| << Chapter < Page | Chapter >> Page > |
Recall the third exam/final exam example .
We examined the scatterplot and showed that the correlation coefficient is significant. We found the equation of the best fit line for the final exam grade as a function of the grade on the third exam. We can now use the least squares regression line for prediction.
Suppose you want to estimate, or predict, the final exam score of statistics students who received 73 on the third exam. The exam scores ( -values) range from 65 to 75. Since 73 is between the -values 65 and 75 , substitute into the equation. Then:
We predict that statistic students who earn a grade of 73 on the third exam will earn a grade of 179.08 on the final exam, on average.
Recall the third exam/final exam example .
What would you predict the final exam score to be for a student who scored a 66 on the third exam?
145.27
What would you predict the final exam score to be for a student who scored a 90 on the third exam?
The x values in the data are between 65 and 75. 90 is outside of the domain of the observed x values in the data (independent variable), so you cannot reliably predict the final exam score for this student. (Even though it is possible to enter x into the equation and calculate a y value, you should not do so!)
To really understand how unreliable the prediction can be outside of the observed x values in the data, make the substitution x = 90 into the equation.
The final exam score is predicted to be 261.19. The largest the final exam score can be is 200.
**With contributions from Roberta Bloom
Notification Switch
Would you like to follow the 'Collaborative statistics using spreadsheets' conversation and receive update notifications?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|