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KING ABDULAZIZ UNIVERSITY
Department of Industrial Engineering
IE 331: Probability and Engineering Statistics
Dr. Ammar Y. Alqahtani
Course Description
In the current scenario the engineers are having large amount of information and data pool available with them. But to make adequate decisions relying upon these data sets is creating hurdles. With regards to this the “Probability & Engineering Statistics” course will help them in making better decisions to minimize the risks.
As a result, we will spend more time on conceptual understanding and use of these techniques and little time on their mathematical foundation.
This course is rigorous and application-oriented engineering and managerial analysis course focusing on manufacturing, other related problems and other functions. To provide students with the necessary understanding of the probability, types of probability distributions, descriptive and inferences statistics and their applications enabling them to get information from set of data to help them in the decision making process in an engineering organization.
Course Prerequisites
STAT 110, MATH 202.
This course is a prerequisite for IE332; Engineering Statistics II.
Textbooks
Probability & Statistics for Engineers and Scientists, Ninth Edition, Walpole, R., Myers, R., Myers, S. & Ye, K., Prentice Hall, (2012), ISBN: 0321748239.
References
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1. Statistics for Engineers and Scientists, Navidi, W., McGraw-Hill, (2006), New York, NY, ISBN: 0071214925.
2. Probability and Statistics for Engineers, Eighth Edition, Miller, I., Freund, J. & Johnson, R., Prentice-Hall, Inc., (2011), ISBN: 0321694988.
3. Probability and Statistics in Engineering, Fourth Edition, Hines, W., Montgomery, D., Goldsman, D. & Borror, C., John Wiley & Sons, Inc., (2003), ISBN: 0471240877.
Course Topics:
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Introduction to Statistics and Data Analysis
Statistical Inference, Samples, Populations, & the Role of Probability*
Sampling Procedures; Collection of Data*
Measures of Locations: The Sample Mean, Median and Mode
Measures of Variability: The Sample Variance and Standard Deviation
Discrete and Continuous Data
Statistical Modeling’ Scientific Inspection, and Graphical Diagnostics
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Probability
Sample Space
Events
Counting Sample Points {Explain briefly}
Probability of an Event
Additive Rules
Conditional Probability, Independence and Product Rule
Bayes Rule
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Random Variables and Probability Distributions
Concept of a random Variable
Discrete Probability Distribution
Continuous Probability Distributions
Joint Probability Distributions
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Mathematical Expectation
Mean of a Random Variable
Variance and Covariance of Random Variables
Means and Variances of Linear Combinations of Random Variables
Chebyshev’s Theorem
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Some Discrete Probability Distributions
Discrete Uniform Distribution (hand out will be provided)
Binomial and Multinomial Distributions
Hypergeometric Distribution
Negative Binomial and Geometric Distributions
Poisson Distribution and the Poisson Process
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Some Continuous Probability Distributions
Continuous Uniform Distribution
Normal Distribution
Areas Under the Normal Curve
Applications of the Normal Distribution
Normal Approximation to the Binomial
Gamma and Exponential Distributions and their Applications
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Fundamental Sampling Distributions and Data Descriptions
Random Sampling*
Sampling Distribution*
Sampling Distribution of Means and the Central Limit Theorem
{Excluding the Sampling Distribution of Difference between Two Means}
t-Distribution
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One- and Two-Samples Estimation Problems
Introduction*
Statistical Inference*
Classical Methods of Estimation*
Single Sample: Estimating the Mean
Standard Error of a Point Estimate
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One- and Two-Samples Tests of Hypotheses
Statistical Hypotheses: General Concepts*
Testing a Statistical Hypothesis {Excluding Calculation of α and β}
The Use of P-Values for Decision Making in Testing Hypotheses
Single Sample: Tests Concerning a Single Mean
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Simple Linear Regression and Correlation
Introduction to Simple Linear Regression*
The Simple Linear Regression Model
Least Squares and the Fitted Model
Correlation
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Course Objectives:
At the end of the course, the students will be able to:
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1.
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Understand about various statistical techniques available for engineering problem.
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2.
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Understand discrete and continuous behavior of systems.
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3.
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Apply the fundamental theories of probability to engineering problems.
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4.
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Apply statistical concepts on real life problems.
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5.
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Perform data analysis using statistical soft wares.
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6.
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Interpret and communicate results of analysis.
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