From the course: Machine Learning & AI Foundations: Linear Regression

What you should know - SPSS Tutorial

From the course: Machine Learning & AI Foundations: Linear Regression

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What you should know

- [Instructor] What should you know to get the most value out of this course? Well, we're gonna be working inside the IBM SPSS Statistics package. So, you're gonna wanna make sure you have access to it. But you're also gonna wanna make sure that you're familiar with it. Certainly opening and saving files, but also other things like setting up the data properly, adding labels, all the basics are things that you should be familiar with because we won't be taking extra time to review that. In particular, you really have to have a good grounding of descriptive statistics and level of measurement. That's declaring your variables as nominal, ordinal, or scale. We're gonna be talking about that a lot, so you're gonna wanna have a good, solid grounding in it. Next, and this one's also very important. Statistical inference. I'm gonna be using phrases like this variable is statistically significant. Or this variable is not statistically significant a lot. So, if that's new language for you, you're gonna wanna revisit the basics of statistics and what statistical inference is all about. Two topics are gonna come up that I'm gonna talk about quite a bit. But if you arrive to the course with a little bit of familiarity with it, it's gonna help a lot. Correlation is one. And another one is the differences between data that looks like a normal distribution as opposed to a distribution that's skewed. All of these topics are covered wonderfully in Bart Poulson's class, SPSS Statistics Essential Training. Unless you really have a fantastic grounding in SPSS, I urge you to seek out that course first. It really does a wonderful job getting you ready for this course.

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