bus_stats[1]

bus_stats[1] - 1. Check for mismeasured X variables. Label...

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1. Check for mismeasured X variables. Label this page PAGE 1 Write the question above your answer. A. What are you doing in Minitab to check for mismeasured X’s and how are you using the output . .what are you looking at? With a highlighter pen, highlight what you’re looking at. B. Using the output shown what did you find, and what did you do in this situation with these data? gression Analysis: Students versus Advertising, California, . .. The regression equation is Students = - 13.5 + 0.452 Advertising - 3.52 California + 1.21 Northeast + 0.138 perunion + 6.13 AOC Predictor Coef SE Coef T P Constant -13.514 3.271 -4.13 0.000 Advertising 0.45150 0.06818 6.62 0.000 California -3.524 1.992 -1.77 0.084 Northeast 1.208 1.677 0.72 0.475 perunion 0.13801 0.09050 1.53 0.134 AOC 6.132 2.145 2.86 0.006 Analysis of Variance Source DF SS MS F P Regression 5 1236.50 247.30 22.38 0.000 Residual Error 44 486.30 11.05 Total 49 1722.79 Unusual Observations Obs Advertising Students Fit SE Fit Residual St Resid 6 63.1 26.791 16.848 1.221 9.943 3.22R 7 58.6 18.754 16.332 2.169 2.422 0.96 X 35 17.1 2.143 1.689 2.071 0.454 0.17 X 40 67.6 21.433 14.151 0.951 7.282 2.29R 43 54.1 6.430 13.002 0.933 -6.572 -2.06R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Descriptive Statistics: Students, Advertising, California, Northeast, . .. Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3 Students 50 0 8.091 0.839 5.930 1.072 4.287 6.966 9.645 Advertising 50 0 46.27 2.10 14.82 13.52 34.92 49.57 58.58 California
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bus_stats[1] - 1. Check for mismeasured X variables. Label...

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