Describe the rationale of a significance test
Define Alpha and Beta errors
Calculate the Power of a study
14.
Apply various tests of significance: their rationale and use.
15.
Calculate Confidence Intervals
16.
Explain the meaning of ‘p’ in statistical terms and its interpretation.
17.
Apply the steps of Hypothesis testing
Choosing an appropriate test of significance
Use the tests of significance for parametric data: for a single mean, for two means of unpaired
observations, two means of paired observations, three or more independent means (ANOVA).
Use the tests of significance for categorical data: for one proportion, two independent
proportions, two paired proportions, several proportions, analyzing frequency tables (2x2, 2xk
tables), large tables with ordered categories.
18.
Identify when and where to use non-parametric tests for a single or more than one samples e.g.
Wilcoxon’s Rank sum tests, Mann-Whitney U-tests etc.
19.
Investigate the association between two continuous variables: using a scattergram to:
Identify dependent and independent variables
Apply correlation–calculate correlation coefficients,
Interpretation and presentation of correlation.
20.
Investigate the relationship of two continuous variables using regression, calculating linear
Regression of y on x and draw line of regression, interpreting and presenting regression.
When to choose regression or correlation?
Contents:
The following are the contents of the course:
1.
Introduction to Statistics
2.
Types of statistical applications
3.
Data presentation: Figures, graphs, tables
4.
Variables
5.
Scales of measurements
6.
Descriptive Statistics:
7.
Measures of central tendencies
8.
Measures of variability
9.
Measures of shapes
10.
Probability:
11.
Probability Distributions: Normal, Poisson, Binomial
12.
Sampling techniques, sampling errors/ Confidence Intervals
13.
Concepts of analytical statistics: Hypothesis testing:
14.
Alpha and Beta errors
