Due 2/1   8 p.m EST

Be on time, grad level work

PLEASE READ

DATA ATTACHED PLEASE USE AND PREVIOUS PAPER ATTACHED

(If the week 8 spv doesn’t open, that’s fine) Just use the other one with the variables

For your Scholar Practitioner Project, conduct descriptive and inferential analyses using your selected data set, your prepared database from Week 8, and SPSS. (BOTH ARE ATTACHED) Summarize numerical results in tables.

• Conduct selected descriptive statistics analyses using SPSS.
• Summarize the numerical results with descriptive analysis tables or graphs, including your interpretation.
• Conduct selected inferential statistics analyses using SPSS.
• Summarize the numerical results with inferential analysis tables or graphs, including your interpretation.
• Follow APA guidelines.
• Support your response

B

o

t

h 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, 20

1

7). 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.

N

ull 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.

R

esults and Analysis

1867

BMI

INCOME2

BMI

INCOME2

.000

1.000

BMI

INCOME2

.495

.

BMI

1867

1867

INCOME2

1867

1867

.000

.000

.000

.000

1

1

4.210

.000

1865

4.372E7

Sig.

Correlations

.000

INCOME2

.000

.989

.000

.000

.000

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.

outputViewer0000000000.xml
Output

&[PageTitle]

Page &[Page]

Log

NEW FILE.
DATASET NAME DataSet1 WINDOW=FRONT.
DATASET ACTIVATE DataSet1.
DATASET CLOSE DataSet0.
REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI R ANOVA CHANGE ZPP

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT BMI

/METHOD=ENTER INCOME2.

00000000011_7283741771240641926_notesData.bin

00000000011_7283741771240641926_notes.xml

Contents

Notes

00000000013_6477315962964612596_tableData.bin

00000000013_6477315962964612596_table.xml

Statistics Variables

Descriptive Statistics

00000000014_1342367962832115234_tableData.bin

00000000014_1342367962832115234_table.xml

Variables2 Variables Statistics

Correlations

00000000015_5241359340228122178_tableData.bin

00000000015_5241359340228122178_table.xml

Details Model

Variables Entered/Removed b

a. All requested variables entered.

b. Dependent Variable: BMI

00000000016_5008579534488409682_tableData.bin

00000000016_5008579534488409682_table.xml

Statistics Model

Model Summary

a. Predictors: (Constant), INCOME2

00000000017_2513022390971733601_tableData.bin

00000000017_2513022390971733601_table.xml

Statistics Source Model

ANOVA b

a. Predictors: (Constant), INCOME2

b. Dependent Variable: BMI

00000000018_2583391135149397617_tableData.bin

00000000018_2583391135149397617_table.xml

Statistics Variables Model

Coefficients a

a. Dependent Variable: BMI

outputViewer0000000001_heading.xml
Output Regression Title

Regression Notes 00000000011_7283741771240641926_notes.xml 00000000011_7283741771240641926_notesData.bin Active Dataset

[DataSet1]  Descriptive Statistics 00000000013_6477315962964612596_table.xml 00000000013_6477315962964612596_tableData.bin Correlations 00000000014_1342367962832115234_table.xml 00000000014_1342367962832115234_tableData.bin Variables Entered/Removed 00000000015_5241359340228122178_table.xml 00000000015_5241359340228122178_tableData.bin Model Summary 00000000016_5008579534488409682_table.xml 00000000016_5008579534488409682_tableData.bin ANOVA 00000000017_2513022390971733601_table.xml 00000000017_2513022390971733601_tableData.bin Coefficients 00000000018_2583391135149397617_table.xml 00000000018_2583391135149397617_tableData.bin

outputViewer0000000002.xml
Output Log

SAVE OUTFILE=’C:\Users\user\Desktop\Analysis of Secondary Data.sav’

/COMPRESSED.

META-INF/MANIFEST.MF
allowPivoting=true