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The Data Hours per week Hours Per month Loss per Month
Citation preview
Houston, Texas
FAT CITY, USAGloria Lobo-Stratton
Sharon LovdahlDennis Glendenning
Does Exercise Effect Weight?
Surveyed forty individuals No Restrictions No Bias No Criteria No Expectations Just Information
The Data Hours per week Hours Per month Loss per Month
1 4.33 0
1 4.33 0
1 4.33 2
1 4.33 2
1 4.33 1
1 4.33 1
1 4.33 2
2 8.67 1
2 8.67 2
2 8.67 3
2 8.67 1
2 8.67 2
2 8.67 1
2 8.67 2
2 8.67 1
2 8.67 1
2 8.67 2
2 8.67 3
2 8.67 1
The Rest of the Story Hours per week Hours Per month Loss per Month
3 13.00 2
3 13.00 1
3 13.00 2
3 13.00 3
3 13.00 2
3 13.00 1
3 13.00 2
3 13.00 4
3 13.00 4
3 13.00 3
3 13.00 4
4 17.33 2
4 17.33 3
4 17.33 4
4 17.33 5
5 21.67 4
5 21.67 3
5 21.67 3
5 21.67 3
8 34.67 4
10 43.33 5
Regression Analysis
The Relationship of the Data Is the Data Linear? What Does It Tell Us? Why Would We Care? Who Could Benefit from This Information?
The Analysis
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.684895
R Square 0.469081
Adjusted R Square 0.455109
Standard Error 0.948561
Observations 40
ANOVA df SS MS F Significance F
Regression 1 30.20881 30.20881 33.574 1.09E-06
Residual 38 34.19119 0.899768Total 39 64.4
Coefficients Standard Error t Stat P-value Lower 95%Intercept 0.898805 0.284557 3.158617 0.003104 0.322751Hours Per month 0.109611 0.018917 5.794307 1.09E-06 0.071316
Predicted Loss Per MonthObservation Predicted Loss per Month Residuals
1 1.373786408 -1.373786408
2 1.373786408 -1.373786408
3 1.373786408 0.626213592
4 1.373786408 0.626213592
5 1.373786408 -0.373786408
6 1.373786408 -0.373786408
7 1.373786408 0.626213592
8 1.848767737 -0.848767737
9 1.848767737 0.151232263
10 1.848767737 1.151232263
11 1.848767737 -0.848767737
12 1.848767737 0.151232263
13 1.848767737 -0.848767737
14 1.848767737 0.151232263
15 1.848767737 -0.848767737
16 1.848767737 -0.848767737
17 1.848767737 0.151232263
18 1.848767737 1.151232263
19 1.848767737 -0.848767737
20 2.323749066 -0.323749066
The Rest of the DataObservation Predicted Loss per Month Residuals
21 2.323749066 -1.323749066
22 2.323749066 -0.323749066
23 2.323749066 0.676250934
24 2.323749066 -0.323749066
25 2.323749066 -1.323749066
26 2.323749066 -0.323749066
27 2.323749066 1.676250934
28 2.323749066 1.676250934
29 2.323749066 0.676250934
30 2.323749066 1.676250934
31 2.798730396 -0.798730396
32 2.798730396 0.201269604
33 2.798730396 1.201269604
34 2.798730396 2.201269604
35 3.273711725 0.726288275
36 3.273711725 -0.273711725
37 3.273711725 -0.273711725
38 3.273711725 -0.273711725
39 4.698655713 -0.698655713
40 5.648618372 -0.648618372
Scatter Plot
Loss per Month
0
1
2
3
4
5
6
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00
Hours
Poun
ds
Regression Line
Chart Title
y = 0.1096x + 0.8988
0
1
2
3
4
5
6
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00
Hours
Poun
ds
Predicted Versus Residuals
Hours Per month Line Fit Plot
0246
0.00 10.00 20.00 30.00 40.00 50.00
Hours Per month
Loss
per
Mon
th
Loss per MonthPredicted Loss per Month
Limitations of the Data
Below 5 hours per month there is no result
A minimum of fifteen minutes a day
More than fifty hours a month not likely
1.6 hours a day
ˆ 0.8988 0.1096xY ˆ 0.8988 0.1096xY ˆ 0.8988 0.1096*5 1.447Y
ˆ 0.8988 0.1096xY
What This Means for You
ˆ 0.8988 0.1096*5 1.447Y
Thank You