Course Contents

1. Sampling Method and the central limit theorem  Sampling the Population, Advantages of sampling, Probability and Non-Probability Sampling Methods, Sampling error, The Central Limit Theorem, Sampling Distribution of Mean with properties, Sampling Distribution of difference between Means with properties. 2. Estimation and the confidence intervals  Point estimates and confidence intervals, Confidence interval for a known  or a large sample, Confidence interval for an unknown s and a small sample, Finite population correction factor, Choosing an appropriate sample size. 3. One sample test of hypothesis  What is a hypothesis? What is hypothesis testing? Five step procedure for a testing hypothesis. One tailed and two tailed test of significance. Testing of a population mean with a known population standard deviation, Testing of a population mean large sample with an unknown population standard deviation, Testing of a population mean small sample with an unknown population standard deviation, P Value in Hypothesis testing, Type one and type two errors, The power of a test, The significance level. 4. Two sample test of hypothesis  Testing hypotheses about difference between two population means.  Comparing populations with small samples, dependent samples.  Comparing dependent and independent samples. 5. Analysis of variance  F-distribution, comparing two population variances, ANOVA assumptions, ANOVA Test, Inference about pairs of treatment means, two-way analysis of variance. 6. Linear regression and correlation  What is correlation analysis, the coefficient of correlation, the coefficient of determination, testing the significance of correlation coefficients, the regression analysis, The standard error of estimate, difference between confidence intervals and prediction intervals, The relationship between the coefficient of determination and the standard error of estimate. 7. Multiple Regression and Correlation Analysis  Multiple regression analysis, multiple standard error of estimate, Assumptions, Evaluating the regression equation, Analysis of residuals. 8. Non-parametric methods Chi Square Applications  Goodness of fit test, Equal expected frequencies, Unequal expected frequencies, Limitations of Chi Square, Using the goodness of fit test to test for normality.

Course Synopsis

The main objectives of the course are:  To inculcate sound theoretical knowledge of statistics  To teach the students how to use statistical tools in economics

Course Learning Outcomes

After completing the course, it is expected that the student will be able:  to understand the basic concepts of statistics.  to use statistical tools in economics and for analytical purpose.