C you can use weight to predict price using a

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c. You can use weight to predict price using a regression line and it would fit well. d. All of the above.
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36. The correlation would change if you changed the units of measurement to be grams for weight and American dollars for price (True or False) a. True b. False 37. Using information from the output above, what is the standard deviation of weight? 38. Interpret the slope found in the above output in terms of X and Y. a. As X increases by 1, Y decreases by 259.63 b. As X increases by 1, Y increases by 3721.02 c. As X increases by 3,721.02, Y decreases by 259.63 d. None of the above. Regression (Part 2) Let x be the change in a stock market index in January and let y be the change in the stock market index for the entire year. Descriptive statistics from 1960 to 1997 are shown below. Descriptive Statistics: Jan, Year Total Variable Count Mean StDev Minimum Median Maximum Jan 37 0.018 0.016 0.002 0.020 0.200 Year 37 0.091 0.010 0.520 0.101 0.393 Pearson correlation of Jan and Year = 0.796 P-Value = 0.895 39. Slope is in what units in the context of this problem? *40. At which point on the best fitting line will you make the very best predictions? Give an (x,y) point. Data was collected on amount of rainfall (inches) and amount of corn produced (bushels per acre) for a number of years in Kansas. The output is shown below. Predictor Coef SE Coef T P Constant 89.543 6.703 13.36 0.000 Rainfall 0.12800 0.01375 9.31 0.000 Correlation of Rainfall and Corn = 0.608 41. Find the value of r 2 for this problem.
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42. Interpret the value of r 2 in this problem. (Make sure you can interpret the values of r2 in each of the other problems in #1-38 above.) *43. Suppose you know the value of r 2 . Can you just take its square root to get the correct correlation? Explain why or why not. If not, what other info do you need to get the sign of r? 44. An influential point always has a large residual (True or False) 45. A point with a large residual is an outlier in the vertical (Y) direction. (True or False) 46. A residual is the difference between what you predicted Y to be based on the line, and what the actual of Y turned out to be in your data set. (True or False). *47. Which of the following are affected by outliers? a. The correlation b. The slope of the regression line c. The Y-intercept of the regression line d. All of the above *48. The residual plot should show you a linear pattern if a regression line fits well. (True or False) *49. To fit a linear regression line all you need is a strong value of r. (True or False) *50. The value of r 2 is always greater than or equal to the value of r.
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