit) Found by minimizing sum of squares of residuals *extra credit for manual calculation from packet 1. Find LSRL using calculator: stat->calc->8 resulted in the output for packet examples, y_hat = a + bx (#8 2. Find LSRL using computer output. 3. Find LSRL using b= r s y /s x. (You are not given data, you are given statistics: s y, s x, x_bar, y_bar, and r.) Find b., Substitute into y_bar = ax_bar + b. Solve for a. Substitute a and b into y_hat = a + bx and you are done.

4. Simple understanding: r v r 2 r, correlation coefficient (strength and direction, only about relationship between x and y, r is related to the slope of LSRL – b = rs y /s x ) R 2, coefficient of determination (how strong=accurate is our LSRL?) 11.80.64.50.25.20.04 00 1

5. Examining LSRLs: r v. r 2 Students height v. weight y_hat = 4.915x -157.613 predicted weight = 4.915(height) -157.613 r = r 2 =

6. To answer the question in your packet, which is the better prediction equation (which would be more accurate in making a prediction)? The one with the highest r 2 value! The higher the value, the more % of variation in y is explained by the LSRL of y on x.

7. Theory behind r 2 It tells us how much better a line with a slope would be at predicting than a line of y=y_bar (the average of all of the y values in the data set). It compares the vertical deviations (residuals) between the sloped line and the horizontal line (y=y_bar) and tells how much better the sloped line is in accounting for this variation. This math and theory can be found in the book You don’t have to know the theory for AP Test or my test.

8. What You Should Know: Summary of r 2 r 2 tells us how accurate our LSRL is at making predictions. Do you think the x value in each observation tells you something about y? How much is it actually telling you? When r 2 = 1 we say “100% of the variation in y is explained by the LSRL of y on x. When r 2 =.64, we say “64% of the variation in y is explained by the LSRL of y on x. r 2 tells us the fractional variation in y that is explained by the LSRL of y on x. MUST USE THIS SPECIFIC LANGUAGE TO INTERPRET r2 ON THE AP TEST AND MY TEST!!!

9. What is a residual? The vertical deviation from y to y_hat from each observation to the LSRL (y_hat). The residual values (the vertical deviations) are stored in your calculator each time you run a linear regression LinReg a+bx. These residuals can be found in RESID in your calculator 2 nd ->Stat->RESID

10. What do the residuals tell us? The residuals tell us whether a line is a best fit (maybe a non-linear function, exponential or power, might fit the data better and help us predict better). How to create a residual plot: Plot x, the explanatory variable, L1 vs. y=RESIDS. If the plot shows a pattern (not scattered), a line is not a best fit.

11. HW Assignment - TBA For each set of data in your packet (4 sets): Enter the data in L1, L2. Run the linear regression to create your LSRL. On a separate sheet of paper, record r and r 2. Also, in two sentences, interpret r and r 2 in context of the data. Create a residual plot (L1 vs. Resids) on your calculator in statplot and sketch it on this separate sheet. On the separate sheet, answer these questions: Is the LSRL (a linear equation) a best fit for this data? How do you know? How accurate is the LSRL? How do you know? Also, complete the last page of the packet for HW.