Unit I : Sampling Theory and Estimation
Sampling theory – population – finite and infinite population – parameter – sample – statistic – sampling – need for sampling – probability sampling – simple random sampling (SRS) – methods of selection of SRS – lottery method and random number table method – non–probability sampling – purposive and judgment sampling – sampling distributions – standard error and its uses – Estimation theory – estimate – estimator – types of estimation – point estimation – properties of good estimators – unbiasedness, consistency, efficiency and sufficiency – interval estimation – confidence limits – confidence interval.
Unit II : Test of Hypothesis
Test of significance – null and alternative hypothesis – Type I and Type II errors – critical region – level of significance – degrees of freedom – large sample test – tests for significance of means and proportions for large samples – small sample test – t–test – testing the significance of single mean – testing the significance of two means for independent and paired samples – F–test for equality of two variances – test for goodness of fit and test for independence of attributes – test for equality of several variances (Bartlett’s test).
Unit III : Correlation Analysis
Bivariate distribution – simple correlation – meaning – assumptions – positive and negative correlation – scatter diagram – computation of correlation coefficient – properties of correlation coefficient – testing and interpretation of correlation coefficient – coefficient of determination – Fisher’s Z–transformation – testing several correlation coefficients – Spearman’s rank correlation (with and without ties).
Unit IV : Simple Linear Regression Analysis
Regression – simple linear regression – meaning – assumptions – fitting of simple linear regression equation of y on x – properties of regression coefficient – testing and interpretation of regression coefficient and intercept.
Unit V : Multiple Linear Regression Analysis
Multiple linear regression – assumptions – difference between simple and multiple linear regression – standardised and partial regression coefficients – fitting of multiple linear regression equation – testing the regression coefficients – interpretation of regression coefficients – multiple correlation – coefficient of multiple determination (R2) – interpretation of R square – selection of variables– step wise regression approach – multi-collinearity – applications of dummy variables.