The design of a sample is one of the most important aspects of any survey: no amount of statistical analysis can compensate for a badly-designed sample. Therefore, the emphasis of this course is the scientific design of samples, determination of sample sizes and is related to methods for analysing the data from a survey. Contents include: Questionnaire design,sampling techniques (simple random, stratified, systematic, cluster, complex), proportional vs disproportional allocation for stratified sampling, ratio and regression estimation, estimation of means, totals proportions and their variances, weighting of survey data, dealing with non-response. Sampling techniques covered will include simple random sampling, stratification, cluster sampling, systematic sampling, multistage sampling, probability proportional to size sampling, as well as noting several non-probability methods possibly used in very small or hard-to-find populations. Applications of these methods for a variety of survey designs will be discussed, and cost, sampling frames, and sampling error estimation techniques will also be addressed.
- Teacher: Alphonse KARAMA
- Teacher: Venuste NTAKIRUTIMANA
This module introduces basic concepts of probability, random variables and probability distributions. It aims to equip the students with a basic knowledge in probability theory and its applications.
Having successfully completed the module, students should be able to
i. Demonstrate understanding of the main concepts and definitions in set theory and functions
ii. Demonstrate an understanding of the axiomatic foundations of probability
iii. Understand the concept of a random variable and show familiarity with common distributions
The module has two units and the following are their respective contents
Unit I Introduction to probability
- Definition of key terms in probability
- Axioms of probability
- Types of probability
- Law of Large Numbers
- The Addition Rules for Probability
- Counting Rules
- The multiplication rule for probability
- Probabilities for “At Least"
- Conditional probability
- Law of total probability
-
Bayes Rule
Unit II. Probability distributions
- Random Variables
- Mean and Variance of a Random Variable
- Moment generating function
- Commonly used probability distribution (Discrete probability distribution)
- Uniform Distribution
- Bernoulli distribution
- Binomial Distribution
- Poisson distribution
- Commonly used probability distribution (Continuous distribution)
- Uniform distribution
- Normal distribution
- Exponential distribution
- Teacher: Venuste NTAKIRUTIMANA
The module investigates concepts of: Econometrics: Econometrics methodology, Correlation and regression analysis, Error terms, Simple linear regression, Multiple linear regression , Assumptions of CLRM (Classical Linear Regression Model), Modelling techniques with E-views and SPSS.
- Teacher: Venuste NTAKIRUTIMANA