Statistics is generally termed as a branch of applied mathematics concerned with the collection of quantitative data and the use of probability theory to estimate population parameters. Descriptive statistics (distribution, summary statistics), run charts, and probability (distributions) are used for developing process control charts and developing process capability indices. A technical staff should have expert knowledge to manipulate and estimate the probability of an event occurring.

This course is designed to help students to understand the fundamentals of random variables, probability distribution and decision making and also to analyze statistical data graphically using frequency distributions and cumulating frequency distributions.

After completing this course, students will possess a basic understanding of descriptive and inferential statistics, and their practical use in making decisions in business and industry.

**WHO SHOULD COMPLETE THIS COURSE?**

- Process Scientist/Engineer
- Design Engineer
- Product Development Engineer
- Regulatory/Compliance Professional
- Design Controls Engineer
- Continuous Improvement Manager
- QA/QC Supervisor
- Manufacturing Engineer
- QC/QC Technician
- Manufacturing Technician
- R&D Engineer

**COURSE OUTLINE**

THE ROLE OF STATISTICS IN ENGINEERING

- The Engineering Method and Statistical Thinking
- Collecting Engineering Data
- Mechanistic and Empirical Methods
- Observing Processes Over Time

DATA SUMMARY AND PRESENTATION

- Data Summary and Display
- Stem and leaf diagrams
- Histograms

DATA SUMMARY AND PRESENTATION II

- Box plots
- Time series plots
- Multivariate data

RANDOM VARIABLES AND PROBABILITY DISTRIBUTION

- Random Variables
- Probability
- Continuous Random Variables

RANDOM VARIABLES AND PROBABILITY DISTRIBUTION II

- Important continuous distributions
- Probability plots
- Discrete Random Variables

BINOMIAL AND POISSON DISTRIBUTION

- Experiment
- Examples
- Properties

FUNCTIONS OF RANDOM VARIABLES

- Linear functions
- Nonlinear functions

DECISION MAKING FOR A SINGLE SAMPLE

- Statistical Interference
- Point Estimation
- Hypothesis Testing

INFERENCE ON THE MEAN OF A POPULATION

- Variance known
- Variance unknown

INFERENCE ON POPULATION

- Inference on variation of a normal population
- Inference on Population proportion
- Testing of Goodness of fit

DECISION MAKING FOR A TWO SAMPLES

- Inference on the Means of Two Population, Variance Known
- Inference on the Means of Two Population, Variance Unknown

DECISION MAKING FOR A TWO SAMPLES II

- Paired t-test
- Inference on the Ratio of variances of two normal populations
- Inference on two population proportions

SUMMARY TABLES

- Summary tables introduction
- What to do if we have more than two samples

BUILDING EMPIRICAL MODELS