The design of a sample is one of the most important aspects of any survey: no amount of statistical analysis can compensate for a badlydesigned 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 nonresponse. 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 nonprobability methods possibly used in very small or hardtofind 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 Eviews and SPSS.
 Teacher: Venuste NTAKIRUTIMANA