University of the Punjab.

Overview

1. Introduction

What is statistics? Nature and subject matter. The role of statistics in managerial decision making. Descriptive vs. inferential statistics.



2. Graphical Descriptions of Data

Graphical methods for describing qualitative data (the bar charts and the pie charts). Graphical methods for describing quantitative data stem and leaf displays and histograms (Application in business and management). Classification into frequency distribution.



3. Numerical Descriptive Measures

Measures of central tendency; Mean, median and mode for grouped and ungrouped data; Various measures of dispersion; Quartile deviation; Mean deviation and standard deviation; Interpreting the standard deviation; Empirical rule and Chebyshev’s theorem; Measures of relative standing, percentiles and z-scores (application in business and economics).



4. Probability

Events sample space and probability; Compound events; Complementary events; Conditional probability; Probabilities of unions and intersections (application in business and economics).



5. Discrete and Continuous Random Variables

Two types of random variables. Probability distributions of discrete random variable. Expected values of discrete random variables. The binomial random variable. Continuous probability distribution. The normal distribution. (Application in business, economics and management)



6. Sampling

Sampling Distributions; Random Sampling; Introduction to Sampling Distributions; Properties of Sampling Distributions; Unbiasedness and minimum variance; The sampling distribution of the sample mean (application in business, economics and management).



7. Estimation

Large sample estimation of a population mean-small-sample estimation of population mean (Application).



8. Hypothesis Testing

Inferences based on a single sample test of hypothesis. The elements of a test of hypothesis large sample test of hypotheses about a population mean. Small sample test of hypotheses about a population mean. (Application in business, economics and management)



9. Inferences Based on Two Samples

Large sample inferences about the difference between two population means. Small sample inferences about the difference between two population means. Paired observations and inference making. (Application in business, economics and management)



10. Sample and Multiple Regression

Probabilistic models; Fitting the model; The method of least squares; Model assumption; Estimation of correlation; The coefficient of determination using the model for estimation and prediction; Simple linear regression, multiple regression analysis; the model and the procedure; Using the model for estimation and prediction (Application in business, economics and management).