MGMT 650 Homework 11 pg 3

Studies have shown that the frequency with which shoppers browse Internet retailers is positive The following data show respondents age and "How many minutes do you browse online retaile Age (X) Time (Y) 16 470 17 319 19 365 22 387 22 293 22 509 22 464 28 274 28 431 28 462 28 626 30 383 33 601 34 598 35 676 35 571 35 612 36 749 39 693 39 505 40 716 42 603 43 509 44 575 48 609 50 557 50 662 51 760 52 428 54 616 58 702 59 775 60 750 10 Compute the correlation between Age and Time using Data Analysis. Include the labels in the In Age (X) Time (Y) Age (X) 1 Time (Y) 0.68756874209865 1 11 Compute the correlation using the Excel function =CORREL. If answers for #10 and 11 do not a 0.69 12 The strength of the correlation motivates further examination. a) Make a scatter plot linked to and near the data above, and with Age on the horizontal (X) axi b) Add to your chart A meaningful title Vertical axis label Time Horizontal axis label Age c) Complete the chart by adding Trendline and checking boxes 10 20 0 100 200 300 400 500 600 700 800 900 f(x) = 7.6094 R² = 0.47275 TIME

13 Read directly from the chart: a) Intercept = 271.94 b) Slope = 7.6094 0.4728 14a Perform regression using Data Analysis. Select the Time data first, include the labels in row 4 in SUMMARY OUTPUT Regression Statistics Multiple R 0.68756874209865 R Square 0.47275077511112 Adjusted R Square 0.45574273559857 Standard Error 104.338336232446 Observations 33 ANOVA df SS MS F Significance F Regression 1 302597.8290566 302597.83 27.795724 9.840657E-06 Residual 31 337481.1406404 10886.488 Total 32 640078.969697 Coefficients Standard Error t Stat P-value Lower 95% Intercept 271.94185116031 56.32444354728 4.8281321 3.513E-05 157.06739118 Age (X) 7.60944947638211 1.443325355484 5.272165 9.841E-06 4.6657680063 14b 15 Use the Data Analysis output to predict the number of minutes spent by a 35-year old shopper. entering the intercept and slope into the formula by clicking on the corresponding cells in the reg (Week 11 Presentation, slide 11) 538 16 Is it appropriate to use this data to predict the amount of time that a 75-year-old will spend brow No Why or why not (Week 11 Presentation, slide 8)? Using this data, the amount of time would be more time online than would seem reasonable. c) R 2 = In the Regression output, highlight the Y-intercept red, the slope blue, and R 2 green.

n the Input Range, and check the Labels checkbox. Upper 95% Lower 95.0% Upper 95.0% 386.816311136 157.067391185 386.81631113577 10.5531309464 4.66576800635 10.553130946417 Enter = followed by the regression formula, gression output. wsing online retailers ?