Question

Back in the day, in a class of 25 students, a statistics professor created a linear...

Back in the day, in a class of 25 students, a statistics professor created a linear regression to predict a student’s overall points earned on a test (y) from their score on one particular problem (x). She found that the regression line, framed as we usually do in class, was given by

y = 48.208 + 2.518(x − 8.625)

and that the sum of the squares of the differences of the actual values of y and the values predicted by the regression line was 1664.175. Use this information to calculate a 95% confidence interval for α.

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Select all the statements that are true of a least-squares regression line. 1. R2 measures how...
Select all the statements that are true of a least-squares regression line. 1. R2 measures how much of the variation in Y is explained by X in the estimated linear regression. 2.The regression line maximizes the residuals between the observed values and the predicted values. 3.The slope of the regression line is resistant to outliers. 4.The sum of the squares of the residuals is the smallest sum possible. 5.In the equation of the least-squares regression line, Y^ is a predicted...
In a statistics course, a linear regression equation was computed to predict the final-test score from...
In a statistics course, a linear regression equation was computed to predict the final-test score from the score on the first quiz. The resulting equation was: ˆy = 25 + 0.84x with a correlation of 0.73 where y is the final test score and x is the score on the first quiz. (a) If Carla scored 82 points on her first quiz, what is the predicted value of her score on the final test? (b) The linear regression line predicted...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in an introductory statistics class took a pretest at the beginning of the semester. The least-squares regression line for predicting the score y on the final exam from the pretest score x was ŷ = 9.6 + 0.77x. The standard error of b1 was 0.42. (a) Test the null hypothesis that there is no linear relationship between the pretest score and the score on the...
Suppose we have collected the midterm and final scores of 200 students in a Statistics class,...
Suppose we have collected the midterm and final scores of 200 students in a Statistics class, and we observed that the scatter plot of final scores versus midterm scores is roughly linear. Suppose the average midterm score is 76, with SD 12, and the average final score is 71, with SD 10. Suppose the correlation coefficient between midterm and final scores is 0.6. Use the regression line, or an equivalent regression procedure, to predict the final score for someone who...
A business statistics professor would like to develop a regression model to predict the final exam...
A business statistics professor would like to develop a regression model to predict the final exam scores for students based on their current GPAs, the number of hours they studied for the exam, the number of times they were absent during the semester, and their genders. The data for these variables are given in the accompanying table. Complete parts a through d below. Score   GPA   Hours   Absences   Gender 87   3.75   2.0   0   Female 77   3.20   4.5   3   Male 82   3.16  ...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in an introductory statistics class took a pretest at the beginning of the semester. The least-squares regression line for predicting the score y on the final exam from the pretest score x was ŷ = 9.1 + 0.79x. The standard error of b1 was 0.41. a). Test the null hypothesis that there is no linear relationship between the pretest score and the score on the...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in an introductory statistics class took a pretest at the beginning of the semester. The least-squares regression line for predicting the score y on the final exam from the pretest score x was ŷ = 9.9 + 0.77x. The standard error of b1 was 0.44. (a) Test the null hypothesis that there is no linear relationship between the pretest score and the score on the...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in...
Can a pretest on mathematics skills predict success in a statistics course? The 82 students in an introductory statistics class took a pretest at the beginning of the semester. The least-squares regression line for predicting the score y on the final exam from the pretest score x was ŷ = 9.9 + 0.78x. The standard error of b1 was 0.44. (a) Test the null hypothesis that there is no linear relationship between the pretest score and the score on the...
A Professor of MAT120 class wants to see if number of absences reflects the score on...
A Professor of MAT120 class wants to see if number of absences reflects the score on the student’s test. He randomly selected 10 students from a class and summarized the data below. Correlations number of absence score out of 100 number of absences Pearson Correlation 1 -.885** Sig. (2-tailed) .001 N 10 10 score out of 100 Pearson Correlation -.885** 1 Sig. (2-tailed) .001 N 10 10 **. Correlation is significant at the 0.01 level (2-tailed). Coefficientsa Model Unstandardized Coefficients...
The table below gives the number of absences and the overall grade in the class for...
The table below gives the number of absences and the overall grade in the class for five randomly selected students. Based on this data, consider the equation of the regression line, yˆ=b0+b1x, for using the number of absences to predict a student's overall grade in the class. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT