Studies have shown that the frequency with which shoppers browse Internet retailers is positively correlated to the frequency with which they actually make purchases.
The following data show respondents age and "How many minutes do you browse online retailers per year?"
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
Compute the correlation between Age and Time using Data Analysis. Include the labels in the Input Range and check the Labels checkbox.
A	B
Age	Time
age data	time data
Compute the correlation using the Excel function =CORREL. If answers for #10 and 11 do not agree, there is an error.
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) axis.
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
Read directly from the chart:
a) Intercept =
b) Slope =
c) R2 =
Perform regression using Data Analysis. Select the Time data first, include the labels in row 4 in the Input Range, and check the Labels checkbox.
In the Regression output, highlight the Y-intercept red, the slope blue, and R2 green.
Use the Data Analysis output to predict the number of minutes spent by a 35-year old shopper. Enter = followed by the regression formula,
entering the intercept and slope into the formula by clicking on the corresponding cells in the regression output.
(Week 11 Presentation, slide 11)
Is it appropriate to use this data to predict the amount of time that a 75-year-old will spend browsing online retailers ?
Why or why not (Week 11 Presentation, slide 8)?