Descriptive Statistics: Basic definitions, Measures of central tendency and variation, Chebychev‟s theorem, z-scores, Frequency distribution, Graphical representation of data stem & Leaf and Box Plots, Symmetry and skewness, Quintiles (Percentiles, Deciles & Quartiles)
Probability Theory: Basic definition and rules of probability, Conditional probability & Bayes‟s Theorem, Counting techniques.
Random Variable: Concept of random variable, Discrete & Continuous random variable and its random variable and variance of random variable and their properties.
Discrete & Continuous Probability Distributions: Uniform, Binomial, Multinomial, Hyper geometric, Negative binomial, Geometric, Poisson, Normal & Exponential distributions and their applications.
Sampling Theory: Sampling distribution of mean, t-distribution, and Sampling procedures.
Regression & Correlation: Linear, Exponential and Multiple Regression Models and Multiple Correlation Coefficient, ANOVA.
Statistical Inference: Estimation of parameters such as mean and variance, Classical and Bayesian method of estimation.
Hypothesis Testing: Z-test, t-test, and Goodness of fit test.
