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# DAILY GRADE #6 --- 20 minutes Test Ch 1 & 2 Mon THQ#2 Due Thurs - PowerPoint PPT Presentation

DAILY GRADE #6 --- 20 minutes Test Ch 1 & 2 Mon THQ#2 Due Thurs. AP STAT Section 3.2: Least Squares Regression Part 3: Interpreting Residual Plots. EQ: How do you use Residual Plots to assess how well a LSRL fits a data set?.

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Section 3.2: Least Squares Regression

Part 3: Interpreting Residual Plots

EQ: How do you use Residual Plots to assess how well a LSRL fits a data set?

---scatterplot of the regression residuals against the predicted value; assess how well a LSRL fits

Residual Plots

LINEAR

ASSOCIATION:

No Pattern

Evident

Pattern

Evident

NONLINEAR

ASSOCIATION:

Ex 1: No pattern in the residual plot, linear association appropriate

Ex 2: No pattern in the residual plot, linear association appropriate

Ex 3: Patternin the residual plot, linear association not appropriate.

Ex 3: Patternin the residual plot, linear association not appropriate.

Ex 3: Patternin the residual plot, linear association not appropriate.

Ex 3: Patternin the residual plot, linear association not appropriate.

Use your graphing calculator to create a residual plot using NEA and FAT.

• To make sure the LAST regression equation your calculator found was for NEA vs FAT, recalculate the scatterplot and the LSRL forNEA vs FAT.

• Now the residualsfor this plot are stored in a list called RESID.

Use your graphing calculator to create a residual plot using NEA and FAT.

• Go to STATPLOT and cut on PLOT1. Select the first graph.

• Choose NEA as Xlist and RESID as Ylist.

Use your graphing calculator to create a residual plot using NEA and FAT.

• RESID is the list of the LAST RESIDUALS your calculator created.

• ZOOM9 Compare to Residual Plot on p. 219.

LSRL

RESIDUAL PLOT

Go back to WS "Calculating Regression Lines“. Answer the question in Part 3.

REMEMBER: You must recalculate the LSRL for this data because RESID contains the residuals from NEA and FAT.

MUST HIT Points When Deciding Upon a Linear Association:

[DON'T RELY ON JUST ONE]

1. Observe Scatterplot for Linearity

2. State Correlation Coefficient --- strong

vs weak

3. State Coefficient of Determination ---

strong vs weak

4. Observe Residual Plot --- pattern

(nonlinear) vs no pattern (linear)

pp. 220 - 222 #39, 40, 42

pp. 227 - 228 #43, 44, 47, 48

pp. 230 - 233 #49 - 51, 53, 55