1. Introduction to Statistics teaching hours: 3 hrs
Meaning, Scope and Limitations of Statistics, Types and Sources of Data, Methods and Problems of Collection of Primary and Secondary Data.
2. Descriptive Statistics teaching hours: 6 hrs
Measure of Central Tendency (Arithmetic Mean, Median, Partition Values, Mode); Measure of Dispersion(Absolute and Relative Measures Range, Quartile Deviation, Mean Deviation, Standard Deviation and Coefficient of Variation)
3. Correlation and Regression Analysis teaching hours: 3 hrs
Correlation: Definition, Scatter diagram, Karl Pearson's coefficient of correlation, Numerical problems for determination of Correlation Coefficients.
Regression: Definition, Dependent and Independent Variables, Least Square nethod only, Numerical Problems.
4. Probability teaching hours: 8 hrs
Definition of Probability, Two basic Laws of Probability( without proof), Conditional Probability; Probability Distributions(Binomial, Poisson and Normal); simple numerical problems.
6. Sample Survey teaching hours: 6 hrs
Concept of Population and Sample; Needs of Sampling; Censuses and Sample Survey; Basic Concept of Sampling; Organizational Aspect of Sample Survey; Questionaire Design: Sample Selection and Determination of Sample Size; Sampling and Non Sampling Errors.
6. Sample Survey Methods teaching hours: 10 hrs
Types of Sampling: Simple Random Sampling with and without Replacement; Stratified Random Sampling; Ratio and Regression Method of Estimation under Simple and Stratified Random Sampling; Systematic Sampling; Cluster Sampling; Multistage Sampling; Probability Proportion to Size Sampling (PPS), Estimation of Population Total and its Variance, Sampling Distributions(t, x2, z) and Related Problems.
7. Design of experiment teaching hours: 6 hrs
Concept of Analysis of Variance(ANOVA), F-Statistic and its Distribution, Linear Model in ANOVA, Analysis of One Way, Two Way Classification(I and m observations per cell)in Fixed Effect Model.
Techniques for using the computer as a tool in the analysis of statistical problems will be introduced. SPSS software should be used for data analysis