Use the following ANOVA table for regression to answer the questions. Response: Y Source DF Sum Sq Mean Sq F-value Pr(>F) Regression 1 353.00 353.00 11.40 0.001 Residual Error 359 11114.64 30.96 Total 360 11467.64 Give the F-statistic and p-value. Enter the exact answers. The F-statistic is i The p-value is i

r = -0.579 Calculate the coefficient of determination. (Round to three decimal places as needed.) What does this tell you about the explained variation of the data about the regression line? % of the variation can be explained by the regression line. (Round to one decimal place as needed.) About the unexplained variation? % of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)

Which of the following tools is not appropriate for studying the relationship between two numerica variables? Correlation coefficient Scatter plot Historam Regression

Please fill in the blanks and select the correct answers over the image provided. Thank you

Interpret the estimated R2 Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041?  Justify your response.

Please do only the last three

Find the indicated​ z-score shown in the graph to the right.                          z Area= 0.0708 z=? 0         A normal curve is over a horizontal z-axis and is centered on 0. A vertical line segment extends from the curve to the horizontal axis at a point labeled z = ?. The area under the curve and to the left of the vertical line segment is shaded and labeled Area = 0.0708.       The​ z-score is enter your response here. ​(Round to two decimal places as​ needed.)

An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:.   Summary measures       Multiple R 0.9689       R-Square 0.9387       Adj R-Square 0.9212       StErr of Estimate 72.218                 Regression coefficients         Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.