Practice_Final

# Practice_Final - Practice Final 1. The residual scatter...

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Practice Final 1. The residual scatter plot on the right consists of 104 observations. Then (a) RMSE is around 0. (b) RMSE is around 40 (c) RMSE is around 25. (d) RMSE cannot be estimated from the plot. 2-3. An insurance agent has selected a sample of drivers that she insures whose ages are in the range from 16 to 42 years. For each driver, she records the age of the driver and the dollar amount of claims that the driver filed in the previous 12 months. A scatterplot showing the dollar amount of claims as the response variable and the age as the predictor shows a linear trend. The least squares regression line is determined to be: ݕ ො = 3715 − 75.4ݔ . A plot of the residuals versus age of the drivers showed no pattern, and the following were reported: ݎ = 0.822 , Standard deviation of the residuals ܵ =312.1. 2. Which of the following is correct? (a) If the age of a driver increases from 20 to 21, the dollar amount of claims is predicted to be decreased by \$75.4 (b) If the age of a driver increases by one year, the dollar amount of claims is predicted to be increased by \$3715 (c) One can use the least squares regression line to obtain a reliable prediction of the dollar amount of claims for a driver whose age is 55 years (d) The dollar amount of claims for a driver of 10 years old is expected to be \$ 2961. 3. Which of the following is not correct (a) 82.2% of the variation in the dollar amounts of claims is explained by the age of the driver. (b) The correlation ݎ between the response variable and the predictor is 0.907 (c) If the histogram of the residuals is symmetric around zero and bell-shaped, then about 68% of the dollar amounts of claims are within 312.1 dollars of the regression line. (d) A driver in the data set whose age is 25 years had a residual of -\$150 using the fitted line above; this means his dollar amount of claims is \$1680. 4. The residual plot for a linear regression model is shown below.

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(a) A linear model is okay because the association between the two variables is fairly strong. (b) The linear model is not good because the correlation between the response and the predictor is near 0. (c) The linear model is not good because some residuals are large. (d) The linear model is not good because of the curve in the residuals. 5. In the output of a simple regression, the p -value associated with the slope is 0.45. Then (a) H 0 : β 1 = 0 should be rejected. (b) the data suggests that the predictor is not helpful in predicting the response. (c) the slope is more than 2 standard errors away from zero. (d) all of the above are correct 6 - 8. Data on 94 houses in the US yields the following regression output.
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## This note was uploaded on 12/20/2011 for the course ACCT/MGMT 2010 taught by Professor A during the Spring '11 term at HKUST.

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Practice_Final - Practice Final 1. The residual scatter...

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