• Population and Sample.
• Random Sampling.
• Parameter and Statistic.
• Quantitative and Qualitative Variables.
• Introduction to Raw (Ungrouped) Data Sets and Grouped Data Sets (Frequency Distributions.)
• Calculations of Measures of Central Tendency for Raw Data Sets. (Mean, Median and Mode).
• Calculations of Measures of Dispersion for Raw Data Sets.  (Range, Average Deviation, Variance, Standard Deviation, Percentiles, Quartiles and Deciles)
  
  

Lecture 2 – Descriptive Statistics II

• Grouped Data Sets which are Known as Frequency Distributions.
• Calculations of Measure of Central Tendency for Grouped Data Sets.
• Calculations of Measures of Dispersion for Grouped Data Sets.
• Adjustments if the Data Set is a Population or a Sample. 
• The Empirical Rule and Chebyshev's Theorem.
• Pearsonian's Coefficient of Skewness.
• The Coefficient of Variation.
  
  

Lecture 3 – Probabilities-The Basics

• The Three Generally Accepted Approaches to Probability.
• Sample Space as Seen From Playing Cards.
• Odds Making (for you horse racing enthusiasts).
• Three Methods of Calculating Probabilities. (Probability Tree, Addition and Subtraction Rules, Frequency Table and Probability Table).
• Arranging the Data Sets into Sub-Sets Using Permutations and Combinations.
  
  

Lecture 4 – Probabilities II - Probability Distributions

• Binominal Probability Distributions - Discrete Data Sets.
• Uniform Probability Distributions - Continuous Data Sets.
• Normal and Standard Normal Probability Distributions - Continuous Data Sets.
• Poisson Probability Distributions - Discrete Data Sets.
• Exponential Probability Distributions - Continuous Data Sets.
• The Expected Value of the Random Variable and its Variability.
• Learn When to Play a Game of Chance and When Not to Play a Game of Chance.  (Expected Value Approach).
  
  

Lecture 5 – Inferential Statistics- The Basics.

• Sampling Distribution of Sample Means.
• The Basics Associated with Inferential Statistics.
• Sampling Error.
• The Z-Process as it Applies to the Sampling Distribution of Sample Means.
• The Central Limit Theorem.
• Four Basic Methods of Sampling That Do Not Impune the Inferential Concept. 
  
  

Lecture 6 – Applying Inferential Statistics - Confidence Intervals.

• Confidence Intervals - What They Are and How to Calculate Them. 
• How to Interpret Confidence Intervals.
• Applying the Z and the t Process.
• How to Control the Interval Width.
• How to Determine the Proper Sample Size.
• Four Properties of a Good Estimator. 
  
  

Lecture 7 – Applying Inferential Statistics - Hypothesis Testing for One and Two Populations.

• What is Hypothesis Testing?
• A Contrast Between Confidence Intervals and Hypothesis Testing.
• How to Set Up a Null Hypothesis. 
• Three Critical Questions That Will Aid in Setting Up the Null Hypothesis.  (Secret to Setting Up the Alternative First). 
• The Five Step Process for Beginners.
• Three Forms of Hypothesis Testing. 
• Two Types of Errors - Alpha and Beta.
• The Use of Z or t-Testing in Hypothesis Testing for One Population.
• The Alternative Z-Test (A Short Cut) for One Population Testing. 
• The p-Value and Its Meaning. 
• An Aid in Understanding the Language of Hypothesis Testing. 
• Hypothesis Testing for Two Populations.  (Independent and Paired Sampling). 
  
  

Lecture 8 – Applying Inferential Statistics - ANOVA, F-Distribution, and Chi Square as a Parametric Test.

• The Chi-Square Test for the Variance of a Single Population.
• The Chi-Square Test as a Non-Parametric Test.
• The F-Distribution for the Variance of Two Populations.
• ANOVA - Analysis of Variance for Three or More Populations. 
  
  

Lecture 9 – Forecasting with Regression Analysis - Simple and Multiple Methods. 

• Simple Regression Using the Method of Least Squares.
• The Difference Between a Deterministic and a Random Model.
• Answering the Question:  How Good is the Relationship Between the Two Variables?
• The Measure of Goodness of Fit and The Measure of Standard Error. 
• The Measure of the Strength of the Relationship.
• Multiple Regression - Adding More Independent Variables. 
• Adjustments Necessary When Working with Multiple Regression.  
  
  

• Use of Moving Averages in Plotting Data and in Forecasting.
• Use of Exponential Smoothing.
• Trend Analysis Using the Method of Least Squares.
• De-Composition of a Time Series.
• Index Numbers - Simple, Composite and Weighted. 
  
  

• How to Construct a Mean and Range Chart.
• How to Construct a p-Chart and a c-Chart.
• How to Interpret a Control Chart.
• How to Develop the Acceptable Level of Defectives Before Sending the Shipment of Goods Back to the Supplier.
• Understanding a Double Sampling Plan. 
  
  

• Various Other Non-Parametric Tests.