3.3 residuals, influential, regression outlier

3.3 residuals, influential, regression outlier - equation...

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AP Statistics Date ___________________ 3.3 Residuals, Influential Observations and Regression Outliers Most parents are very excited to hear their children speak their first words. Does the early talker have an advantage over the late bloomer? A study of the development of young children recorded the age in months at which each of 21 children spoke their first word and their Gesell Adaptive Score, the result of an aptitude test taken much later. Researchers were interested in examining whether the age at which a child begins to talk can predict their later score on a test of mental ability. The data appears below. Subject Age, first word Gesell Score Residual Subject Age, first word Gesell Score Residual 1 15 95 12 9 96 2 26 71 13 10 83 3 10 83 14 11 84 4 9 91 15 11 102 5 15 102 16 10 100 6 20 87 17 12 105 7 18 93 18 42 57 8 11 100 19 17 121 9 8 104 20 11 86 10 20 94 21 10 100 11 7 113 1. Enter the data into your calculator. Make a scatter plot of the data. Sketch the scatter plot below. 2. Calculate, using your calculator, the LSRL equation. Write it below. Move the LSRL
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Unformatted text preview: equation into Y1 then superimpose it on your scatter plot. 3. What is the predicted score for a child who first spoke at 15 months? 4. What is the actual (observed) score for a child who first spoke at 15 months? 5. Calculate the residual for Child #1showing your work in the space below. 6. Use your calculator to determine the residuals for all 21 children. Write them in the column indicated in the data table. 7. Make a residual plot (a scatter plot of the explanatory variable against the regression residuals) on your calculator. Sketch the plot in the space below. 8. Interpret the residual plot in a clear, concise sentence. Is the LSRL equation a good model for the data? 9. Are there any outliers? If so, which child(ren)? 10. Are there any influential observations? If so, which child(ren)?...
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