If you haven’t done this yet that you can follow the tutorial here. Outputting a dataset to a CSV file in preparation for submitting it to Kaggleīefore you get started you will need to install a virtual machine and then the SAS university edition files.Using the KEEP statement to only keep variables that you want in your dataset.Using IF…THEN.ELSE logic to modify a dataset.

Calculating the proportion of men and women who survived using PROC FREQ.

Reading a CSV file into SAS using PROC IMPORT.

It explains how to use the SAS University Edition to do the following: It should be useful both for people who want to learn SAS, but also for those who want to use SAS to enter the Kaggle competition. The tutorial is designed to be roughly equivalent to the first excel lesson available on the Kaggle website. All contents under (CC) BY-NC-SA license, unless otherwise noted.This is the first of our tutorials on using SAS university edition to explore the data from the Kaggle Titanic: Machine Learning from Disaster edition. For example, from here you should see the page on “How do I use my existing SAS data, raw data, or Microsoft Excel data in SAS Studio?” Ĭopyright © Melinda Higgins, Ph.D. Definitely read through the section on “Working with Your Data and Results” to understand how to access your local hard drive(s) from inside the SAS University App. NOTE: The App is over 1.7GB – you will need to be on a fast network connection and even then it will take some time to download.Īdditional help and guidance is located at. The App runs inside the Oracle virtual machine you will set up. You will need to download and install the Oracle Virtual Box software and then download the SAS University Edition App. Be sure to read the quick start PDF and watch the quick start video.

You can download versions for Windows, Mac or Linux. The Direct Download is FREE, runs on your local machine, and is the one you want. The Amazon Web Services requires an internet connection, costs money, and your data has to be uploaded to the cloud. Discriminant Analysis/MANOVA, Mediators and SEMĭownload the Direct Download so you’ll have the software available when offline. Multilevel (Mixed or Nested) Linear Models (MLM) Dependent/Paired data and Repeated Measures Logistic & Poisson Regression - Generalized Linear Regression Modeling Covariates and Interaction/Moderator Effects Interactions, Moderators, Covariates, Factors

14,15 Regression Diagnostics and Variable Selection (cont'd).

Multivariate Regression & Variable Selection

Sas university edition slow down wifi