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.
oferecido por
SAS
Informações sobre o curso
29,723 visualizações recentes
Os alunos que estão fazendo este Course são
- Biostatisticians
- Data Analysts
- Scientists
- Medical Doctors
- Data Scientists
Programa - O que você aprenderá com este curso
Semana
11 hora para concluir
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.
2 vídeos (Total 13 mín.), 5 leituras
5 leituras
Learner Prerequisites10min
Choosing and Setting Up SAS Software for this Course10min
Follow These Instructions to Set Up Data for This Course (REQUIRED)30min
Completing Demos and Practices10min
Using Forums and Getting Help10min
3 horas para concluir
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.
17 vídeos (Total 41 mín.), 2 leituras, 9 testes
17 videos
Overview1min
Statistical Modeling: Types of Variables1min
Overview of Models3min
Explanatory versus Predictive Modeling1min
Population Parameters and Sample Statistics1min
Normal (Gaussian) Distribution2min
Standard Error of the Mean51s
Confidence Intervals2min
Statistical Hypothesis Test4min
p-Value: Effect Size and Sample Size Influence3min
Scenario48s
Performing a t Test4min
Demo: Performing a One-Sample t Test Using PROC TTEST3min
Scenario1min
Assumptions for the Two-Sample t Test2min
Testing for Equal and Unequal Variances2min
Demo: Performing a Two-Sample t Test Using PROC TTEST4min
2 leituras
Parameters and Statistics10min
Normal Distribution10min
9 exercícios práticos
Question 1.015min
Question 1.025min
Question 1.035min
Question 1.045min
Question 1.055min
Practice - Using PROC TTEST to Perform a One-Sample t Test20min
Question 1.065min
Practice - Using PROC TTEST to Compare Groups20min
Introduction and Review of Concepts30min
Semana
24 horas para concluir
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.
29 vídeos (Total 70 mín.), 2 leituras, 14 testes
29 videos
Overview1min
Scenario58s
Identifying Associations in ANOVA with Box Plots1min
Demo: Exploring Associations Using PROC SGPLOT1min
Identifying Associations in Linear Regression with Scatter Plots1min
Demo: Exploring Associations Using PROC SGSCATTER2min
Scenario56s
The ANOVA Hypothesis1min
Partitioning Variability in ANOVA2min
Coefficient of Determination1min
F Statistic and Critical Values1min
The ANOVA Model2min
Demo: Performing a One-Way ANOVA Using PROC GLM6min
Scenario49s
Multiple Comparison Methods2min
Tukey's and Dunnett's Multiple Comparison Methods1min
Diffograms and Control Plots1min
Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM6min
Scenario53s
Using Correlation to Measure Relationships between Continuous Variables1min
Hypothesis Testing for a Correlation1min
Avoiding Common Errors When Interpreting Correlations5min
Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR6min
Scenario45s
The Simple Linear Regression Model1min
How SAS Performs Simple Linear Regression1min
Comparing the Regression Model to a Baseline Model2min
Hypothesis Testing and Assumptions for Linear Regression1min
Demo: Performing Simple Linear Regression Using PROC REG7min
2 leituras
What Does a CLASS Statement Do?10min
Correlation Analysis and Model Building10min
14 exercícios práticos
Question 2.015min
Question 2.025min
Question 2.035min
Question 2.045min
Practice - Performing a One-Way ANOVA20min
Question 2.055min
Question 2.065min
Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons20min
Question 2.075min
Question 2.085min
Practice - Describing the Relationship between Continuous Variables20min
Question 2.095min
Practice - Using PROC REG to Fit a Simple Linear Regression Model20min
ANOVA and Regression30min
Semana
32 horas para concluir
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.
13 vídeos (Total 43 mín.), 1 leitura, 5 testes
13 videos
Overview1min
Scenario1min
Applying the Two-Way ANOVA Model3min
Demo: Performing a Two-Way ANOVA Using PROC GLM7min
Interactions3min
Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM5min
Demo: Performing Post-Processing Analysis Using PROC PLM4min
Scenario44s
The Multiple Linear Regression Model2min
Hypothesis Testing for Multiple Regression1min
Multiple Linear Regression versus Simple Linear Regression2min
Adjusted R-Square1min
Demo: Fitting a Multiple Linear Regression Model Using PROC REG7min
1 leituras
The STORE Statement10min
5 exercícios práticos
Question 3.015min
Practice - Performing a Two-Way ANOVA Using PROC GLM20min
Question 3.025min
Practice - Performing Multiple Regression Using PROC REG20min
More Complex Linear Models30min
2 horas para concluir
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.
11 vídeos (Total 28 mín.), 3 leituras, 4 testes
11 videos
Overview47s
Scenario1min
Approaches to Selecting Models2min
The All-Possible Regressions Approach to Model Building1min
The Stepwise Selection Approach to Model Building3min
Interpreting p-Values and Parameter Estimates2min
Demo: Performing Stepwise Regression Using PROC GLMSELECT7min
Scenario37s
Information Criteria2min
Adjusted R-Square and Mallows' Cp56s
Demo: Performing Model Selection Using PROC GLMSELECT5min
3 leituras
Activity - Optional Stepwise Selection Method Code10min
Information Criteria Penalty Components10min
All-Possible Selection
4 exercícios práticos
Question 4.015min
Practice - Using PROC GLMSELECT to Perform Stepwise Selection20min
Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques20min
Model Building and Effect Selection20min
Semana
43 horas para concluir
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.
18 vídeos (Total 46 mín.), 7 testes
18 videos
Overview1min
Scenario40s
Assumptions for Regression2min
Verifying Assumptions Using Residual Plots3min
Demo: Examining Residual Plots Using PROC REG5min
Scenario47s
Identifying Influential Observations1min
Checking for Outliers with STUDENT Residuals1min
Checking for Influential Observations2min
Detecting Influential Observations with DFBETAS1min
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5min
Demo: Examining the Influential Observations Using PROC PRINT6min
Handling Influential Observations1min
Scenario38s
Exploring Collinearity1min
Visualizing Collinearity2min
Demo: Calculating Collinearity Diagnostics Using PROC REG5min
Using an Effective Modeling Cycle1min
7 exercícios práticos
Practice: Using PROC REG to Examine Residuals20min
Question 5.015min
Practice: Using PROC REG to Generate Potential Outliers20min
Question 5.025min
Question 5.035min
Practice: Using PROC REG to Assess Collinearity20min
Model Post-Fitting for Inference30min
2 horas para concluir
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.
11 vídeos (Total 27 mín.), 1 leitura, 4 testes
11 videos
Overview1min
Scenario27s
Predictive Modeling Terminology1min
Model Complexity51s
Building a Predictive Model2min
Model Assessment and Selection1min
Demo: Building a Predictive Model Using PROC GLMSELECT10min
Scenario29s
Preparing for Scoring1min
Methods of Scoring1min
Demo: Scoring Data Using PROC PLM4min
1 leituras
Partitioning a Data Set Using PROC GLMSELECT10min
4 exercícios práticos
Question 6.015min
Practice: Building a Predictive Model Using PROC GLMSELECT20min
Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20min
Model Building for Scoring and Prediction30min
4.8
8 avaliaçõesPrincipais avaliações do Statistics with SAS
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.
This course is really fantastic!\n\nI love SAS, and I love analysis\n\nHope this will help me in my Ph. D studying career
Instrutores
Jordan Bakerman
Analytical Training ConsultantEducation
Sobre 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
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