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MATH 375 Syllabus

Statistical Methods II

Revised: January 7, 2020

Course Description

Least squares estimates of parameters in regression models, simple linear regression, multiple linear regression, hypothesis testing and confidence intervals in linear regression models, testing of models, data analysis and appropriateness of models, time series models, moving average, regression-based and/or ARIMA models, estimation, data analysis and forecasting with time series models, forecast errors and confidence intervals.  Analysis of real data will be included.

Prerequisite: MATH 270

Student Learning Objectives

By the end of the course, students will be able to understand and explain

  1. Least square estimates of parameters in linear regression analysis
  2. Simple linear regression
  3. Multiple linear regression
  4. Hypothesis testing and confidence intervals in linear regression models
  5. Testing of models, data analysis and appropriateness of models in linear regression
  6. Linear time series models
  7. Moving average, regression-based and/or ARIMA models
  8. Estimation, data analysis and forecasting with time series models
  9. Forecast errors and confidence intervals in time series models

Text

Michael Kutner, Christopher Nachtsheim, John Neter and William Li. Applied Linear Statistical Models, Fifth Edition

Paul Cowpertwait and Andrew Metcalfe. Introductory Time Series with R.

Grading Procedure

Grading procedures and factors influencing course grade are left to the discretion of individual instructors, subject to general university policy.

Attendance Policy

Attendance policy is left to the discretion of individual instructors, subject to general university policy.

Course Outline

From Applied Linear Statistical Models:

  • Chapter 1: Linear Regression with One Predictor Variable (1 week)
  • Chapter 2: Inferences in Regression and Correlation Analysis (1 week)
  • Chapter 3: Diagnostics and Remedial Measures (1 week)
  • Chapter 4: Simultaneous Inferences and Other Topics Regression Analysis (0.5 week)
  • Chapter 5: Matrix Approach to Simple Linear Regression Analysis (1 week)
  • Chapter 6: Multiple Regression I (1 week)
  • Chapter 7: Multiple Regression II (1 week)

From Introductory Time Series with R:

  • Chapter 1: Introduction and Exploratory Methods (2 weeks)
  • Chapter 2: Basic Models (2 weeks)
  • Chapter 3: Time Series Regression (2 weeks)