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Created: Oct 20, 2017
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Week 9- Depression
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StatCrunch week 9


Depression is becoming a more widely acknowledged problem in social media and the news. More and more emphasis is being placed on depression education and research. As part of a long-term study of individuals 65 years of age or older, sociologists and physicians at the Wentworth medical Center in upstate New York investigated the relationship between geographic location, health status, and depression.


Samples of 20 healthy individuals were selected from three geographic locations: Florida, New York, and North Carolina. Then, each was given a standardized test to measure depression (higher test scores indicate higher levels of depression). Then a sample of 20 individuals with one or more comorbidities (arthritis, hypertension, and/or heart ailment) were taken from the three geographic locations. They were given the same standardized test to measure depression. For this analysis, the depression scores will be treated as I/R data. Health status is recorded as “healthy” or “CMB” indicating one or more comorbidities. Two-way ANOVA will be used to assess the effects of location and comorbidities on depression scores. We can assume that these groups are independent and randomly assigned. After reviewing our normal probability QQplot and fiited values scatter plot, we can also determine there is no evidence of issues with normality or homogeneity of variance assumptions.

Result 1: QQ Plot- Wk 9   [Info]
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Result 2: Scatter Plot- Fitted Values- wk 9   [Info]
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Result 3: Two Way ANOVA- Wk 9   [Info]
Two Way Analysis of Variance results:
Responses: Score
Row factor: Location
Column factor: Health

ANOVA table

Tukey HSD results (95% level) for Location:
Florida subtracted from
New York1.60.0182111423.18178890.0468
North Carolina0.475-1.10678892.05678890.7563
New York subtracted from
North Carolina-1.125-2.70678890.456788860.2138

Tukey HSD results (95% level) for Health:
CMB subtracted from

Tukey HSD results (95% level) for Location*Health:
Florida,CMB subtracted from
New York,CMB0.75-1.98065453.48065450.9676
New York,Healthy-6.5-9.2306545-3.7693455<0.0001
North Carolina,CMB-0.55-3.28065452.18065450.9919
North Carolina,Healthy-7.45-10.180654-4.7193455<0.0001
Florida,Healthy subtracted from
New York,CMB9.76.969345512.430654<0.0001
New York,Healthy2.45-0.280654475.18065450.1054
North Carolina,CMB8.45.669345511.130654<0.0001
North Carolina,Healthy1.5-1.23065454.23065450.6053
New York,CMB subtracted from
New York,Healthy-7.25-9.9806545-4.5193455<0.0001
North Carolina,CMB-1.3-4.03065451.43065450.7389
North Carolina,Healthy-8.2-10.930654This two-way ANOVA with interaction was done to assess the effects of location and health status on depression scores. After reviewing the results, we can determine that there is no evidence of interaction between location and health on depression scores (F-stat 1.35 and p-value 0.26). There is also no evidence of effect of location on depression scores (F-stat 3.04 and p-value 0.05). We do however have evidence of a difference on the effect of health on average depression scores (F-stat 200.45 and p-value <0.0001). With 95% confidence, we can estimate that the average depression scores for those with a CMB are 6.62 to 8.78 points higher than “healthy” individuals. These interpretations are supported by the interaction plot.

Result 4: Two Way ANOVA Interaction- Wk 9   [Info]
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Based on the results of this study it does appear that having a comorbidity holds the greatest impact on depression.

Conclusions/Further Study

Depression screenings should be prioritized in health care settings for all patients but especially those with CMBs. Further studies should look at if there is a relationship between type of CMB and depression score or between number of CMBs and depression scores.

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By nku.katie.waters
Oct 24, 2017

Hi Ashleigh,
Great job on your report! I scanned quickly through your report and the only thing I noticed was that you forgot the word "average" in a couple of your interpretations. (We are talking about average depression scores.)

Please review the solutions and let me know if you have any questions.
By ekilburn81
Oct 21, 2017

Great report. I did my report on the same one. I find it very interesting because I can relate to some of information. As a disabled veteran I hurt my back while in the Army. I am in pain and that right there makes me depressed because I am tired of the pain. I would like to see more research on what comorbidities show the most depression. Great job.
By sadouskasm
Oct 20, 2017

This is an interesting study. I would like more research as to why comorbidities effect depression and other diseases studied as well.

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