Answer Happy

Homework and Quizzes ~ An online statistics professor recently collected grades of 31 students from a homework assignment and a quiz covering the same material. A scatterplot of this data are given below. Grades on Homework and Quizzes 80 100 120 Homework Grade and the response variable is 100 06 80 Quiz Grade 70 60 50 40 1. The explanatory variable is ??? ??? T 60
2. The relationship between Homework Grade and Quiz Grade is best described as ??? A linear regression model is fitted to the data using the statistical software, R. A summary of that model fit is given below: Coefficients Estimate Std. Error 11.81 t value 0.695 Pr(> [t|) 0.492 Intercept 8.212 Homework 0.7948 0.1382 5.752 3.15e-06 Residual standard error: 10.83 on 29 degrees of freedom Multiple R-squared: 0.5329, Adjusted R-squared: 0.5168 3. Which statement about the coefficient of determination is true? O A. About 51.68% of the variability in homework grades is explained by the linear association with quiz grades. The remaining variation is due to chance. O B. About 73% of variability in quiz grades is explained by the linear association with homework grades. The remaining variation is due to chance. OC. On average about 73% of quiz grades come from homework. O D. About 53.29% of the variability in quiz grades is explained by the linear association with homework grades. The remaining variation is due to chance. 4. Use the information in the software output to write the equation for the regression line for this model: y = + X 5. Use the information given in the software output to find a 90% confidence interval for the true slope of the linear equation representing the relationship between Homework Grade and Quiz Grade. ) "
6. What are the test statistic and the p value for the test of Ho: P₁ = 0 versus Ha: B₁ ‡ 0? Test Statistic: and p value: 7. Below is the residual plot for the data. Interpret the plot Grades Residual Plot 70 80 Fitted Values Residuals 10 0 -10 -20 50 60 90 100
O A. The plot presents a pattern. The residuals are not scattered around zero. A linear model is appropriate for the data. OB. The plot presents a pattern. The residuals are scattered around zero. A linear model is not appropriate for the data. OC. The plot does not present any pattern. The residuals are scattered around zero. A linear model is appropriate for the data. OD. The plot does not present any pattern. The residuals are scattered around zero. A linear model is not appropriate for the data. 8. When using the homework grade to predict a student's quiz grade, a student would prefer to have a O A. Negative residual because that means the student's quiz grade is higher than what was predicted with the model. OB. Negative residual because that means the student's quiz grade is lower than what was predicted with the model. O C. Positive residual because that means the student's quiz grade is better than what was predicted with the model. O D. Positive residual because that means the student's quiz grade is lower than what was predicted with the model. O E. Residual equal to zero because that means the student's grade is exactly predicted with the model. Another professor, Dr. Gonzalez, made similar observations about the relationship between the grades on the homework and quiz that he assigned in his class. His regression line follows the equation: y = 18.5642 +0.5970 x. 9. Predict a quiz grade for a student who scored 77.1345% on the homework in Dr. Gonzalez's class.