Module 3 Hypothesis Testing Concepts and 1-Sample Tests

Lesson M03-02

Duration: 33 minutes

In this lesson we learn how to perform the hypothesis test by comparing the test statistic (Z value) to a critical value (which depends on the significance level and the alternate hypothesis).  The calculation of the Z test statistic is covered and the dependence of the critical value on the significance level and the alternate hypothesis is presented.  An example and exercise follow.

Statistics, Hypothesis Testing, & Regression Reference Textbook – Section 3 pages 7-12

Module 3 Hypothesis Testing Concepts and 1-Sample Tests

Lesson M03-01

Duration: 44 minutes

In the context of a 1-sample means test, the null hypothesis and possible alternate (one-side and two-sided) hypotheses are introduced.  We start with the case where the standard deviation is known (or a “good” estimate is available).  This is called the 1-sample Z test.  (Z is the test statistic).  Type I and Type II errors are defined.  

Statistics, Hypothesis Testing, & Regression Reference Textbook – Section 3 pages 1-7

Module 2 Graphical Analysis

Lesson M02-03

Duration: 30 minutes

In this Lesson, you have the opportunity to practice interpreting several plots as well as creating some from the raw data.  

Module 2 Graphical Analysis

Lesson M02-02

Duration: 17 minutes

Contour, Probability Plot, Probability Distribution, Bubble Plot, Pie Chart, Bar Chart

In Lesson 2 we look at some additional graphical tools.  A Contour plot is very useful to visualize how combinations of two variables affect the response values, per a predictive model that was developed using DOE or Regression.  Probability Plots allow us to determine if a particular distribution reasonably fits a dataset.  Bubble Plots allow us to explore 3 variables simultaneously.  Pie charts and Bar charts are familiar to most people and allow us to compare characteristics for different groups or categories

Module 2 Graphical Analysis

Lesson M02-01

Duration: 44 minutes

Pareto, Run Chart, Boxplot, Indiv. Value Plot, Histogram, Scatter Plot

In the first lesson we explore some basic graphical tools.  Pareto charts are useful to help in prioritization of quality issues by considering the frequency and severity of issues.  Run charts are a simple plot of data over time to get an overall idea of the stability of the data (without the additional complexity of control charts).  Box Plots and Individual Value plots are useful for comparing data from different groups to each other.  Histograms allow us to view the spread and shape of the distribution of continuous data.  Finally, Scatter plots allow us to visualize the relationship between two variables.  

Statistics, Hypothesis Testing, & Regression Reference Textbook – Section 2 pages 1-23

Module 1 Basic Statistics & Distributions

Lesson M01-04

Duration: 33 minutes

The Central Theorem is an important result and describes the behavior of process averages that come from a stable (single) process.  While we don’t prove this theorem, we get familiar with it using an every-day example.  It is important to distinguish between raw data and averages as they have very different properties.  The impact of sample size on the variation of averages distributions is introduced and this will come into play when we discuss power and sample size later in the course.   

Statistics, Hypothesis Testing, & Regression Reference Textbook – Section 1 pages 53-59

Module 1 Basic Statistics & Distributions

Lesson M01-03

Duration: 25 minutes

The most common probability distribution utilized in Quality methods (The Normal Distribution) is introduced here.  Some important characteristics of this distribution are presented.  We also discuss the Standard Normal Distribution and its utility in comparing data that come from different distributions.  This will be a useful lead-in to Hypothesis Testing.  

Statistics, Hypothesis Testing, & Regression Reference Textbook – Section 1 pages 37-52, 60-62

Module 1 Basic Statistics & Distributions

Lesson M01-02

Duration: 41 minutes

Here we review some of the most common statistics that describe central tendency (mean, median, mode) and variation (range, standard deviation).  The formulas or procedures for calculating them is covered.  We also introduce the “5 number summary” for describing a dataset (Min, Q1, Median, Q3, Max).  

Statistics, Hypothesis Testing, & Regression Reference Textbook – Section 1 pages 9-36

Module 1 Basic Statistics & Distributions

Lesson M01-01

Duration: 13 minutes

The two main types of data (Continuous/Variable and Attribute) are discussed here, and examples are provided.  Typically, we must use different methods for analyzing variable and attribute data.  Continuous and Attribute variables and responses are also handled differently in predictive models.  We also discuss population/process statistics and sample statistics that are obtained from sample data.  Normally, we make inferences about a population or process based on data from a sample.

Statistics, Hypothesis Testing, & Regression Reference Textbook – Section 1 pages 1-8