12 May Q-1a. You run a call center and are concerned about customer support
Q-1a. You run a call center and are concerned about customer support
service levels on the Help Desk. You want to know how many calls
per day are handled by your help desk staff. You collect the data at
left over a 75-day period. Use appropriate descriptive statistics to
make sense of this data. Use an appropriate graph also. Explain
your findings so that your non-quantitative partner will understand
them. (5 pts.)
Help Desk Calls Day # Calls 1 45 2 30 3 53 4 69 5 67 6 45 7 17 8 46 9 32 10 33 11 59 12 74 13 42 14 35 15 38 16 18 17 41 18 63 19 72 20 43 21 45 22 44 23 48 24 48 25 67 26 72 27 41 28 52 29 75 30 40 31 34 32 40 33 88 34 63 35 38 36 48 37 45 38 50 39 150 40 84 41 28 42 37 43 52 44 44 45 49 46 70 47 75 48 58 49 23 50 62 51 10 52 71 53 80 54 70 55 41 56 47 57 99 58 38 59 29 60 83 61 60 62 54 63 35 64 46 65 51 66 58 67 72 68 86 69 48 70 48 71 51 72 62 73 62 74 85 75 150
Can demographic information be helpful in predicting sales at sporting goods stores? The data at left are monthly sales totals from a random sample of 33 stores in a large chain of nationwide sporting goods stores. All stores in the franchise, and thus within the sample, are approximately the same size and carry the same merchandise. The county, or in some cases counties, in which the store draws the majority of its customers is referred to here as the customer base. For each of the 33 set are:
Sales ——Latest one month sales total (dollars)
Income —Median family income of customer base (dollars)
Age ——–Median age of customer base (years)
HS ———-Percentage of customer base with a high school diploma
College —Percentage of customer base with a college diploma
Growth —Annual population growth rate of customer base over the past 10 years.
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Q-2a. Construct a scatter plot, using sales as the dependent variable and median family income as the
independent variable. Discuss the scatter plot. (5 pts.)
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Q-2b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients
b0and b1 and state the regression equation. (5 pts.)
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Q-2c. Interpret the meaning of the Y-intercept, b0, and the slope, b1, in this problem. (5 pts.)
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Q-2d. Compute the coefficient of determination r2, and interpret its meaning. (5 pts.)
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Q-2e. Construct a 95% confidence interval estimate of the population slope and interpret its meaning. ( 5 pts.)
Sales ($) Age Growth Income ($) HS College 1695712.62 33.16 0.8299 26748.51 73.59 17.8350 3403862.05 32.67 0.6619 53063.79 88.46 31.9439 2710352.91 35.66 0.9688 36090.14 73.54 18.6198 529215.46 33.07 0.0821 32058.07 79.18 20.6284 663686.65 35.76 0.4646 47843.42 84.18 35.2032 2546324.34 33.81 2.1796 50180.97 93.50 41.7057 2787046.2 30.98 1.8048 30710.08 78.02 28.0250 612696.05 30.78 -0.0569 29141.7 70.29 15.0882 891822.03 32.32 -0.1577 55980.15 70.67 10.9829 1124967.97 32.53 0.3664 28730.88 63.74 13.2458 909500.98 31.44 2.2256 31109.23 76.91 19.5500 2631166.88 33.16 1.5158 55614.12 82.95 20.8135 882972.65 31.87 0.1413 23038.43 65.21 16.9796 1078573.12 33.41 -1.0400 34531.72 73.49 32.9920 844320.19 34.05 1.6836 30350.36 80.22 22.3185 1849119.03 28.89 2.3596 38964.94 87.60 24.5670 3860007.32 36.11 0.7840 49392.77 85.30 30.8790 826573.88 32.81 0.1164 25595.69 65.59 17.4545 604682.87 33.05 1.1498 29622.61 80.62 18.6356 1903611.6 33.50 0.0606 31586.1 80.38 38.3249 2356808.39 32.68 1.6338 49674.56 79.85 23.7780 2788571.96 28.52 1.1256 28878.98 81.24 16.9300 1634878.29 32.89 1.4884 24287.08 70.22 19.1429 2371627.37 30.50 4.7937 46711.24 87.10 30.8843 2627837.96 30.29 1.8922 43449.81 80.21 26.5570 1868116.33 31.29 1.8667 31694.45 75.29 28.3600 2236796.86 33.05 1.7896 45459.22 77.62 19.2490 1318876.23 32.93 0.2707 47047.34 85.18 35.4994 1868097.84 31.84 3.0129 26433.24 74.18 18.6375 1695218.57 31.08 3.4630 33396.66 81.70 41.1130 2700194.42 32.18 0.7041 26179.36 73.41 17.8566 1156049.77 31.69 -0.1569 33454.64 73.72 26.5426 643858.44 34.03 0.7084 42271.5 78.65 29.8734
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