Data is an essential commodity and various organizations today unlock data to allow them to make business decisions that are highly informed. Data in open source has become highly available and U.K Government has a wide range of available open data to analyse. The paper of this report lies in information extraction from data sets of health for supporting development for wide range of food products that are healthy. The scope of this paper lies in analysing and extracting information from distinct data sets using a specific tool of data analytics that is either SAS JMP or SAS Enterprise guide or base SAS. After this analysis, results for the data will be analysed for showing the requirement for a wide range of food products that are healthy.

1. OPEN DATA ANALYSIS USING
BUSINESS INTELLIGENCE
TOOLS
Public health: Obesity, Physical activity and diet
[DATE]
[COMPANY NAME]
[Company address]

2. 1. Executive Summary
Data is an essential commodity and various organizations today unlock data to allow them to
make business decisions that are highly informed. Data in open source has become highly
available and U.K Government has a wide range of available open data to analyse. The paper
of this report lies in information extraction from data sets of health for supporting
development for wide range of food products that are healthy. The scope of this paper lies in
analysing and extracting information from distinct data sets using a specific tool of data
analytics that is either SAS JMP or SAS Enterprise guide or base SAS. After this analysis,
results for the data will be analysed for showing the requirement for a wide range of food
products that are healthy.

3. 2. Introduction
Organizations in the public sector do not compete with each other but they are bound to face
pressure of unprecedented nature when providing citizen based services and elements in a
manner that is highly cost effective, transparent and efficient. As a matter of fact, it is
expected that these organizations will be more focused on the customer and will respond to
the needs of the stakeholders in a shrinking budget climate (Health at a glance 2013).
Because of this, agencies in public sector just like those in the private sector are trying to
adopt new behavioural model wherein agencies are needed to maintain immense data amount
collected everyday so that decisions are made just in time. Business Intelligence and its tools
in this regard are a boon to both organizations, in the public as well as private sector as BI
along with its tools allows to maintain and leverage data.
The data which has been analysed in this paper is taken from:
https://data.gov.uk/dataset/statistics_on_obesity_physical_activity_and_diet_england and is
statistics on obesity, physical activity and diet, England. The data analysis will be crucial for
health sector organizations as the statistics reveal a wide range of data on diet, obesity and
physical activity collaborated together from several sources. Furthermore, the statistics over
overweight, obesity prevalence, levels of physical activity in children and adults, purchase
and consumption of food as well as energy intake along with overweight health outcomes.
3. Methodology
This report takes up statistics analysis to assess obesity, physical activity and diet among
different population groups in the UK. Presently, base SAS has various regression methods
having the capability to perform regression stepwise. These procedures are inclusive of REG,
LOGISTIC, GLMSELCT and PHREG. Regression of stepwise nature is a variable used
widely for selecting a method applicable for any predictive model of building process. This is
a collaboration of backward elimination and forward selection (Licktenstein et al 2006).
Every step is inclusive of both selections wherein variables are regarded to be added to model
along with eliminating backward that is responsible for examining variables to be removed
from model. The variable’s F statistics to be added in the model should be essential the level
of SLENTRY. The phase of forward selection is altered in the perspective that already
variables within the model do not significantly remain. One backward elimination step

4. removes the variable then it remains to be as such. A step of forward selection can be
followed through the step of backward elimination.
Quality of variables is provided using Qualitative research analysis and it is impossible for
inquiring on the human behaviour quality and offering full-fledged analysis. Quantitative
research is employed to empirically investigate through use of statistical methods.
Some of the data as revealed by the statistical data report was obtained from the records of
Health and Social care Information centre which in turn was collected by performing data
collection survey on healthcare institutes of UK. There are data sets involved in the study
about previously not published figures regarding Finished Consultant Episodes for 2013-2014
that have been provided by using HSCIC statistics on hospital episode along with HSCIC
prescribing unit database on prescription drugs that are given to treat the condition of obesity.
Datasets are analysed by 2 ways in this report (Health survey for England 202). Firstly by
using descriptive statistical analysis and secondly by using correlation analysis as is required
to analyse the different variables in the report. The basic feature of data is explained using the
first analysis that explains 2 important measures regarding statistics, for example central
tendency measure along with dispersion technique. Mean, median and mode are essential
parameters of central tendency with mean being the large dataset average number. Other
parameters are covered by second measure inclusive of range, deviation on standard basis as
well as varying factors.
In order to analyse using the 2nd
type of analysis, bivariate and univariate correlation
estimates ae used with several kinds of relationships essential for analysing the variables
relationship with each other and against each other.
4. Describing data sets
Obesity, diet and physical activity data sets are collected from UK government statistical
health database. This is because obesity is a growing concern among the population groups in
UK. BMI or Body Mass Index is regarded as the best way by which obesity incidences can be
created and critiqued. The selected database utilizes BMI to estimate obesity and the
relationship that most overweight individuals depict with physical activity and diet (such as
not doing any exercise or eating a lot of junk food). In order to calculate BMI, weight along
with double times the height is divided. Depending upon the ratio estimate, the data set
described the comparative scale of weight enhancement in people in UK over a period of 1

5. year only (Health at a glance 2013). The measurements of weight from BMI helped to offer
recommendations along with revealing the mistake that most individuals commit regarding
diet and inadequate physical activity. The data sets were divided over 6 chapters wherein
Chapter 2 to 6 covered overweight, physical activity performed along with the diet by
maintaining important links to every statistics section. The last chapter described data sets
related to health outcomes presenting wide range of data on obesity health outcomes which is
inclusive of health risks, admission in health institutes and drugs prescription applied for
treating obesity condition. The data sets were distributed only over a period of 1 year along
with a comparison with the data sets in 2006 to 2007. For example, data sets revealed that in
2013, few men compared to women were consuming the recommended fruit portions and
vegetables. Same proportion of girls and boys also engaged in consuming more than 5
portions every day.
Furthermore of importance were the data sets regarding Finished admission episodes in
National health care institutes with obesity as primary diagnosis for approximately 9325
hospitals.
5. Analysis
Descriptive Analysis for statistics
This descriptive analysis revealed the statistical details about Physical activity. Adult’s
physical activity 4 weeks before the interview was examined by considering the overall
activities self-reported participation and through description of participation frequency and
activity type. From the descriptive analysis of datasets it was evident that the present
guidelines for aerobic activity recommends that adults 19 and plus should spend at the least
150 minutes every week engaging oneself in moderate intensive physical activity and
vigorous activity should be around 75 minutes every week. The participant’s proportion to
meet the present aerobic activity UK guidelines increased as there was increase in equivalent
amount of household income. 76 percent of men and women by 63 percent are highest
income achieving quintile for meeting newer guidelines. In physical activity descriptive
statistics the home environment was considered as an essential component:

6. From this perspective, the characteristics of the participants using descriptive statistics was
revealed:

7. The descriptive statistics illustrated the use of gender, age and education level in order to
analyse whether knowledge and attitude regarding obesity and its implications are known to
the general population groups or not. This was used to establish the association that is
correlation existing between the environment with home and food, the physical activity and
diet in relation to the sedentary behaviours.
Most of the information used in this data set came from 3 main national surveys inclusive of
living costs and food survey, national diet and nutrition survey and England’s health survey.
Trends in purchase revealed the diet pattern for adults in UK.
Overall percentage regarding purchase of vegetables and fruit decreased from a period of
2010 to 2013 with customers spending more on fresh and processed vegetables as well as
fresh and processed fruits.

8. Correlation
Physical activity
With regard to Physical activity, there existed a clear correlation between aerobic activities
guidelines being met and the category of body mass index wherein men around 75 percent
were those not overweight in comparison with overweight men of 71 percent. The women
equivalent figures revealed 64 percent and 58 percent. Intake of energy from fast food was
directly correlated with the physical activity related attitude and perception as well as
knowledge.
Higher relationship furthermore was evident between prevailing obesity and diseases selected
which was evident from drugs prescribed for example high blood pressure through gender
and Body Mass Index, 2014 as given below:

9. There was a direct correlation found to exist between those who are overweight and their
Basal metabolic index. The pattern was same for females and males revealing a higher blood
pressure for those with raised circumference of their waist.
Also the statistics revealed that there was a correlation between environment and the
measurement of BMI, age, weight at birth, gender as well as level of education

10. Bases on the purchase of food and drink, the total intake of energy per individual was 4.1
percent low in 2012 when compared with that of 2008. This was a significant statistical
downward correlation throughout a period of 4 years confirming the long term downward
correlation apparent. Total intake of energy was considered at an average of 2209 kcal per
individual each day in the year 2012.
Health Outcomes as per the correlational analysis indicated that there is a strong positive
correlation between scores of obesity and negative health outcomes.
The following graph depicts Spearman’s rho correlation coefficients inclusive of a being the
correlation that exists between physical activity of habitual nature and energy density on an
average along with dietary fat percent saturated through visiting year and b is the correlation
between habitual fibre intake and physical activity. Correlation is essential to be considered
over a period of time to gain comparative results successfully from the analysis being done:

11. The following is canonical correlational analysis to evaluate health attitudes and obesity
correlated with composition of body. The variance percent is explained through initial indices
and initial indices standardization for canonical co-efficiency.
Here significance is given at P is lesser than 0.1 and significance at P is less than 0.05 for
dependant significance variates evaluation with univariate association test along with initial
correlation analysis. It was evident that the relationship existing between variables in the
groups was different from an year to the other but the correlation existing between the
population groups that is obesity correlates as well as composition of body decreased with
every visit to the health care with decrease in correlation with regard to linear trends for
combined data wherein R2 =0.72. This revealed a reduction related to age in relationship
lying between correlates of obesity and composition of body.

12. 6. Summary and Conclusion
According to the report of McKinsey Global Institute, 2014, obesity is the second prominent
issue influencing UK individuals health with smoking being the first and the issue costs
population groups across UK 45 billion dollars as of 2014. Therefore, a major role is played
by this report as it illustrates the findings by analysing through use of statistical techniques
and descriptive variable datasets regarding this health issue (Health at a glance 2013). There
exists a direct relationship as per the analysis in this report, between physical activity, obesity
and diet intake. These three are inter-related in a way that the deformities in any one of the
mentioned issues results in deforming the whole balance of health with regard to an
individual. The data results revealed that even though only over a period of 1 year but the
condition of obesity is still growing. The relationship is interrelated in a way that the statistics
revealed a conformity. Obtaining data was extremely easy as available on the government site
along with being sufficient to reveal the true status that the objective of the study maintained.
7. References
Health at a Glance, 2013, OECD Indicators. Organisation for Economic Co-operation and
Development, November 2013. http://www.oecd.org/health/health-systems/health-at-a-
glance.htm
Health Survey for England, 2012, Child Trend Tables. The Health and Social Care
Information Centre, 2013. http://www.hscic.gov.uk/catalogue/PUB13219
Lichtenstein, A.H., Appel, M. Brands, L.J., et al., 2006, Diet and lifestyle
recommendations revision 2006: a scientific statement from the American Heart
Association Nutrition Committee,” Circulation, vol. 114, no. 1, pp. 82–96