Data Set A - Predictors of Body Fat

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Data Set A  - Predictors of Body Fat – January Version

The percent of a person’s body that is fat is a matter of concern for health and fitness. According to NHS (2023), obesity is a term described for people who have excess body fat. The risks of living with obesity include:

· type 2 diabetes

· coronary heart disease

· some types of cancer, such as breast cancer and bowel cancer

· stroke

However, percentage bodyfat is expensive and difficult to measure accurately.

To check if a patient is a healthy weight, a widely used method is to calculate BMI. For most adults, if your BMI is:

· below 18.5 – you're in the underweight range

· 18.5 to 24.9 – you're in the healthy weight range

· 25 to 29.9 – you're in the overweight range

· 30 to 39.9 – you're in the obese range

· 40 or above – you're in the severely obese range

According to Dagan et al. (2013), waist circumference has a strong correlation with BMI. A health agency wishes to explore further the suggestion made by Dagan et al. (2013) to decide on guidance on easier methods for patients to self-check their health.

From table 1 (DASL, 2023), we have the following variables.

· Density

· Pct. BF = Percentage Body Fat (%)

· Age=Age (years)

· Weight= Weight (lbs)

· Height=Height (Inches)

· Neck=Neck circumference (cm)

· Chest=Chest circumference (cm)

· Abdomen=Abdomen circumference (cm)

· Waist=Waist Circumference (cm)

· Hip=Hip circumference (cm)

· Thigh=Thigh circumference (cm)

· Knee=Knee circumference (cm)

· Ankle=Ankle circumference (cm)

· Bicep=Bicep circumference (cm)

· Forearm=Forearm circumference (cm)

· Wrist=Wrist circumference (cm)

· BMI

Using the data collected in table 1 the health agency wants you to investigate the following.

1. Build three simple linear regression models to predict BMI with waist circumference, bodyfat percentage, and neck circumference.

2. Of the three variables modelled, which has the most impact on BMI (if there is any difference)?

3. Is there a significantly difference of BMI for a person under 40 compared with a man over 40?

Reference list:

Dagan, S.S., Segev, S., Novikov, I. et al. (2013). Waist circumference vs body mass index in association with cardiorespiratory fitness in healthy men and women: a cross sectional analysis of 403 subjects. Nutr J 12, 12 https://doi.org/10.1186/1475-2891-12-12

DASL (2023). Bodyfat. https://dasl.datadescription.com/datafile/bodyfat/?_sfm_methods=Regression&_sfm_cases=4+59943

NHS (2023). Obesity. https://www.nhs.uk/conditions/obesity/

Task Brief:

This is the assessment brief for the statistical analysis report, which is worth 50% of your overall grade for this course.

Upon successful completion of this assessment, you will be able to:

· LO1: Critique original research data sets relevant to their field of study selecting appropriate statistical methods

· LO2: Discuss the relevance, validity, and reliability of statistical methods in the context of experimental design

· LO3: Evaluate and interpret scientific information and data, both qualitative and quantitative, relevant to applications of their subject area

This assessment will require you to analyse and evaluate data and present the data, conclusions, and any future recommendations in the form of a report.

There is no single or best solution for this assessment, and you are to develop your own strategy in tackling the report.

An exemplar will be provided for to help you with formatting.

Any calculations must only use the methods and techniques described to you on this course (Theme 1-8).

Microsoft Excel must be used for the statistical analysis you will perform below. Use of other software is not permitted.

The report should include the following elements.

1. Introduction

In this section of the report, you should identify the purpose of research or experiment.  You should make sure to explain why this report should be read and the questions you wished to be answered.

2. Experimental Design

In this section, you should identify and state explicitly:

· the factors

· treatment

· Any lurking variables

Using the data you have chosen; you should use an appropriate random sampling method to choose a sample of size 50.

From the data provided and sample selected, you must highlight any:

· possible errors in measurement

· outliers

You must describe their effect on your conclusions.

You should select and justify what statistical methods you will use to identify any statistical links between the variables in the data, and to link this with the questions you wish to answer.

3. Results

Here you present your main findings from your statistical calculations and graphs. This section should only include numerical and graphical outputs without an analysis or discussion of what these outputs might mean.

Any calculations and graphs used in this report must follow the methods and techniques described to you on this course (Theme 1-8).

· Make sure to only include main results that help answer the questions you wish to find answers to.

· Stay away from reporting results that do not help you answer your question.

· Makes sure any descriptive statistics and numerical calculations are presented in clearly labelled tables.

· Appropriate charts and graphical summaries should be used to show patterns in the data or links between the variables.

4. Discussion and Findings

In this section, you shall provide your analysis and define your results in context of the scenario you have chosen. You should make clear what the answer to your research question is and any future recommendations.

You should consider the following to help with your analysis:

· Use appropriate descriptive statistics to compare treatments

· Analyse and interpret the numerical calculations, commenting on how the collection method of the data and the quality of the data effects the validity of the results.