Summer 2019 - STAT 302 D100
Analysis of Experimental and Observational Data (3)
Class Number: 2645
Delivery Method: In Person
The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Quantitative.
Lab Instructor: Marie Loughin
1. Introduction to Regression Analysis
Simple regression, regression and causality, assumptions of linear regression, measuring adequacy of assumptions, estimation of error variance, inferences concerning slope and intercept, inferences concerning the simple regression line, interpretation of estimated regression lines, prediction with regression line.
2. Correlation and its Relationship to Regression
Definition of the correlation coefficient, R, measures of association, the bivariate normal distribution, what R does not measure, estimation and testing with R.
3. Analysis of Variance
One- and two-way analysis of variance, the analysis of variance table and related tests, fixed and random effects, multiple comparison procedures and contrasts.
4. Multiple Regression Analysis
Using more than one independent variable, graphical considerations for this problem, assumptions, collinearity, estimation of the best regression equation, analysis of variance table, overall and partial F tests.
5. The General Linear Model
Multiple regression and analysis of variance as special cases of the general linear model. The general procedure for constructing F-tests by fitting restricted models. Applications to analysis of covariance and comparison of two regression models.
6. Correlations: Multiple, Partial and Multiple-Partial
Correlation matrix, multiple correlation coefficient, the multivariate normal distribution, partial correlation coefficient, F-tests for multiple and partial correlations.
7. Analysis of Residuals
Checking the assumptions of the regression and analysis of variance models, effects of departures from the assumptions, transformations of the response and predictor variables.
- Participation 5%
- Assignments 15%
- Midterm 30%
- Final 50%
Above grading is subject to change.
MATERIALS + SUPPLIES:
R can be accessed via Jupyter, an online platform, at https://sfu.syzygy.ca/. Alternatively, R Studio and R statistical software can be downloaded free of charge from https://www.rstudio.com/ and https://cran.r-project.org/, respectively.
STAT2: Building Models for a World of Data. Author: Ann R. Cannon. Publisher: Freeman
Department Undergraduate Notes:
Students with Disabilites:
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