Pre-requisite: None
Course Content:
Descriptive Statistics: Definition of basic statistical terminologies, application of statistics in real world, types and sources of data, scales of measurement of data, variables, methods and tools of data collection, organization and presentation of data, sample statistics and population parameters, summary statistics (numeric and graphical) for continuous and categorical data, measures of symmetry and skewness.
Sampling and Sampling Techniques: Definition of terms, probability sampling techniques: simple random sampling, systematic sampling, stratified sampling, and cluster and non-probability sampling: convenience/accidental sampling, judgemental/purposeful sampling and quota sampling
Simple Linear Regression and Correlation: Concepts and assumptions of linear regression, fitting least-squares regression line and interpretation of regression coefficients. Constructing confidence intervals and testing hypothesis for the constant and slope parameters, coefficient of determination, adjusted coefficient of determination; Correlation analysis.
Elementary Probability Theory: Definition of key terms, applications of probability in real world, axioms of probability, rules of probability: law of total probability, Bayes’ rule, additional rule, multiplication rule, independent and mutually exclusive events, permutations and combinations.
Random Variables and Probability Distributions: Random variables, types of random variables, expected and variance of random variables, probability distributions of discrete random variables and its properties e.g., Poisson, Binomial; probability distribution of continuous random variables e.g., normal; applications of probability distributions of random variables.
Sampling Distributions: Sampling distributions of sample statistics e.g., z-distribution, t-distribution, Chi-square distribution and F-distribution.
Statistical Inference
Estimation Theory: Point and interval estimators, point and interval estimates, confidence intervals for single, and two population means, proportions and variance, interpretation of confidence interval.
Hypothesis Testing: Elements of hypothesis testing; null and alternative hypotheses, significance level, test statistics, construct critical region or decision rule, conclusion, Type I and Type II errors, one-tailed and two-tailed tests, Tests of means, e.g., one sample t-test, independent samples t-test, and paired samples t-test.
Learning outcomes:
By the end of the course, students are expected to be able to: