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Overivew of BioStats, Study notes of Biochemistry

Overivew of BioStats and detailed info

Typology: Study notes

2019/2020

Uploaded on 03/07/2025

ankush-singla
ankush-singla 🇺🇸

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Bio stats assignment
Assignment Title: Exploring the Relationship Between Lifestyle Factors and
Blood Pressure
Introduction:
High blood pressure (hypertension) is a significant risk factor for cardiovascular
disease. This assignment explores the relationship between lifestyle factors (like
diet, exercise, and smoking status) and blood pressure using a provided dataset.
You will use descriptive statistics, hypothesis testing, and potentially regression
analysis to investigate these relationships.
Dataset:
Option 1 (Introductory): A simplified, simulated dataset with variables like:
oSystolic Blood Pressure (mmHg)
oDiastolic Blood Pressure (mmHg)
oAge (years)
oGender (Male/Female)
oSmoking Status (Yes/No)
oExercise (Hours per week - categorized as <3, 3-5, >5)
Option 2 (Intermediate/Advanced): A publicly available dataset (e.g., from
NHANES or Kaggle) related to cardiovascular health. This might include
more variables like BMI, cholesterol levels, dietary intake (e.g., sodium), and
other relevant health indicators. Consider the Framingham Heart Study data
as a good option.
Tasks:
1. Descriptive Statistics:
oCalculate descriptive statistics (mean, median, standard deviation,
quartiles) for systolic and diastolic blood pressure. Stratify these
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Bio stats assignment

Assignment Title: Exploring the Relationship Between Lifestyle Factors and Blood Pressure Introduction: High blood pressure (hypertension) is a significant risk factor for cardiovascular disease. This assignment explores the relationship between lifestyle factors (like diet, exercise, and smoking status) and blood pressure using a provided dataset. You will use descriptive statistics, hypothesis testing, and potentially regression analysis to investigate these relationships. Dataset:Option 1 (Introductory): A simplified, simulated dataset with variables like: o Systolic Blood Pressure (mmHg) o Diastolic Blood Pressure (mmHg) o Age (years) o Gender (Male/Female) o Smoking Status (Yes/No) o Exercise (Hours per week - categorized as <3, 3-5, >5)  Option 2 (Intermediate/Advanced): A publicly available dataset (e.g., from NHANES or Kaggle) related to cardiovascular health. This might include more variables like BMI, cholesterol levels, dietary intake (e.g., sodium), and other relevant health indicators. Consider the Framingham Heart Study data as a good option. Tasks:

  1. Descriptive Statistics: o Calculate descriptive statistics (mean, median, standard deviation, quartiles) for systolic and diastolic blood pressure. Stratify these

statistics by relevant categorical variables (e.g., smoking status, exercise category, gender). o Create appropriate visualizations (histograms, box plots, scatter plots) to explore the distribution of blood pressure and its relationship with other variables. Explain what these visualizations tell you.

  1. Hypothesis Testing: o Choose TWO of the following hypotheses (or develop your own with instructor approval):  Is there a statistically significant difference in mean systolic blood pressure between smokers and non-smokers?  Is there a statistically significant difference in mean diastolic blood pressure between individuals who exercise regularly (e.g., >5 hours/week) and those who do not?  Is there a correlation between age and systolic blood pressure?  Is there a statistically significant difference in blood pressure between men and women? o For each chosen hypothesis:  State the null and alternative hypotheses.  Choose an appropriate statistical test (t-test, ANOVA, correlation, etc.) and justify your choice.  Perform the test and report the p-value.  Interpret the results in the context of the research question. What conclusions can you draw?
  2. (Optional - More Advanced): Regression Analysis o Build a multiple linear regression model to predict systolic blood pressure based on several predictor variables (e.g., age, BMI, smoking status, exercise).

Interpretation: Emphasize the importance of interpreting statistical results in the context of the research question and avoiding over-interpretation.