1. Probability distributions
(i) Discrete distributions - Binomial, Poisson
(ii) Continuous distributions - Uniform, Exponential, Normal, LogNormal
2. Sampling Methods and Sampling Distributions
(i) Statistics and Parameter
(ii) Types of sampling - random and non-random sampling
(iii) Sampling distributions - conceptual basis; standard error; sampling from normal populations;
Central Limit Theorem; relationship between sample size and standard error; Finite
Population Multiplier
3. Estimation
(i) Point Estimation – properties of estimators; the method of moments and the method of
maximum likelihood
(ii) Interval Estimation – basic concepts; interval estimates and confidence interval; calculation
of interval estimates of mean and proportion from large samples; interval estimation using
the t distribution; determining the sample size in estimation
4. Hypothesis Testing
(i) Basic Concepts – Null and Alternative Hypotheses; Type I and Type II errors; the p – value;
the significance level; power of a test
(ii) One Sample Tests – hypothesis testing of means when the population standard deviation is
known and when it is unknown; hypothesis testing of proportions for large samples
(iii) Two Sample Tests – tests for difference between means – large sample sizes and small
sample sizes; test for difference between proportions – large sample sizes; testing difference
between means with dependent samples
5. Chi–square and Analysis of Variance
(i) Chi-square as a test of (a) independence and (b) goodness of fit
(ii) ANOVA – basic concepts; the F distribution and the F statistic; inferences about a population
variance; inferences about two population variances
6. Non-parametric tests
(i) Basic concepts
(ii) The Sign Test
(iii) The Signed-Rank Test
(iv) Rank Sum Tests – The Mann-Whitney U Test; The Kruskal-Wallis Test
(v) Tests based on runs
(vi) Rank Correlation
(vii) Kolmogorov-Smirnov Test
7. Time series and Forecasting
(i) Variations in time series; trend analysis; cyclical, seasonal and irregular variations;
consideration of all four components of a time series
(ii) Time Series analysis in frecasting
8. Multivariate data analysis (demonstration of software package)
(i) Basic concepts
(ii) Types of multivariate techniques
(iii) Factor Analysis
(iv) Multiple Regression Analysis
(v) Discriminant Analysis
(vi) Cluster Analysis