SPSS SECONDARY DATA 8 PAGES Inferential Analysis (OBESITY AND POVERTY)

Both income and obesity are related in some non-linear ways. In most poor countries or third world countries, obesity level usually increase with a rise in come, while in developed nations, it decreases with income (Pee, et.al, 2017). The aim of this paper is to determine the relationship between poverty and obesity. In particular, we would like to know whether low income earners are at a higher risk of being affected by obesity. Our research question is therefore, “Are people living in poverty more likely to be affected by obesity?”. We therefore calculated the Body Mass Index of the individuals who participated in the research.

Null hypothesis: There is a significant relationship between obesity and poverty level.

Alternative hypothesis: There is no statistically significant relationship.

In my case, I assumed that poor people are those with a value less than 4 in terms of income level. I then ran a regression analysis of all the participants with an income level of less than 4 and their Body Mass Index in order to determine whether there is any association between the two variables (Chaterjee & Hadi, 2006). The total number of the poor individuals is 1867 out of the 7689 of the whole population considered during the study.

Results and Analysis

Descriptive Statistics
	Mean	Std. Deviation	N
BMI	44.1789	153.06134	1867
INCOME2	2.08	.822	1867

Correlations
		BMI	INCOME2
Pearson Correlation	BMI	1.000	.000
	INCOME2	.000	1.000
Sig. (1-tailed)	BMI	.	.495
	INCOME2	.495	.
N	BMI	1867	1867
	INCOME2	1867	1867

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	.000a	.000	.000	153.10236	.000	.000	1	1865	.989
a. Predictors: (Constant), INCOME2

ANOVAb
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	4.210	1	4.210	.000	.989a
	Residual	4.372E7	1865	23440.334
	Total	4.372E7	1866
a. Predictors: (Constant), INCOME2
b. Dependent Variable: BMI

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	95% Confidence Interval for B	Correlations
	B	Std. Error	Beta			Lower Bound	Upper Bound	Zero-order	Partial	Part
1	(Constant)	44.059	9.652		4.565	.000	25.129	62.989
	INCOME2	.058	4.312	.000	.013	.989	-8.400	8.515	.000	.000	.000
a. Dependent Variable: BMI

Findings

Correlation Table indicates that the correlation between BMI and income is significant since the p-value is less than the 0.05 significant level. From the Model Summary and ANOVA tables above, it can be deduced that the p-value (0.989) is greater than the 0.05 significance level. We therefore fail to reject the null hypothesis and conclude that there is statistically significance relationship between obesity and the level of poverty (Rubi, 2009). Thus, it can be alluded that people living in poverty are more likely to be affected by obesity. Some of the reasons for the rise in obesity cases among the poor individuals could be: irregular meals, lower education level, as well as higher rate of unemployment (Boison, 2017). Another factor is low physical activity since most poor people lack enough money to purchase sport equipment.

References

Boison, C. D. (2017). Relationship Between Family Income And Obesity. MA: Book Venture Publishing LLC.

Chatterjee, S., & Hadi, A. S. (2006). Regression Analysis by Example. Hoboken, NJ: John Wiley & Sons.

Pee, S. D., Taren, D., & Bloem, M. W. (2017). Nutrition and Health in a Developing World. New York, NY: Humana Press.

Rubin, A. (2009). Statistics for Evidence-Based Practice and Evaluation. Boston, MA: Cengage Learning.