Lectures

Lectures (GWU EMSE-271 Data Analysis for Engineers and Scientists)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Concepts


 * Session 1** - Exploratory Data Analysis, Probability Calculus (Bayes), Random Variables
 * Discrete and continuous random variables
 * Session 2** - Distribution Theory | Calculating Probabilities | Sums of Random Variables | Point-Estimation
 * Ask Leslie ___, rules for calculating interesting values, dependence (covariance), estimators (desireable characteristics)


 * Session 3** - Estimator Distributions | (Estimation) |Confidence Intervals (Mean Confidence Intervals | Variance Confidence Intervals) | Hypothesis Testing (Mean Hypothesis Testing | Variance Hypothesis Testing) | Goodness-of-Fit ( Method-of-Moments | Maximum Likelihood Estimation ) | Miscellaneous Notes


 * [null hypothesis | acceptance and rejection terminology | Type I and Type II errors | p-value]
 * How do estimators behave? ...
 * DQE wording: realization


 * Session 4/5** - Chi-Squared Goodness-of-Fit | Credibility Intervals | Two Sample Hypothesis Testing | Joint Normal Distribution
 * Anderson-Darling Goodness-of-Fit Test


 * Session 5 Notes** - MATRICES AND VECTORS (Matrix-Vector Algebra) - TBD - Describing Vector Observations | Matrix Algebra | Linear Combinations | Coordinate Systems | Singular Value Decomposition


 * Session 6** - Multivariate Analysis - TBD - Joint Normal Distribution | Multivariate Point Estimation | Matrix Determinant | Geometric Interpretation | Hotelling T2 Hypothesis Test | Hotelling T2 Sample Mean Test


 * Session 7** - ---> Look at the following topic areas:

MULTIVARIATE ANALYSIS

- TBD - Joint Normal Distribution | Multivariate Point Estimation | Matrix Determinant | Geometric Interpretation | Hotelling T2 Hypothesis Test | Hotelling T2 Sample Mean Test

- ASIDE: Lattin Overview (Introduction | Regression | Principal Components | Factor Analysis | Multidimensional Scaling | Clustering | Canonical Correlation | Structured Equation Models with Latent Variables | Analysis of Variance | Discriminant Analysis | Logit Choice Models}

- Regression Analysis

- Principal Component Analysis (PCA)

- Analysis of Variance ANOVA: