Kursbeskrivning: Advanced Techniques: Regression SPVC

PLEASE DO NOT MAKE TRAVEL ARRANGEMENT FOR THIS SELF PACED VIRTUAL COURSE (SPVC). ONCE YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT.

After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. You will then have a 30 day window in which to complete your course. Within this 30 days, you will have 336 hours of elab time. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day, but lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Once you have accessed the course, help is available Monday through Friday; questions will be responded to within 24 hours.

NO EXTENSIONS FOR THIS COURSE WILL BE GRANTED.

This is the self paced training version of "Advanced Techniques: Regression" classroom course. This self-paced training course examines regression techniques used to explore the relationships between interval scale variables in detail. You will develop an understanding of when to apply each technique, how to apply it and how to interpret the results. Additionally, the course will cover some preliminary data analysis steps, how to check the underlying assumptions and suggestions of how to proceed when your assumptions fail.

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Those who want to know when to use and how to set up regression in SPSS as well as how to interpret the results. SPSS users who want to improve their understanding of regression techniques.

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Those who want to know when to use and how to set up regression in SPSS as well as how to interpret the results.

You should have:

• Interest in regression techniques.

For users of SPSS for Windows Base System, SPSS Regression Models and SPSS Advanced Models.

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Please refer to course overview.

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• Running stepwise regression
• Stepwise regression output
• Running multiple regression
• Assumptions of the linear model
• Residuals and influential points
• Introduction to regression
• Simple regression output
• Fitting lines to data
• Logistic regression output
• Running logistic regression
• Influential points and multicollinearity
• Examining the data
• Running simple regression
• Multiple regression output

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