In this module a range of statistical topics are introduced and their applications are explored. Emphasis is placed on application.
Graphic and Tabular Descriptive Techniques: Types of data. Tabulation. Charts and graphs.
Summary statistics: Measures of Central Tendency, Location and Dispersion. Shape of the data distribution, skewness and kurtosis. Geometric mean.
Data Collecting and Sampling: Methods of data collection, sampling.
Probability: Basic probability, posterior probabilities, marginal, joint and conditional probabilities, Bayes’ theorem, decision trees.
Probability Distributions: Discrete and continuous probability distributions, expected values and variance of discrete probability distributions, covariance between two distributions.
Statistical inference: Point and interval estimates. Hypothesis testing for single mean and proportion, testing for difference between two means and proportions using normal and t-distributions. Chi square tests for categorical data and goodness of fit tests.
Analysis of Variance: One-Way and Two-way analysis of variance. F-test
Regression and correlation: Ordinary least squares, linear and log-linear functions. Testing the parameters of a regression line. Prediction. Goodness of fit of model. Pearson product-moment and rank correlation coefficients. Spurious correlation.
Time-series data: Time-series analysis using Additive and Multiplicative models. Finding the trend by moving averages and regression. Seasonal variation and deseasonalisation of data. Residual variation. Forecasting. Price and quantity index numbers. Composite indexes, fixed base and chain-linked indices. Commonly used indices.
The module is taught through a combination of lecture hours, tutorials, laboratory sessions and on-line resources
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 40 |
| Other Assessment(s) | 60 |