Linear Regression

Surname 1
22/09/2017
From: Name
To: Instructor
Subj: Linear Regression
Introduction
Hip fracture is increasingly becoming a global health challenge. Thousands, if not
millions of people are faced with this challenge. According to Anderson (2015), hip fracture
can happen at any age and common causes include impact from accident, and weak bones
referred to as bone loss (osteoporosis). CDC (2015) asserts that more than 200 million
people globally have experienced a hip fracture, resulting to a huge burden on financial and
health-care resources.
Problem statement
In order to confirm the possibilities of rare outcome of hip fracture incidence, this
research project examines whether dairy and meat consumption influences the rate of hip
fracture among population.
Research Objective
Research Question
Is there a relationship between dairy and meat consumption, and bone density?
Statistical Question
Do dairy and meat consumption influence bone density?
Research Methodology
The study utilized dataset from Africa, Asia, Europe, Asia, North America, and Latin
America, data from 40 countries. Hip fracture incidence, dairy consumption, lactase
persistence SNPs, animal protein consumption and per capita income were the variables
selected for analysis. Excel and SAS provides good tools for regression test, therefore, they
Surname 2
were utilized. Multiple regression test was used to establish whether there is a relationship
between our variables, which were; dairy consumption and meat consumption (independent
variable) and bone density as the dependent variable.
Presentation of Findings
The analysis findings of data from 40 countries containing information of dairy and
meat consumption, and bone density variable were computed and presented in summary
tables and scatter.
Table 1.1 Shows correlation between Hip fracture rates per 100,000 and dairy consumption for the
data set of 40 countries globally. The correlation value is 0.85
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.852238764
R Square
0.726310911
Adjusted R Square
0.719108566
Standard Error
111.8497235
Observations
40
Coefficients
Standard
Error
t Stat
P-value
Lower 95%
Upper
95%
Lower
95.0%
Intercept
28.5661493
35.5862439
0.80273
0.427123
-43.4744
100.6067
-43.4744
X Variable 1
1.783139656
0.17756649
10.0421
3.04E-12
1.423675
2.142604
1.423675
Table 1.2 Shows correlation between Hip fracture rates per 100,000 and meat consumption for the
data set of 40 countries globally. The correlation value is 0.45
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.450356854
R Square
0.202821296
Adjusted R Square
0.181842909
Standard Error
190.8903687
Observations
40
Coefficients
Standard
Error
t Stat
P-value
Lower 95%
Upper
95%
Lower
95.0%
Intercept
151.8020187
67.25297
2.257179
0.029827
15.6555
287.9485
15.6555
X Variable 1
2.740536917
0.881384
3.109358
0.003545
0.956269
4.524805
0.956269
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The Table 1.2 a summary output computed using Excel, also visualized in Figure 1.2
Figure 1.1 The Scatter plot showing relationship between Hip fracture rates per 100,000 and
dairy consumption for the data set of 40 countries globally. The correlation value is 0.85.
The Figure 1.1 a scatter plot ( Source: SAS)
Figure 1.2 The scatter plot showing relationship between Hip fracture rates per 100,000 and
meat consumption from a data set of 40 countries. The correlation value is 0.45.
The Figure 1.2 a scatter plot calculated using SAS, an Analysis of Table 1.2
y = 1.7831x + 28.566
R² = 0.7263
10
110
210
310
410
510
610
710
810
910
5 55 105 155 205 255 305 355 405
Hip fracture Per 100000
Dairy Consumption
Hip fracture versus Dairy consumption
Series1
Linear (Series1)
y = 2.7405x + 151.8
R² = 0.2028
10
110
210
310
410
510
610
710
810
910
5 25 45 65 85 105 125 145
hip Fracture per 100,000
Meat consumption (kg)
Scatter plot: Hip Fracture Versus Meat Consumption
Series1
Linear (Series1)
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Discussion of findings
Table 1.1 indicates that dairy consumption as an independent variable creates a
stronger relationship with bone density (r=0.85, as shown in (Figure 1.1) scatter plot: Hip
Fracture vs. Dairy Consumption). In addition, a strong relationship is demonstrated for the
second independent variable of meat consumption and bone density (Table 1.2), as shown in
(Figure 1.2: scatter plot: Hip Fracture Incidence vs. Meat consumption) and r=0.45.
Conclusion
Based on the findings, we can conclude that, there is a stronger relationship between
dairy and meat consumption, and bone density. Therefore, individuals with the highest meat
and dairy consumption have the lowest risk of bone fracture due to their heavy bone density.
Recommendation
The study recommends that the stakeholders must improve the welfare of its citizens
so that they can afford a good diet that is comprised of adequate meat and dairy consumption.
Poor diet leads to malnutrition occurrence, with risks of developing diseases such as
Osteoporotic. According to Holick and Khan (2005), the osteoporotic risk is frequent in
countries with highest malnutrition prevalence, which is typically instigated by low personal
disposable income. For instance, the authors assert that majority of women in North
American suffer from osteoporosis because of inadequate calcium in their bodies. Inadequate
calcium intake contributes to diminished bone density. In addition, a medical study by
Odawa, Ojwang, and Muia (2014) indicates that a diagnosis of osteoporosis among 24.3% of
postmenopausal women and osteopenia in 32% is because of bones disorder.
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References
Anderson, J. (2016, March 12). Hip fracture. Retrieved from ENCYCLOPÆDIA
BRITANNICA: https://www.britannica.com/science/hip-fracture
Center for Disease Control (CDC). (2015, February 1). Hospitalization for Total Hip
Replacement Among Inpatients Aged 45 and Over: United States, 20002010.
Retrieved from CDC: https://www.cdc.gov/nchs/data/databriefs/db186.htm
Holick MF, S. E. (2005). Prevalence of Vitamin D inadequacy among postmenopausal North
American women receiving osteoporosis therapy. Pub med.
Odawa F, O. S. (2004). The prevalence of post menopausal osteoporosis in black Kenyan
women. East African Med Journal, 4547.

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