How to construct various control charts Which control charts to use for a particular type of data How to measure the capability of a process The basic themes of quality management and Deming’s 14 points The basic aspects of Six Sigma

Demand Management Qualitative Forecasting Methods Time Series Analysis – Static method – Adaptive method Simple & Weighted Moving Average Forecasts Exponential Smoothing Exponential Smoothing with trend Exponential Smoothing with trend & seasonality Simple Linear Regression

To use regression analysis to predict the value of a dependent variable based on an independent variable The meaning of the regression coefficients bo and b . To evaluate the assumptions of regression analysis and know what to do if the assumptions are violated . To make inferences about the slope and correlation coefficient . To estimate mean values and predict individual values

 Recognize situations in which to use analysis of variance  Understand different analysis of variance designs  Perform a single-factor hypothesis test and interpret results  Conduct and interpret post-hoc multiple comparisons procedures  Analyze two-factor analysis of variance tests

 Test hypotheses for the difference between two independent population means (standard deviations known or unknown)  Test two means from related samples for the mean difference  Complete a Z test for the difference between two proportions  Use the F table to find critical F values  Complete an F test for the difference between two variances

 Formulate null (H0) and alternative hypotheses (H1) for applications involving one sample population mean or proportion  Formulate a decision rule for testing a hypothesis  Know how to use the critical value and p-value approaches to test the null hypothesis (for both mean and proportion problems)  Know what Type I and Type II errors are

Chapter 7 Confidence Interval Estimation

To compute probabilities from the normal distribution. To compute probabilities from the uniform distribution. To compute probabilities from the exponential distribution

Basic probability concepts  Sample spaces and events, simple probability, joint probability Conditional probability  Statistical independence, marginal probability Bayes’s Theorem

To describe the properties of central tendency, variation and shape in numerical data To calculate descriptive summary measures for a population To construct and interpret a box-and-whisker plot To describe the covariance and coefficient of correlation as a measure of the strength of the relationship between two numerical variables