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About this document
- Pages
- 8 pages
- Price
- $8 one-time, yours to keep
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- Included in every plan
- Format
- Subject
- Environmental Science, Medical, Statistics
- Module
- ENVI 1096
This assignment requires statistical analysis of data from two studies: one on cholesterol levels in heart-attack patients and another on atmospheric pollutants. It guides you through hypothesis testing, choosing statistical tests, and interpreting results. It is for the Research Methods for Postgraduate Students module.
What's inside
- Statistical analysis of cholesterol levels in heart-attack patients
- Hypothesis testing and choosing statistical tests
- Interpreting results of statistical tests
- Statistical analysis of atmospheric pollutants
- Reporting findings
Who it is for: Students taking the Research Methods for Postgraduate Students module. (Postgraduate)
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Summary
Statistical analysis of heart attack and air pollution data tests for changes and relationships in health and environmental data. Students do this to see how variables change or relate to each other over time.
Study one: Cholesterol levels
Cholesterol levels in heart-attack patients were measured on two days to see if they had changed. Correlations and differences between days are calculated to see if there is a significant change.
Null and research hypotheses
The null hypothesis states there is no difference between the means of two related groups. The research hypothesis states that there is a difference.
Statistical tests and assumptions
Tests of normality are carried out to decide if parametric or non-parametric tests should be used. The choice of test depends on whether the data are normally distributed.
Study two: Air pollutants
The relationship between atmospheric pollutants is examined. A scatter plot is used to check for linearity. The Pearson correlation coefficient tests for a significant association.
Key terms
- Null hypothesis
- A hypothesis that there is no difference between two groups.
- Paired samples test
- A statistical test to compare the means of the same group at different times.
- Pearson correlation coefficient
- A measure of the strength and direction of the linear relationship between two variables.
- Normality
- A property of data where the values are symmetrically distributed around the mean.
- Dependent t-test
- A test to see if the means of the same group differ at two different times.
- Scatter plot
- A graph to show the relationship between two variables.
Questions this document helps with
- What are the steps to test for changes in cholesterol levels over time?
- How do you check if data are normally distributed?
- What does a negative correlation between two pollutants mean?
Summary written with AI from the document's text.
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