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Effects of Obesity on the Incidence of Diabetes

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    The CDC also attributes the onset of type 2 diabetes to obesity in many cases. The purpose of this analysis is to determine the effects of obesity (OBESE) on the incidence of diabetes (DIABETIC) while holding the effects of alcohol consumption (ALCOHOL), ethnicity (HISPANIC), and age (AGE) constant. This study will use cross-sectional data from the 50 states for the year 2010. The model (less constant and coefficients) is: DIABETIC = OBESE + ALCOHOL + HISPANIC + AGE

    The dependent variable, DIABETIC, is defined as the percent of people who have been told by a doctor that they have diabetes, and is extracted from the Center for Disease Control and Prevention Behavioral Risk Factor Surveillance System (2011). Data for the independent variables OBESE, ALCOHOL, HISPANIC, and AGE were also taken from the Center for Disease Control and Prevention Behavioral Risk Factor Surveillance System (2011) by percentage. OBESE is defined as individuals with a body mass index (BMI) greater than 30.

    ALCOHOL is defined as heavy drinkers (adult men having more than two drinks per day and adult women having more than one drink per day). HISPANIC is defined as individuals who identify themselves as having Hispanic ethnicity. Lastly AGE is defined as individuals 45 years of age and older. The independent variables were selected due to their association with diabetes. According to the CDC “Risk factors for type 2 diabetes include older age, obesity,… and race/ethnicity” (Center for Disease Control and Prevention, 2011).

    The American Diabetes Association links alcohol consumption and hypoglycemia “Women should drink 1 or fewer alcoholic beverages a day (1 alcoholic drink equals a 12 oz beer, 5 oz glass of wine, or 1 ? oz distilled spirits (vodka, whiskey, gin, etc. ). Men should drink 2 or fewer alcoholic drinks a day” (American Diabetes Association, 2011). II. Regression Analysis The model was regressed and the results are shown in Table 1. Table 1 – Original Regression Results Dependent Variable: DIABETIC Adjusted R2 = 0. 6442 n = 54 | Independent Variables| Coefficients| t Statistic| P-Value| OBESE| 0. 486| 6. 4528| 0. 0000| ALCOHOL| -0. 2904| -1. 8262| 0. 0739| HISPANIC| 0. 0441| 4. 1307| 0. 0001| AGE| 0. 1207| 4. 3074| 0. 0001| The initial regression analysis is pretty good. The adjusted R2 value of 0. 6442 shows there is a moderate positive correlation. The coefficients for OBESE, HISPANIC and AGE are positive as expected. The coefficient for ALCOHOL, however, is negative where a positive result was expected. This might be because while ALCOHOL has negative effects on a person suffering from diabetes it is not itself a cause of diabetes. Using a level of significance of 0. 5 and using the P-Value above all variables with the exception of ALCOHOL are shown to be statistically significant. Following the initial regression a correlation matrix was completed. The results are shown in Table 2. Table 2 – Cross Correlation Matrix The data above shows a good cross correlation matrix. There are no coefficients less the -0. 7 or greater than 0. 7. This indicates that no multicollinearity was found using the cross correlation matrix. Variance inflation factors were calculated. The results are shown in Table 3. Table 3 – Variance Inflation Factors

    All variance inflation factors are calculated to be less than 10. This also indicates there is not a problem with multicollinearity. While no problem with multicollinearity was found to exist there was a variable that was shown to be insignificant. The variable ALCOHOL was removed from the data and the regression analysis was repeated. After removing the ALCOHOL data the model was regressed again. The results are shown in Table 4. Table 4 – Final Regression Results Dependent Variable: DIABETIC Adjusted R2 = 0. 5971 n = 54 | Independent Variables| Coefficients| t Statistic| P-Value| OBESE| 0. 947| 8. 0804| 0. 0000| HISPANIC| 0. 0486| 4. 5747| 0. 0001| AGE| 0. 1155| 4. 0509| 0. 0001| The final regression has an adjusted R2 value of 0. 5971 showing there is a moderate positive correlation. The coefficients for OBESE, HISPANIC and AGE are positive as expected after the removal of ALOCHOL. Using a level of significance of 0. 05 and using the P-Value above all variables are shown to be statistically significant. Following the final regression a correlation matrix was completed. The results are shown in Table 5. Table 5 – Cross Correlation Matrix The data above shows a good cross correlation matrix.

    There are no coefficients less the -0. 7 or greater than 0. 7. This indicates that no multicollinearity was found using the cross correlation matrix. Variance inflation factors were calculated. The results are shown in Table 6. Table 6 – Variance Inflation Factors All variance inflation factors are calculated to be less than 10. This also indicates there is not a problem with multicollinearity. After conducting all of the analysis the final results can be found in Table 7. Table 7 – Final Results I chose to remove ALCOHOL from the final regression for two reasons.

    First, the coefficient was negative and I was expecting a positive result. Excessive alcohol consumption does not decrease your chances of getting diabetes. Second, the P-value of 0. 0739 was shown not to be statistically significant when tested against a level of significance of 0. 05. The adjusted R2 value did go down slightly but there is still a moderate positive relationship. III. Conclusions The final model is shown below: DIABETIC = -8. 53 + 0. 39OBESE + 0. 05HISPANIC + 0. 12AGE Overall that data shows a moderate positive relationship between DIABETIC, OBESE, HISPANIC, and AGE.

    As expected the variable with the greatest impact on DIABETIC is OBESE. The data shows that people who are clinically obese, that is having a BMI greater then 30, have a 39% greater chance of becoming diabetic. ALCOHOL was proven not to be statistically significant in developing diabetes. Researching this topic further I would look for an additional cause of diabetes to replace alcohol consumption. References American Diabetes Association Home Page – American Diabetes Association. (n. d. ). American Diabetes Association Home Page – American Diabetes Association. Retrieved November 19, 2011, from http://www. iabetes. org/ CDC – Basics about Diabetes – Diabetes & Me – Diabetes DDT. (n. d. ). Centers for Disease Control and Prevention. Retrieved November 19, 2011, from http://www. cdc. gov/diabetes/consumer/learn. htm Center for Disease Control and Prevention Behavioral Risk Factor Surveillance System. (n. d. ). Centers for Disease Control and Prevention. Retrieved November 19, 2011, from http://apps. nccd. cdc. gov/brfss/page. asp? cat=XX&yr=2010&state=All#XX Appendix 1. Excel Summary output for original regression: 2. Excel Summary output for final regression:

    Effects of Obesity on the Incidence of Diabetes. (2016, Dec 18). Retrieved from https://graduateway.com/diabet-analysis/

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