## Course Description

In this three-day hands-on course students learn about descriptive statistics and inferential statistics, and how to present statistical data to make critical decisions in engineering, education, and business.

## Course Objectives

Upon successful completion of this course, students will be able to:

- Calculate important statistical parameters
- Analyze data distributions
- Properly create accurate charts and graphs
- Validate data measurements
- Conduct reliable data acquisition
- Make sound business decisions based on statistical information
- Use statistics to present conclusion in written reports and presentations
## Course Benefits

Many companies make decision based on the highest paid person's opinion, and not based on facts or numbers. With data growing in goeometrical proportions, better business decisions can now be made with available data. Organizing data into statistics is fairly easy, but presenting statistical information in the proper way to make a correct assessment, is key to making the best decision.

## Who Should Attend

This course is valuable for anyone that has to present statistical information or make a decision based on statistical data including:

- Engineers
- Technicians
- Business decision makers
- Sales managers
- Marketing managers
- Project managers
- Educators
- Environmentalists
- Health care professionals
## Prerequisite

Students are expected to know how to add, subtract, multipy, and divide.

## Method Of Instruction

Lecture, demonstrations, four short interactive quizzes, one dozen short videos, questions and answers, and numerous hands-on exercises.

## Hands-on Exercises

Throughout this course, students perform a series of extensive hands-on exercises including:

- Weighted Mean Exercise
- Median, Mode, and Midrange Exercise
- Standard Deviation Exercise
- How Many Licks Does It Take To Lick A Lollipop?
- Geometric Distribution Exercise
- Circle Graph Exercise
- Correlation Exercise
- Choosing the Right Graph
- Measurement Exercise
- Confidence Intervals
## Course Outline

Chapter 1: Basic Statistical Definitions

- Types Of Statistics
- Statistical Population
- Sample
- Central Tendency
- Average
- Arithmetic Mean
- Geometric Mean
- Harmonic Mean
- Means Exercise
- Weighted Mean
- Median
- Mode
- Range
- Midrange
- Variance
- Absolute Deviation
- Standard Deviation
Chapter 2: More Statistical Definitions

- Probability Distribution Function
- Cumulative Distribution Function
- Central Limit Theorem
- Normal Distribution
- Z-Score
- Bernoulli Trial
- Binomial Distribution
- Bernoulli Trial Exercise
- Negative Binomial Distribution
- Exponential Distribution
- Geometric Distribution
- Poisson Distribution
- Outlier
- Kurtosis
- Skewness
- Distributions With Heavy Tails
Chapter 3: Using Graphs And Charts

- Data
- Organizing Data In Tables
- Relative Frequency Table
- Ratio
- Percent
- Types Of Graphs
- Correlation
- Line And Curve Fitting
- Choosing A Graph
- Misleading Graphs
Chapter 4: Validity And Reliability

- Methods Of Obtaining Data
- Validity
- Maximize Validity
- Selection Bias
- Sampling Bias
- Reliability
- Consistency And Inconsistency
- Errors In Measurement
- Accuracy And Precision
Chapter 5 Confidence Intervals

- Standard Error
- Variance Of The Sample
- Standard Deviation Of The Sample
- Standard Error Of The Mean
- Confidence Limits
- t Distribution
- Confidence Interval Example
- Confidence Intervals Of Large Sample Size