Location

Date

Duration

Language

Discipline

Introduction

This course focuses on the techniques and applications statistical data analysis.  Typically, focuses on understanding the data, empirical model building using observational data for characterization, estimation, inference and prediction.  Participants will study the theory, principles and methods for statistical analysis of observational data. Regression analysis, Parameter Estimation, and Testing of Hypotheses will be the primary tools to be discussed. Participants will develop empirical model building skills and be able to employ the models for characterization, estimation and prediction purposes.

While statistical techniques are emphasized throughout, the course has a strong engineering and management orientation. Guidelines are given throughout the course for selecting the proper type of statistical technique to use in a wide variety of product and non-product situations.

Objective

On successful completion of the course you will be able to:

Audience

Engineers and Senior Engineers/Specialists working in technical areas (Field and Headquarters) dealing with production or maintenance activities. Planning Engineers with technical background, Reliability Engineers, etc… This course is also intended for engineers in various industrial and service sectors, private and public fields that need a tool to plan for the future of their company. Strategic planning managers, research and development managers, general managers, and can be tailored according to company’s specific needs.

Content

The following topics will be covered in 5 days

DAY ONE

INTRODUCTION & BASICS

• Introduction

• Types of Data: Measurement & Categorical Variables

• Measurement scales

• Variables

• Parameters

• Statistics:

o Descriptive Statistics

o Inferential Statistics

• Accuracy And Precision

• Summation Notation

• Confidence Intervals

• Exercises

UNIVARIATE DATA

• Central Tendency

???? Mean

???? Median

???? Mode

• Spread

???? Range

???? Semi-Interquartile Range

???? Variance

???? Standard Deviation

• Shape

???? Skew

???? Kurtosis

• Graphs

• Exercises

BIVARITE DATA

• Scatter plots

• Pearson’s Correlation

• Example Values of r

• Exercises

PROBABILITY

• Simple & Conditional Probability

• Probability of (A and B) and (A or B)

• Binomial Distribution

• Exercises

DAY TWO

NORMAL DISTRIBUTION

• Definition

• Standard Normal Distribution

• Conversion to Percentiles and Back

• Exercises

SAMPLING DISTRIBUTION

• Definition

• Sampling Distribution of the Mean

• Standard Error

• Central Limit Theorem

• Difference Between Means

• Proportion

• Difference Between Proportions

• Exercises

POINT ESTIMATION

• Overview

• Characteristics of Estimators

• Estimation Variance

• Exercises

CONFIDENCE INTERVALS

• Overview

• Mean, σ Known

• Mean, σ Estimated

• General Formula

• Difference Between Means of Independence Groups: σ Known; σ Estimated

• Linear Combination of means from Independent Groups

• Exercises

DAY THREE

LOGIC OF HYPOTHESIS TESTING

• Ruling Out Changes as an Explanation

• The Null Hypothesis

• Steps in Hypothesis Testing

• The Precise Meaning of the p Value

• At What Level is H0 Really Rejected

• Statistical and Practical Significance

• Type I and II Errors

• One- and Two-Tailed Tests (t-tests)

• Confidence Intervals and Hypothesis Testing

• Exercises

HYPOTHESIS TESTING WITH STANDARD ERRORS

• General Formula

• Tests of μ, σ Known

• Tests of μ, σ Estimated

• μ1 - μ2, Independent Groups, σ Estimated

• μ1 - μ2, Dependent Groups, σ Estimated

• Linear Combination of Means, Linear Combination of Means, Independent

Groups

• Proportions

• Differences Between Proportions

• Exercises

POWER & *P VALUE

• Introduction

• Factors Affecting Power

o Introduction

o Size of Differences Between Means

o Significance Level

o Sample Size

o Variance

o Other Factors

• Estimating Power

• *P Value

• Exercises

ANALYSIS OF VARIANCE (ANOVA)

• Preliminaries

• ANOVA with 1 Between-Subjects Factor

• Tests supplementing ANOVA

• Formal Model

• Expected Mean Squares

• Exercises

PREDICTION

• Introduction

• Standard Error of the Estimate

• Partitioning the Sums of Squares

• Confidence Intervals and Significance Tests for Correlation and Regression

• Simple Linear Regression

 • Multiple Linear Regression

• Exercises

DAY FOUR

HYPOTHESIS TESTING WITH STANDARD ERRORS

• General Formula

• Tests of μ, σ Known

• Tests of μ, σ Estimated

• μ1 - μ2, Independent Groups, σ Estimated

• μ1 - μ2, Dependent Groups, σ Estimated

• Linear Combination of Means, Linear Combination of Means, Independent

Groups

• Proportions

• Differences Between Proportions

• Exercises

POWER & *P VALUE

• Introduction

• Factors Affecting Power

o Introduction

o Size of Differences Between Means

o Significance Level

o Sample Size

o Variance

o Other Factors

• Estimating Power

• *P Value

• Exercises

ANALYSIS OF VARIANCE (ANOVA)

• Preliminaries

• ANOVA with 1 Between-Subjects Factor

• Tests supplementing ANOVA

• Formal Model

• Expected Mean Squares

• Exercises

 DAY FIVE

PREDICTION

• Introduction

• Standard Error of the Estimate

• Partitioning the Sums of Squares

• Confidence Intervals and Significance Tests for Correlation and Regression

• Simple Linear Regression

• Multiple Linear Regression

• Exercises

Certificate

MAESTRO CONSULTANTS Certificate of Completion for delegates who attend and complete the training course

Methodology

Our courses are highly interactive, typically taking a case study approach that we have found to be an effective method of fostering discussions and transferring knowledge. Participants will learn by active participation during the program through the use of individual exercises, questionnaires, team exercises, training videos and discussions of “real life” issues in their organizations.
The material has been designed to enable delegates to apply all of the material with immediate effect back in the workplace.

Fees

$4000