MCCCD Program Description

Foundational Statistics for Engineers

Course: MAT280

First Term: 2022 Fall

Lec + Lab 3.0 Credit(s) 5.0 Period(s) 5.0 Load
Subject Type: Academic
Load Formula: T- Lab Load

Description: Fundamentals of probability, descriptive statistics, sampling distributions, parameter estimation, tests of hypotheses, regression analysis, analysis of variance, and design of experiments.

MCCCD Official Course Competencies
1. Interpret the mean, median, standard deviation, and variance for a sample of data. (I) 2. Interpret histograms and probability plots. (I) 3. Perform probabilistic computations utilizing counting methods including laws of joint and total probability. (I) 4. Find probabilities from binomial distributions. (I) 5. Find probabilities and inverse probabilities from both normal and t-distributions (I, II) 6. Apply the central limit theorem appropriately. (II, III, IV) 7. Utilize confidence intervals and hypothesis testing. (III, IV) 8. Conduct a paired t-test. (III, IV) 9. Use Analysis of Variance (ANOVA) to determine which populations are different from one another. (V, VI) 10. Conduct a 2k experimental design and determine which factors are statistically significant. (VI) 11. Analyze underlying factors using residual plots. (VI) 12. Accurately apply both simple and multiple regression models. (VI) 13. Identify which terms in a multiple regression model should be included. (VI) 14. Evaluate assumptions of regression via residual analysis. (VI)
MCCCD Official Course Competencies must be coordinated with the content outline so that each major point in the outline serves one or more competencies. MCCCD faculty retains authority in determining the pedagogical approach, methodology, content sequencing, and assessment metrics for student work. Please see individual course syllabi for additional information, including specific course requirements.

MCCCD Official Course Outline
I. Random variables and probability distributions A. Mean, median, variance, standard deviation B. Binomial, district, geometric, poisson and hypergeometric distribution II. Probability distributions A. Joint, beta, cumulative, conditional, common joint, weibull, lognormal, erlang and gamma distributions B. Conditional probability C. Covariance and correlation III. Tests of hypothesis A. Hypothesis testing B. Tests of the mean of a normal distributions C. Tests of variance and standard deviations D. Nonparametric tests E. Equivalent testing IV. Regression and correlation A. Empirical models B. Simple linear regression C. Hypothesis tests D. Confidence intervals E. Correlation F. Regression in transformed variables G. Logistic regression V. Design and analysis of single factor experiments A. Analysis of variance B. Multiple comparisons following (ANOVA) C. Random effect model D. Randomized complete block design VI. Design of experiments with several factors A. Two factor factorial experiments B. General factorial experiments C. 2K factorial design D. Factorial (ANOVA)

MCCCD Governing Board Approval Date: March 22, 2022

All information published is subject to change without notice. Every effort has been made to ensure the accuracy of information presented, but based on the dynamic nature of the curricular process, course and program information is subject to change in order to reflect the most current information available.