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MAT 375 – Probability and Statistics for Engineers – sample syllabus Class Times: TR 75 minutes, F 50 minutes Room: Computer classroom Student Learning Outcomes: After successfully completing the course, the student will be able to do the following: 1. 2. 3. 4. 5. Understand the basic rules of probability. Determine the probability distribution, expected value, and variance of random variables. Analyze and interpret data graphically and numerically. Apply the Central Limit Theorem. Construct confidence intervals to estimate a population mean and proportion (one and two samples). 6. Determine an appropriate hypothesis test to apply to data and then successfully carry out the test and interpret the results. 7. Perform and interpret the results of a simple linear regression analysis. 8. Use a statistical software package to analyze data and to solve probability and statistics problems. Course Work: Graded work, completed in class or outside of class Projects Exams List of Topics: Measures of Location: Sample Mean and Median Measures of Variability: Range, Standard deviation, Interquartile range Discrete and Continuous Data Sample Space and Events Probability of an Event Additive Rules Conditional Probability, Independence and the Product Rule Bayes Rule Random Variables Discrete and Continuous distributions Joint probability Distributions & Conditional Distributions Mathematical Expectation (Mean, Variance, Moments) and Their Properties Chebyshev’s Theorem Bernoulli trials and the Binomial Probability Distribution Geometric and Negative Binomial Probability Distribution Poisson Probability Distribution Hypergeometric Probability Distribution Gaussian (Normal) Probability Density Functions Lognormal Probability Density Functions Normal Approximation to the Binomial Exponential Probability Density Function Central Limit Theorem Chi-square, T and F distributions Covariance and Correlation Estimation of Means and Difference of Means Estimation of Proportions and Difference of Proportions Hypothesis Testing for a Mean Hypothesis Testing for a Proportion Goodness of Fit Test and Test of Independence for Categorical Variables ANOVA (one way) Simple Linear Regression Inference on Linear Regression Parameters Utilize statistical software to study and apply probability theory through simulation of experiments with random outcomes.
