Houston, Texas FAT CITY, USA Gloria Lobo-Stratton Sharon Lovdahl Dennis Glendenning

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