Topic outline

  • COURSE DESCRIPTION

    StatbisThis course prepares the program participants on the use of statistics in assisting managerial decision making in the business world. Specific topics to be covered include techniques of counting, probability theory and elements of statistics including binomial and normal distributions, descriptive statistics, sampling distributions, statistical inferential procedures for population parameters, simple linear regression and correlation analysis and time-series forecasting.

  • Topic 1

    Introduction
    How statistics is used in business The sources of data used in business The types of data used in business The basics of Microsoft Excel

  • Topic 2

    Organizing and Visualizing Data
    Developing tables and charts for categorical data Developing tables and charts for numerical data The principles of properly presenting graphs Examination of cross tabulated data using the contingency table and side-by-side bar chart

  • Topic 3

    Numerical Descriptive Measures
    Measures of central tendency, variation, and shape Population summary measures Five number summary and Box-and-Whisker plots Covariance and Coefficient of correlation

  • Topic 4

    Basic Probability

    How to define and examine basic probability concepts Define conditional, joint and marginal probability To use Bayes' theorem to revise probabilities Statistical Independence

  • Topic 5

    Discrete Probability Distributions
    Addressed the probability of  a discrete random variable Define covariance and discuss its application in finance To compute probability from the binomial, Poisson and Hypergeometric distribution How to use this distribution to solve business problem

  • Topic 6

    The Normal Distribution & Other Continuous Distributions
    Define continuous distribution: normal, uniform and exponential Probabilities using formulas and tables The concept of the sampling distribution The importance of the Central Limit Theorem Examine when to apply different distributions

  • Topic 7

    Sampling and Sampling Distributions
    To construct and interpret confidence interval estimates for the mean and the proportion How to determine the sample size necessary to develop a confidence interval for the mean or proportion How to use confidence interval estimates in auditing

  • Topic 8

    Confidence Interval Estimation
    The basic principles of hypothesis testing How to use hypothesis testing to test a mean or proportion The assumption of each hypothesis-testing procedure, how to evaluate them and the consequences if they are violated Formulate a decision rule for testing a hypothesis Know Type I and Type II errors

  • Topic 9

    Fundamentals of Hypothesis Testing: One-Sample Tests
    Use hypothesis testing for comparing the difference between: The means of two independent populations The means of two related populations The proportions of two independent populations The variances of two independent populations

  • This topic

    Topic 10

    Two-Sample Tests
    The basic concepts of experimental design How to use the one-way analysis of variance to test for the differences among the means of several groups How to use the two-way analysis of variance and interpret the interaction

  • Topic 11

    Analysis of Variance
    How and when to use the chi-square test for contingency tables How to use the Marascuillo procedure for determining pair-wise differences when evaluating more than two porportions How and when to use the McNemar test How and when to use nonparametric tests

  • Topic 12

    Chi-Square Tests and Nonparametric Tests
    Using regression analysis to predict the value of a dependent variable based on an independent variable The meaning of the regression coefficients b0 and b1 Evaluating the assumptions of regression analysis and know what to do if the assumptions are violated Making inferences about the slope and correlation coefficient Estimating mean values and predict individual values

  • Topic 13

    Simple Linear Regression
    How to develop a multiple regression model How to interpret the regression coefficients How to determine which independent variables are most important in predicting a dependent variable How to use quadratic terms in a regression model How to measure the correlation among independent variables

  • Topic 14

    Introduction to Multiple Regression
    Discussed the important of forecasting Performed smoothing of data series Described least square trend fitting and forecasting Addressed time series forecasting Addressed autoregressive models  Described procedure for choosing appropriate models