This course requires you to have a background in basic statistics, as well as have a working knowledge of SAS software. LinkedIn Learning courses are recommended to gain a background in SAS. In addition, three prerequisite courses on study design on LinkedIn Learning are required.
- [Instructor] There are three areas of knowledge you should have before beginning this course. First, you should have a background in conducting basic regression analysis in statistics. You should have already been introduced to simple and multivariate linear and logistic regression and have been exposed to interpreting the results from regression models. It's not necessary that you have done these regressions in SAS. If you used another statistical package, but are familiar with how to do a basic regression, that's fine. Of course, since SAS is the regression star, you'll want to see what it's like in SAS. As long as you know regression basics, you'll be fine in this course. Second, you need to have a basic background in SAS. If you are not familiar with SAS, try these LinkedIn Learning courses listed on the slide. They are SAS courses taught by another author, Jordan Bakerman. These courses can be helpful if you've never had a SAS course before or do not use it as part of your work. Lastly, in order to completely understand and follow along with this course, you need to have taken three prerequisite courses on LinkedIn Learning. The first two, as you see on the slide, are on how to apply study designs to existing health care data. The last course listed on the slide is actually the first course in a two course series on SAS data analysis in health care. Since this current course is the second course in a two course series, please make sure that you take the first course before you proceed with this one. It's called SAS Essential Training: Descriptive Analysis for Healthcare Research. In this course, we are first going to cover linear regression. In chapter one, we'll prepare for linear regression and in chapter two we'll do the linear regression modeling. Our goal will be to develop a model and put in on the spreadsheet like the one you see on the slide. Then we'll tackle logistic regression. In chapter three, we'll prepare for logistic regression and then in chapter four we'll create our model. Again, our goal will be to develop a model that we can display in an Excel spreadsheet like the one on the slide. And then in chapter five, I'll show you how to actually take the models you develop for linear and logistic regression and put them on the spreadsheets I'm showing you. I'll also show you alternative ways you can choose to present your models. So with me, you always get over 100%. So I included a couple of bonus chapters for you. In chapter six, we talk about some issues in regression including collinearity and interactions. But we do not get hung up on these issues. Next, in chapter seven, I share with you some of my regression tips about various topics including categorizing variables and choosing reference groups. Are you all up to speed? Yes, no, well if the answer is yes, let us proceed.
- Preparing for linear regression
- Creating plots for testing assumptions
- Linear regression modeling
- Interpreting the linear regression model
- Logistic regression modeling
- Presenting linear and logistic regression models
- Issues in regression