Webinar Series: Linear and Logistic Regression

Date: 
Tuesday, May 9, 2017
Event type: 
Webinar
Time: 
10:00am - 12:00pm PST

 

DATES AND TIMES

This webinar series includes two companion courses. Each course is covered over three, two hour sessions delivered from 10:00am - 12:00pm PST each day.


Linear Regression: Session 1 - Tue May 9 | Session 2 - Wed May 10 | Session 3 - Fri May 12

Logistic Regression: Session 1 - Tue May 16 | Session 2 - Wed May 17 | Session 3 - Fri May 19


Overview

This webinar series is divided into two, three session courses. Background theory and live demos will be presented during the live session, with take-home practice assignments and readings provided to further explore the theory, methods and applications of linear and logistic regression.

The series is intended for users with some introductory knowledge of hypothesis testing and simple bivariate analysis who wish to learn about regression modeling techniques. It therefore focuses on the application of models, when and how to run and interpret the results with general formulas presented, but does not delve into their statistical theory. Course demonstrations and homework exercises will include analysis using SPSS software.

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Prior required knowledge

This is a beginner to intermediate level webinar series therefore participants will be expected to have some introductory knowledge of hypothesis testing, statistical power, correlation coefficients, and simple bivariate regression. Previous knowledge in and/or work with regression is helpful but not required.

LINEAR REGRESSION

Webinar objectives

By the end of this webinar series, participants will be able to:

  • Plan for and run linear regression in SPSS, checking statistical assumptions and appropriateness of the regression results including being aware of the common misconceptions and hazards in interpreting regression results.
  • Communicate effectively with statistical analysis on regression methods
  • Understand the concepts such as covariate, confounder and interactions
  • Work on a data set to produce tangible results  to build a parsimonious model starting from descriptive analysis to model fitting

Course content

Session 1: Identifying and interpreting correlation and regression analysis

Topics covered:

  • Interpret scatterplots for quantitative bivariate data
  • Identify when to use correlation
  • Interpret the results of correlation coefficients
  • Identify when to use linear regression
  • Assumptions to be met
  • Introduction to case study to be reviewed during Session 3

Session 2: Interpreting results

Topics covered:

  • Interpret the results for linear regression
  • Short description of confounder, interaction, covariates
  • Multivariate linear analysis

Session 3: Start-to-finish modeling technique

Topics covered:

  • Case study: analysing a data set from descriptive statistics to model building

LOGISTIC REGRESSION

Webinar objectives

By the end of the Logistic Regression Webinar Series, participants should be able to:

  • Identify the circumstances and situations in which logistic regression is appropriate, if not required
  • Understand and interpret logistic regression output from commonly used software such as SPSS.

Course content

Session1: Introducing logistic regression analysis

Topics covered:

  • Categorical vs. continuous variables
  • Dummy variables
  • Odds and odd ratios
  • Why logistic regression?
  • Logit transformation
  • Laws of logarithms
  • Introduction to case study to be reviewed during Session 3

Session 2: Univariate and multivariate logistic regression analysis

Topics covered:

  • Logistic regression modeling
  • Interpreting results for logistic regression
  • Multivariate linear analysis

Session 3: Start-to-finish modeling technique

Topics covered

  • Case study: analysing a dataset from descriptive statistics to model building

Webinar-based course format

The interactive webinar software will provide remote access for students to view the instructor's screen, listen to the lecture in real time, and ask questions. The instructor will provide lecture slides (PowerPoint) for pre-reading prior to the start of the webinar. Data sets will be provided so that students can follow along with webinar demonstrations and complete related homework assignments between webinar sessions for review and practice. Webinar PowerPoint slide decks and related live demonstrations will focus on the use of SPSS software however additional code will be provided for R and SAS users upon request.

Students can download statistical software for use on their computers from the following sites:

R software package:

SPSS software package:

Free Trial Version:

https://www-01.ibm.com/marketing/iwm/dre/signup?source=SWG-STATS-DESKTOP_TRIAL&S_PKG=ov5354&S_TACT=000000OA&S_OFF_CD=10000400&_&lnk=STW_US_THP_A7_TL&lnk2=trial_SPSSstat&lang=en_US

Note: If the Free Trial Version is downloaded it will need to be removed from your computer prior to loading any future SPSS software packages

Grad Student fee package:

https://estore.onthehub.com/WebStore/OfferingsOfMajorVersionList.aspx?pmv=12c7bd0a-436e-e511-9411-b8ca3a5db7a1&cmi_mnuMain=2ff73789-74c7-e011-ae14-f04da23e67f6&cmi_mnuMain_child=2a1143f0-74c7-e011-ae14-f04da23e67f6&utm_source=statistics24-cta&utm_medium=othlandingpage&utm_campaign=SPSS

Certificate of workshop completion

For each webinar-based course, participants will be issued a certificate of course completion following a short summary quiz.

Instructor biography

Shayesteh Jahanfar is a reproductive epidemiologist with 22 years of teaching and research experience. She is an analytical consultant, an epidemiologist and an assistant professor at Central Michigan University supporting health researchers to gain analytical and study design skills. She is a clinician, has two PhDs in Obstetrics and Gynecology (New South Wales University, Australia) and in Epidemiology and Health Care (University of British Columbia, Canada).

Workshop fees

  • Regular rate: $295
  • Student rate: $165

 

Enrollment is limited - reserve your seat by emailing: ann.greenwood@popdata.bc.ca


Page last revised: April 11, 2017