Question

Studies have shown that the frequency with which shoppers browse Internet retailers is related to the frequency with which they actually purchase products and/or services online. The following data show respondents age and answer to the question “How many minutes do you browse online retailers per week?”

Age (X)	Time (Y)
13	5662
19	4549
16	3772
44	1872
32	2799
52	1355
39	1966
15	5682
40	1602
53	1186
48	1832
37	2253
36	2241
42	1001
30	2474
42	1943
28	3021
11	5682
32	2192
39	1784
23	2707
37	1801
17	4827
11	2693
18	4340
50	1399
52	1593
9	9154
41	1504
26	2627
30	2575
32	2711
53	2368

10) Use Data > Data Analysis > Correlation to compute the correlation checking the Labels checkbox.

11) Use the Excel function =CORREL to compute the correlation. If answers for #1 and 2 do not agree, there is an error.

12) The strength of the correlation motivates further examination.

a) Insert Scatter (X, Y) plot linked to the data on this sheet with Age on the horizontal (X) axis.

b) Add to your chart: the chart name, vertical axis label, and horizontal axis label.

c) Complete the chart by adding Trendline and checking boxes

13) Read directly from the chart:

a) Intercept =

b) Slope =

c) R2 =

Perform Data > Data Analysis > Regression.

14) Highlight the Y-intercept with yellow. Highlight the X variable in blue. Highlight the total standard error in orange

SUMMARY OUTPUT

Answer 1

10) Use Data > Data Analysis > Correlation to compute the correlation checking the Labels checkbox.

Ans:

	Age (X)	Time (Y)
Age (X)	1
Time (Y)	-0.81122	1

11) Use the Excel function =CORREL to compute the correlation. If answers for #1 and 2 do not agree, there is an error.

Ans: The correlation estimated using e Excel function =CORREL is -0.81122.

12) The strength of the correlation motivates further examination.

The correlation value -s -0.81122 between the Age (X) and Time (Y). Hence, it has a strong negative relationship. Therefore, the motivation of the correlation is that to find the regression equation to predict the Time using the Age.

a) Insert Scatter (X, Y) plot linked to the data on this sheet with Age on the horizontal (X) axis.

b) Add to your chart: the chart name, vertical axis label, and horizontal axis label.

c) Complete the chart by adding Trendline and checking boxes

13) Read directly from the chart:

a) Intercept =6223

b) Slope = - 103.27

c) R2 =0.6581

Perform Data > Data Analysis > Regression.

14) Highlight the Y-intercept with yellow. Highlight the X variable in blue. Highlight the total standard error in orange

SUMMARY OUTPUT

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.8112
R Square	0.6581
Adjusted R Square	0.6471
Standard Error	1032.1123
Observations	33
ANOVA
	df	SS	MS	F	Significance F
Regression	1	63559413	63559413	59.66587	1.0298E-08
Residual	31	33022931	1065256
Total	32	96582344
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	6223.04193	468.1432	13.2930	0.0000	5268.2576	7177.8263
X Variable 1	-103.274024	13.3699	-7.7244	0.0000	-130.5421	-76.0059