Statistics with SAS

SAS

Informações sobre o curso

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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.

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Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 21 horas para completar

Inglês

Legendas: Francês, Português (Brasil), Russo, Inglês, Espanhol

Instrutores

Classificação do instrutor

4.59/5 (27 classificações)

Jordan Bakerman

Analytical Training Consultant

Education

12,257 aprendizes

2 Cursos

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 21 horas para completar

Inglês

Legendas: Francês, Português (Brasil), Russo, Inglês, Espanhol

oferecido por

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.

Programa - O que você aprenderá com este curso

Semana

Semana 1

1 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.

1 hora para concluir

2 vídeos (Total 13 mín.), 5 leituras

2 videos

Welcome and Meet the Instructor1min

Demo: Exploring Ames Housing Data10min

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.

3 horas para concluir

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

Semana 2

4 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.

4 horas para concluir

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

Semana 3

2 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.

2 horas para concluir

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.

2 horas para concluir

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

Semana 4

3 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.

3 horas para concluir

18 vídeos (Total 46 mín.)

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.

2 horas para concluir

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

Semana

Semana 5

4 horas para concluir

Categorical Data Analysis

In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.

4 horas para concluir

25 vídeos (Total 73 mín.)

25 videos

Overview1min

Scenario59s

Associations between Categorical Variables2min

Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE6min

Scenario43s

The Pearson Chi-Square Test3min

Odds Ratios4min

Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ5min

Scenario22s

The Mantel-Haenszel Chi-Square Test1min

The Spearman Correlation Statistic40s

Demo: Detecting Ordinal Associations Using PROC FREQ2min

Scenario49s

Modeling a Binary Response3min

Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC6min

Interpreting the Odds Ratio3min

Comparing Pairs to Assess the Fit of a Logistic Regression Model4min

Scenario50s

Specifying a Parameterization Method4min

Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC7min

Scenario35s

Interactions between Variables2min

Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC3min

Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC3min

Demo: Generating Predictions Using PROC PLM2min

18 exercícios práticos

Question 7.015min

Question 7.025min

Practice: Using PROC FREQ to Examine Distributions20min

Question 7.035min

Question 7.045min

Question 7.055min

Question 7.065min

Practice: Using PROC FREQ to Perform Tests and Measures of Association20min

Question 7.075min

Question 7.085min

Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis20min

Question 7.095min

Question 7.105min

Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables20min

Question 7.115min

Question 7.125min

Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions20min

Categorical Data Analysis30min

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
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o curso pode não oferecer essa opção. Você pode experimentar um teste gratuito ou solicitar o auxílio financeiro.
Em vez disso, o curso pode oferecer 'Curso completo, sem Certificado'. Com esta opção, é possível ver todo o conteúdo do curso, enviar as avaliações necessárias e obter uma nota final. Isso também significa que você não poderá comprar uma experiência de Certificado.
O que recebo ao adquirir o Certificado?
Quando você adquire o Certificado, ganha acesso a todo o material do curso, incluindo avaliações com nota atribuída. Após concluir o curso, seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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Sim, a Coursera oferece auxílio financeiro aos alunos que não podem pagar a taxa. Faça a solicitação clicando no link Auxílio financeiro, abaixo do botão "Inscreva-se" à esquerda. Você será solicitado a preencher um formulário e será notificado se for aprovado. Saiba mais.

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Statistics with SAS