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r = 0.238, n = 92
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Construct a scatter diagram for the given data. 
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Determine which plot shows the strongest linear correlation. 
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Find the value of the linear correlation coefficient r. 
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| 0.708 |
| -0.071 |
| 0.246 |
| 0.235 |
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Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. 
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Find the best predicted value of y corresponding to the given value of x. 
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| 19.0 |
| 18.5 |
| 22.0 |
| 18.0 |
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Is the data point, P, an outlier, an influential point, both, or neither? 
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| Influential point |
| Neither |
| Outlier |
| Both |
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Use the computer display to answer the question. 
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| (31.61, 75.09); We can be 95% confident that the test score of an individual who studies 4.5 years will lie in the interval (31.61, 75.09) |
| (42.72, 63.98); We can be 95% confident that the test score of an individual who studies 4.5 years will lie in the interval (42.72, 63.98) |
| (31.61, 75.09); We can be 95% confident that the mean test score of all individuals who study 4.5 years will lie in the interval (31.61, 75.09) |
| (42.72, 63.98); We can be 95% confident that the mean test score of all individuals who study 4.5 years will lie in the interval (42.72, 63.98) |
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Use the given information to find the coefficient of determination. 
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| -0.0707 |
| 0.5009 |
| 0.2353 |
| 0.7077 |
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Find the explained variation for the paired data. 
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| 87.4757 |
| 511.724 |
| 498.103 |
| 599.2 |
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Find the unexplained variation for the paired data. 
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| 599.2 |
| 96.103 |
| 511.724 |
| 87.476 |
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Find the total variation for the paired data. 
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| 498.103 |
| 599.2 |
| 87.4757 |
| 511.724 |
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Find the standard error of estimate for the given paired data. 
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| 13.060 |
| 4.1097 |
| 5.3999 |
| 7.1720 |
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Construct the indicated prediction interval for an individual y. 
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| 58.5 < y < 84.5 |
| 56.5 < y < 86.5 |
| 52.1< y < 90.9 |
| 65.4 < y < 77.6 |
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Use computer software to find the regression equation. Can the equation be used for prediction? 
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Use computer software to obtain the regression and identify R2, adjusted R2, and the P-value. 
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| .213, .182, .213 |
| .025, -060, .750 |
| .049, .-021, .123 |
| .089, .032, .634 |
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Use computer software to obtain the regression equation. Use the estimated equation to find the predicted value. 
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| 15 |
| 7 |
| 9.259 |
| 12 |
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Find the indicated multiple regression equation. 
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Use computer software to find the best regression equation to explain the variation in the dependent variable, Y, in terms of the independent variables, X1, X2. 
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Construct a scatterplot and identify the mathematical model that best fits the data. Assume that the model is to be used only for the scope of the given data and consider only linear, quadratic, logarithmic, exponential, and power models. Use a calculator or computer to obtain the regression equation of the model that best fits the data. You may need to fit several models and compare the values of R to power of (2). 
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