This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.
A more advanced treatment of ANOVA and regression occurs in the Statistics 2: ANOVA and Regression course. A more advanced treatment of logistic regression occurs in the Categorical Data Analysis Using Logistic Regression course and the Predictive Modeling Using Logistic Regression course.
Ismerje meg hogyan:
- Generate descriptive statistics and explore data with graphs.
- Perform analysis of variance and apply multiple comparison techniques.
- Perform linear regression and assess the assumptions.
- Use regression model selection techniques to aid in the choice of predictor variables in multiple regression.
- Use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression.
- Use chi-square statistics to detect associations among categorical variables.
- Fit a multiple logistic regression model.
- Score new data using developed models.
- Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables
A képzés angol nyelven folyik.
A képzés elérhető e-learning formában is, kérjen ajánlatot.
Course Overview and Review of Concepts
- Descriptive statistics.
- Inferential statistics.
- Examining data distributions.
- Obtaining and interpreting sample statistics using the UNIVARIATE procedure.
- Examining data distributions graphically in the UNIVARIATE and FREQ procedures.
- Constructing confidence intervals.
- Performing simple tests of hypothesis.
- Performing tests of differences between two group means using PROC TTEST.
ANOVA and Regression
- Performing one-way ANOVA with the GLM procedure.
- Performing post-hoc multiple comparisons tests in PROC GLM.
- Producing correlations with the CORR procedure.
- Fitting a simple linear regression model with the REG procedure.
More Complex Linear Models
- Performing two-way ANOVA with and without interactions.
- Understanding the concepts of multiple regression.
Model Building and Effect Selection
- Automated model selection techniques in PROC GLMSELECT to choose from among several candidate models.
- Interpreting and comparison of selected models.
Model Post-Fitting for Inference
- Examining residuals.
- Investigating influential observations.
- Assessing collinearity.
Model Building and Scoring for Prediction
- Understanding the concepts of predictive modeling.
- Understanding the importance of data partitioning.
- Understanding the concepts of scoring.
- Obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM.
Categorical Data Analysis
- Producing frequency tables with the FREQ procedure.
- Examining tests for general and linear association using the FREQ procedure.
- Understanding exact tests.
- Understanding the concepts of logistic regression.
- Fitting univariate and multivariate logistic regression models using the LOGISTIC procedure.
- Using automated model selection techniques in PROC LOGISTIC including interaction terms.
- Obtaining predictions (scoring) for new data using PROC PLM.
Before attending this course, you should:
- Have completed the equivalent of an undergraduate course in statistics covering p-values, hypothesis testing, analysis of variance, and regression.
- Be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS Programming 1: Essentials course.
A tanfolyam SAS/STAT szoftver használatára épül.