An observation that falls within the range of the other X values but which lies far above or below the regression line and hence produces a large residual is which of the following? Onfuential observation Estrapolaton Outier OResidual Deviator

R2

When examining a plot of the residuals produced by the regression model, which of the following statements are true The residuals should be both positive and negative values. They should expand outward, producing a conical shape as your predicted y value increases in size. The residuals should show a clear positive relationship. Low values of your independent variable should produce negative residuals, while high values of your independent variable should produce positive residuals. The residuals should produce a clear u-shaped patter. All values should be positive. The residuals should appear to be random, with a horizontal band around the x- axis. They should be both positive and negative values.

Which of the following is a true attribute of a regression line? A. A regression line is a tool for making predictions about future observed values for center variables B. A regression line reduces a linear relationship to its bare essential. C. A regression line allows us to analyze a relationship without being distracted by small details. D. all of the above

From the results shown above, write the regression equation.

A residual plot from a simple linear regression analysis is shown to the right. Use the plot to answer the question below. residuals 0 X Which of the following statements regarding the plot is true? OA. The condition that the residuals are normally distributed is not met since not all of the residuals fall on the reference line in the residual plot. OB. The condition that the residuals have constant variation is met since the lines connecting the largest positive residuals and largest negative residuals are parallel. Oc. The condition that the residuals are normally distributed is not met since there is a diamond shape to the residuals in the residual plot. OD. The condition that the residuals have constant variation is not met since the variation increases and then decreases as x gets larger.

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.

Consider the following hypothetical regression:   FRIES = 22.5 + 0.08*TRAFFIC + 9.1*COUPON? + -1.1*TEMP where FRIES is the number of pounds of fries a restaurant sells in a week, TRAFFIC is the number of people who walked by the restaurant that week (foot traffic), COUPON? is a dummy variable of if the restaurant offered a coupon or not that week (1=coupon, 0=no coupon); and TEMP is the average high that week, measured in Fahrenheit. All variables are statistically significant. If the average high is expected to be 4 degrees warmer next week, how should FRIES change? 1   Increase by 18.1 pounds 2   Decrease by 4.4 pounds 3   It is impossible to tell without knowing the values of TRAFFIC and COUPON?. 4   Increase by 4.4 pounds 5   Increase by 26.9 pounds

3. Regression analysis breaks scores on the DV into... (explain and give equations)

In this section we introduced a descriptive measure of the utility of the multiple linear regression equation for making predictions.a. Dene and interpret this descriptive measure.b. Identify the symbol used for this descriptive measure.