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.
Offered By
Statistics with SAS
SASAbout this Course
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Learner Career Outcomes
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got a tangible career benefit from this course
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Intermediate Level
Approx. 21 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Learner Career Outcomes
50%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 21 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Offered by
SAS
Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.
Syllabus - What you will learn from this course
1 hour to complete
Course Overview and Data Setup
In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.
1 hour to complete
2 videos (Total 13 min), 5 readings
3 hours to complete
Introduction and Review of Concepts
In this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.
3 hours to complete
17 videos (Total 41 min), 2 readings, 9 quizzes
4 hours to complete
ANOVA and Regression
In this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.
4 hours to complete
29 videos (Total 70 min), 2 readings, 14 quizzes
2 hours to complete
More Complex Linear Models
In this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.
2 hours to complete
13 videos (Total 43 min), 1 reading, 5 quizzes
2 hours to complete
Model Building and Effect Selection
In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.
2 hours to complete
11 videos (Total 28 min), 3 readings, 4 quizzes
3 hours to complete
Model Post-Fitting for Inference
In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.
3 hours to complete
18 videos (Total 46 min)
2 hours to complete
Model Building for Scoring and Prediction
In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.
2 hours to complete
11 videos (Total 27 min), 1 reading, 4 quizzes
Reviews
TOP REVIEWS FROM STATISTICS WITH SAS
by CKMar 18, 2021
The theoretical parts on introductory statistical analysis (in shorts tests, anova, regression) are well covered. The technical parts i.e coding applications complement the theoretical parts 100%.
by MSSep 4, 2019
The best course for statistics I've ever seen. I've learned statistics here not in university. Big like to all those people provide this valuable course for us. Thanks a million.
by CAMay 22, 2020
A Guided lesson even for a beginner. It gives you a general overview of statistics with great emphasis on SAS programming and statistical interpretations of your analyses.
by SSMar 7, 2021
Very useful and well organized course. It is useful in case you are new to modelling but also when you need to revise what you have learned before :)
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