Review Homework

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Review Homework

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Review Homework. Residuals. From the Carnegie Foundation math.mtsac.edu/ statway /lesson_3.3.1_version1.5A. Residual (or error). Observed y MINUS predicted y. Analyzing Residuals. Determines the effectiveness of the regression model. Residual Plots. - PowerPoint PPT Presentation

Transcript of Review Homework

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Review Homework

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Residuals

From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A

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Residual (or error) Observed y MINUS predicted y

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Analyzing ResidualsDetermines the effectiveness of the regression model

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Residual PlotsA scatterplot of Residuals vs. X

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Residual Plots Determine

If it the model is appropriate, then the plot will have a random scatter.

If another model is necessary, the plot will have a pattern. Pattern = Problem

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Example of Random Scatter

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ExamplesDetermine, just by visual inspection, if

the linear model is appropriate or inappropriate.

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Linear model appropriate or inappropriate?

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The only way to know is to see the residual plot.

1. Does their appear to be a pattern in the residual plot?Yes, quadratic.2. Does this support your original guess?

You must now see that a linear model does NOT fit this data.

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Linear model appropriate or inappropriate?

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The only way to know is to see the residual plot.

1. Does their appear to be a pattern in the residual plot?Yes, it fans out as

x increases.2. Does this support your original guess?

You must now see that a linear model does NOT fit this data.

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Linear model appropriate or inappropriate?

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The only way to know is to see the residual plot.

1. Does their appear to be a pattern in the residual plot?Yes, it looks quadratic.

2. Does this support your original guess?

This was very tricky. The scale was very small. You must now see that a linear model does NOT fit this data.

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Linear model appropriate or inappropriate?

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The only way to know is to see the residual plot.

1. Does their appear to be a pattern in the residual plot?Yes, it seems decrease as x increases.2. Does this

support your original guess?This was tricky. You must now see that a linear model does NOT fit this data.

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Example: Calculate ResidualTracking Cell Phone Use over 10 days

Total Time (minutes)

Total Distance (miles

Predicted Total Distance

Residuals(observed – predicted)

32 51 54.4 -3.419 30 31.928 4736 5617 2723 3541 6522 4137 7328 54

1.73 0.96y x

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Example: Calculate ResidualTracking Cell Phone Use over 10 days

Total Time (minutes)

Total Distance (miles

Predicted Total Distance

Residuals(observed – predicted)

32 51 54.4 -3.419 30 31.9 -1.928 47 47.5 -0.536 56 61.3 -5.317 27 28.5 -1.523 35 38.8 -3.841 65 70.0 -522 41 37.1 3.937 73 63.1 9.928 54 47.5 6.5

1.73 0.96y x

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Good fit or not?

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