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Course Curriculum

Statistics 1: Setup for SAS Studio Required Setup - Creating the Data for Practices in this Course File
Statistics 1: Setup for SAS Studio Required Setup – Creating the Data for Practices in this Course File Details 00:00:00
e-Course Features
e-Course Features Details 00:00:00
What You Learn in This Course
What You Learn in This Course Details 00:00:00
1.1 Introduction to Statistics
1.1 Introduction to Statistics Details 00:00:00
1.2a Introduction
1.2a Introduction Details 00:00:00
1.2b Descriptive and Inferential Statistics
1.2b Descriptive and Inferential Statistics Details 00:00:00
1.2cExplanatory versus Predictive Modeling
1.2cExplanatory versus Predictive Modeling Details 00:00:00
1.2d Populations and Samples
1.2d Populations and Samples Details 00:00:00
1.2e Parameters and Statistics
1.2e Parameters and Statistics Details 00:00:00
1.2g Types of Variables Quantitative and Categorical
1.2g Types of Variables Quantitative and Categorical Details 00:00:00
1.2h Scales of Measurement (Nominal, Ordinal, Interval, and Ratio)
1.2h Scales of Measurement (Nominal, Ordinal, Interval, and Ratio) Details 00:00:00
1.2i Statistical Methods
1.2i Statistical Methods Details 00:00:00
1.2j Scenario Exploring Your Data - Introduction
1.2j Scenario Exploring Your Data – Introduction Details 00:00:00
1.2k Scenario Exploring Your Data
1.2k Scenario Exploring Your Data Details 00:00:00
1.3a Descriptive Statistics
1.3a Descriptive Statistics Details 00:00:00
1.3b Describing Your Data
1.3b Describing Your Data Details 00:00:00
1.3c Measures of Location
1.3c Measures of Location Details 00:00:00
1.3d Percentiles
1.3d Percentiles Details 00:00:00
1.3e Measures of Variability
1.3e Measures of Variability Details 00:00:00
1.3f Scenario Using Descriptive Statistics to Answer Data
1.3f Scenario Using Descriptive Statistics to Answer Data Details 00:00:00
1.3g The MEANS Procedure
1.3g The MEANS Procedure Details 00:00:00
1.3h Using PROC MEANS to Generate Dedcriptive Statistics
1.3h Using PROC MEANS to Generate Dedcriptive Statistics Details 00:00:00
1.3 Practice 1 Calculate Basic Statistics Using PROC MEANS
1.3 Practice 1 Calculate Basic Statistics Using PROC MEANS Details 00:00:00
1.3 Practice 1 Calculate Basic Statistics Using PROC MEANS with Soloutions
1.3 Practice 1 Calculate Basic Statistics Using PROC MEANS with Soloutions Details 00:00:00
1.4a Picturing Your Data
1.4a Picturing Your Data Details 00:00:00
1.4b Picturing Your Data Histogram
1.4b Picturing Your Data Histogram Details 00:00:00
1.4c Normal Distribution
1.4c Normal Distribution Details 00:00:00
1.4d Assessing Normality
1.4d Assessing Normality Details 00:00:00
1.4e Measures of Shape Skewness
1.4e Measures of Shape Skewness Details 00:00:00
1.4f Measures of Shape Kurtosis
1.4f Measures of Shape Kurtosis Details 00:00:00
1.4g Normal Probability Plots
1.4g Normal Probability Plots Details 00:00:00
1.4h Box Plots
1.4h Box Plots Details 00:00:00
1.4i Comparing Distributions (Summary)
1.4i Comparing Distributions (Summary) Details 00:00:00
1.4j Scenario Assessing Normality (An Example)
1.4j Scenario Assessing Normality (An Example) Details 00:00:00
1.4k The UNIVARIATE Procedure
1.4k The UNIVARIATE Procedure Details 00:00:00
1.4l Statistical Graphics Procedures in SAS
1.4l Statistical Graphics Procedures in SAS Details 00:00:00
1.4m The SGPLOT Procedure
1.4m The SGPLOT Procedure Details 00:00:00
1.4 Code Challenge
1.4 Code Challenge Details 00:00:00
1.4n ODS Graphics Output
1.4n ODS Graphics Output Details 00:00:00
1.4o Using SAS to Picture Your Data
1.4o Using SAS to Picture Your Data Details 00:00:00
1.4 Practice 2 Producing Descriptive Statistics and Box Plots
1.4 Practice 2 Producing Descriptive Statistics and Box Plots Details 00:00:00
1.4 Practice 2 Producing Descriptive Statistics and Box Plots with Solution
1.4 Practice 2 Producing Descriptive Statistics and Box Plots with Solution Details 00:00:00
1.5a Confidence Intervals for the Mean
1.5a Confidence Intervals for the Mean Details 00:00:00
1.5b Point Estimators, Variability, and Standard Error
1.5b Point Estimators, Variability, and Standard Error Details 00:00:00
1.5c Distribution of Sample Means
1.5c Distribution of Sample Means Details 00:00:00
1.5d Interval Estimators
1.5d Interval Estimators Details 00:00:00
1.5e Confidence Intervals
1.5e Confidence Intervals Details 00:00:00
1.5f Normality and the Central Limit Theorem
1.5f Normality and the Central Limit Theorem Details 00:00:00
1.5g Scenario Calculating Confidence Intervals for the Mean
1.5g Scenario Calculating Confidence Intervals for the Mean Details 00:00:00
1.5h Using PROC MEANS to Generate a Confidence Interval for the Mean
1.5h Using PROC MEANS to Generate a Confidence Interval for the Mean Details 00:00:00
1.5i DemonstrationCalculating a 95% Confidence Interval
1.5i Demonstration Calculating a 95% Confidence Interval Details 00:00:00
1.5 Practice 3 Using PROC MEANS to Generate a 95% Confidence Interval for the Mean
1.5 Practice 3 Using PROC MEANS to Generate a 95% Confidence Interval for the Mean Details 00:00:00
1.5 Practice 3 Using PROC MEANS to Generate a 95% Confidence Interval for the Mean with Solution
1.5 Practice 3 Using PROC MEANS to Generate a 95% Confidence Interval for the Mean with Solution Details 00:00:00
1.6a Hypothesis Testing
1.6a Hypothesis Testing Details 00:00:00
1.6b Decision-Making Process
1.6b Decision-Making Process Details 00:00:00
1.6c Steps in Hypothesis Testing
1.6c Steps in Hypothesis Testing Details 00:00:00
1.6d Types of Errors and Power
1.6d Types of Errors and Power Details 00:00:00
1.6e The p-Value, Effect Size, and Sample Size
1.6e The p-Value, Effect Size, and Sample Size Details 00:00:00
1.6f Statistical Hypothesis Test
1.6f Statistical Hypothesis Test Details 00:00:00
1.6g The t Statistic, t Distribution, and Two-Sided t-Test
1.6g The t Statistic, t Distribution, and Two-Sided t-Test Details 00:00:00
1.6h Scenario Comparing Sample and Hypothesized Means
1.6h Scenario Comparing Sample and Hypothesized Means Details 00:00:00
1.6i Using PROC UNIVARIATE to Generate a t Statistic
1.6i Using PROC UNIVARIATE to Generate a t Statistic Details 00:00:00
1.6j Using PROC UNIVARIATE to Perform a Hypothesis Test
1.6j Using PROC UNIVARIATE to Perform a Hypothesis Test Details 00:00:00
1.6 Practice 4 Using PROC Univariate to Perform a One-Sample t-Test
1.6 Practice 4 Using PROC Univariate to Perform a One-Sample t-Test Details 00:00:00
1.6 Practice 4 Using PROC Univariate to Perform a One-Sample t-Test with Solution
1.6 Practice 4 Using PROC Univariate to Perform a One-Sample t-Test with Solution Details 00:00:00
Summary Lesson 1 Introduction to Statistics
Summary Lesson 1 Introduction to Statistics Details 00:00:00
Lession 1 Quiz
Lession 1 Quiz Details 00:00:00
Lession 1 Quiz Feedback
Lession 1 Quiz Feedback Details 00:00:00
2.1 Analysis of Variance Overview
2.1 Analysis of Variance Overview Details 00:00:00
2.2a Graphical Analysis of Associations Introduction
2.2a Graphical Analysis of Associations Introduction Details 00:00:00
2.2b Scenario Getting to Know the Ames Housing Data
2.2b Scenario Getting to Know the Ames Housing Data Details 00:00:00
2.2c Using Box Plots to Explore Associations
2.2c Using Box Plots to Explore Associations Details 00:00:00
2.2d Scenario Exploring Associations in Ames Housing Data
2.2d Scenario Exploring Associations in Ames Housing Data Details 00:00:00
2.2e Exploring Associations with Box Plots
2.2e Exploring Associations with Box Plots Details 00:00:00
2.3a Two-Sample t-Tests Introduction
2.3a Two-Sample t-Tests Introduction Details 00:00:00
2.3b The Two-Sample t-Test
2.3b The Two-Sample t-Test Details 00:00:00
2.3c Assumptions for the Two-Sample t-Test
2.3c Assumptions for the Two-Sample t-Test Details 00:00:00
2.3d F-Test for Equality of Variance
2.3d F-Test for Equality of Variance Details 00:00:00
2.3e Scenario Comparing Group Means
2.3e Scenario Comparing Group Means Details 00:00:00
2.3f Identifying Your Data
2.3f Identifying Your Data Details 00:00:00
2.3g The TTEST Procedure
2.3g The TTEST Procedure Details 00:00:00
2.3h Running PROC TTEST in SAS
2.3h Running PROC TTEST in SAS Details 00:00:00
2.3i Examining the Equal Variance t-Test and p-Values
2.3i Examining the Equal Variance t-Test and p-Values Details 00:00:00
2.3j Examining the Unequal Variance t-Test and p-Values
2.3j Examining the Unequal Variance t-Test and p-Values Details 00:00:00
2.3k Interpreting Two-Sample t-Test Results
2.3k Interpreting Two-Sample t-Test Results Details 00:00:00
2.3l One-Sided Tests
2.3l One-Sided Tests Details 00:00:00
2.3m Scenario Testing for Differences on One Side
2.3m Scenario Testing for Differences on One Side Details 00:00:00
2.3n The TTEST Procedure and SIDES Option
2.3n The TTEST Procedure and SIDES Option Details 00:00:00
2.3o Performing a One-Sided t-Test
2.3o Performing a One-Sided t-Test Details 00:00:00
2.3 Code Challenge
2.3 Code Challenge Details 00:00:00
2.3 Practice 1 Using PROC TTEST to Compare Means
2.3 Practice 1 Using PROC TTEST to Compare Means Details 00:00:00
2.3 Practice 1 Using PROC TTEST to Compare Means with Solution
2.3 Practice 1 Using PROC TTEST to Compare Means with Solution Details 00:00:00
2.4a One-Way ANOVA Introduction
2.4a One-Way ANOVA Introduction Details 00:00:00
2.4b ANOVA Overview
2.4b ANOVA Overview Details 00:00:00
2.4c The ANOVA Hypothesis
2.4c The ANOVA Hypothesis Details 00:00:00
2.4d The ANOVA Model
2.4d The ANOVA Model Details 00:00:00
2.4e Sums of Squares
2.4e Sums of Squares Details 00:00:00
2.4f Assumptions for ANOVA
2.4f Assumptions for ANOVA Details 00:00:00
2.4g Predicted and Residual Values
2.4g Predicted and Residual Values Details 00:00:00
2.4h Scenario Comparing Group Means with One-Way ANOVA
2.4h Scenario Comparing Group Means with One-Way ANOVA Details 00:00:00
2.4i Examining Descriptive Statistics across Groups
2.4i Examining Descriptive Statistics across Groups Details 00:00:00
2.4j The GLM Procedure
2.4j The GLM Procedure Details 00:00:00
2.4 Code Challenge
2.4 Code Challenge Details 00:00:00
2.4k Using The GLM Procedure
2.4k Using The GLM Procedure2.4k Using The GLM Procedure Details 00:00:00
2.4 Practice 2 Analyzing Data in a Completely Randomized Design
2.4 Practice 2 Analyzing Data in a Completely Randomized Design Details 00:00:00
2.4 Practice 2 Analyzing Data in a Completely Randomized Design with Solution
2.4 Practice 2 Analyzing Data in a Completely Randomized Design with Solution Details 00:00:00
2.5a ANOVA with Data from a Randomized Block Design Introduction
2.5a ANOVA with Data from a Randomized Block Design Introduction Details 00:00:00
2.5b Observational Studies Versus Controlled Experiments
2.5b Observational Studies Versus Controlled Experiments Details 00:00:00
2.5c Nuisance Factors
2.5c Nuisance Factors Details 00:00:00
2.5d Including a Blocking Variable in the Model
2.5d Including a Blocking Variable in the Model Details 00:00:00
2.5e More ANOVA Assumptions
2.5e More ANOVA Assumptions Details 00:00:00
2.5f Scenario Creating a Randomized Block Design
2.5f Scenario Creating a Randomized Block Design Details 00:00:00
2.5 Practice 3 Analyzing Data in a Randomized Block Design
2.5 Practice 3 Analyzing Data in a Randomized Block Design Details 00:00:00
2.5 Practice 3 Analyzing Data in a Randomized Block Design With Solution
2.5 Practice 3 Analyzing Data in a Randomized Block Design With Solution Details 00:00:00
2.6a Two-Way ANOVA with Interactions Introduction
2.6a Two-Way ANOVA with Interactions Introduction Details 00:00:00
2.6b Multiple Comparison Methods
2.6b Multiple Comparison Methods Details 00:00:00
2.6c Tukey's Multiple Comparison Method
2.6c Tukey’s Multiple Comparison Method Details 00:00:00
2.6d Dunnett's Multiple Comparison Method
2.6d Dunnett’s Multiple Comparison Method Details 00:00:00
2.6e Scenario Determining Which Mean Is Different
2.6e Scenario Determining Which Mean Is Different Details 00:00:00
2.6f Diffograms and the Tukey Method
2.6f Diffograms and the Tukey Method Details 00:00:00
2.6g Control Plots and the Dunnett Method
2.6g Control Plots and the Dunnett Method Details 00:00:00
2.6h PROC GLM with LSMEANS
2.6h PROC GLM with LSMEANS Details 00:00:00
2.6i Performing a Post Hoc Pairwise Comparison
2.6i Performing a Post Hoc Pairwise Comparison Details 00:00:00
2.6 Practice 4 Post Hoc Pairwise Comparisons
2.6 Practice 4 Post Hoc Pairwise Comparisons Details 00:00:00
2.6 Practice 4 Post Hoc Pairwise Comparisons with Solution
2.6 Practice 4 Post Hoc Pairwise Comparisons with Solution Details 00:00:00
2.7a Two-Way ANOVA with Interactions Introduction
2.7a Two-Way ANOVA with Interactions Introduction Details 00:00:00
2.7b Additional Linear Modeling Terminology
2.7b Additional Linear Modeling Terminology Details 00:00:00
2.7c n-Way ANOVA
2.7c n-Way ANOVA Details 00:00:00
2.7d Interactions
2.7d Interactions Details 00:00:00
2.7e The Two-Way ANOVA Model
2.7e The Two-Way ANOVA Model Details 00:00:00
2.7f Scenario Using a Two-Way ANOVA
2.7f Scenario Using a Two-Way ANOVA Details 00:00:00
2.7g Identifying Your Data
2.7g Identifying Your Data Details 00:00:00
2.7h Applying the Two-Way ANOVA Model
2.7h Applying the Two-Way ANOVA Model Details 00:00:00
2.7i Examining Your Data with PROC MEANS
2.7i Examining Your Data with PROC MEANS Details 00:00:00
2.7j Examining Your Data with PROC SGPLOT
2.7j Examining Your Data with PROC SGPLOT Details 00:00:00
2.7k Specifying Interactions in PROC GLM
2.7k Specifying Interactions in PROC GLM Details 00:00:00
2.7l Performing Two-Way ANOVA with Interactions
2.7l Performing Two-Way ANOVA with Interactions Details 00:00:00
2.7m Performing a Post Hoc Pairwise Comparison
2.7m Performing a Post Hoc Pairwise Comparison Details 00:00:00
2.7 Practice 5 Performing Two-Way ANOVA
2.7 Practice 5 Performing Two-Way ANOVA Details 00:00:00
2.7 Practice 5 Performing Two-Way ANOVA with Solution
2.7 Practice 5 Performing Two-Way ANOVA with Solution Details 00:00:00
2.7n Saving Analysis Results with the STORE Statement
2.7n Saving Analysis Results with the STORE Statement Details 00:00:00
2.7o Performing Postprocessing Tasks with the PLM Procedure
2.7o Performing Postprocessing Tasks with the PLM Procedure Details 00:00:00
2.7p Scenario Storing PROC GLM Results for Future Analysis
2.7p Scenario Storing PROC GLM Results for Future Analysis Details 00:00:00
2.7q Performing Two-Way ANOVA with an Interaction by Using PROC GLM
2.7q Performing Two-Way ANOVA with an Interaction by Using PROC GLM Details 00:00:00
2.7r Performing Postprocessing Analysis by Using PROC PLM 401
2.7r Performing Postprocessing Analysis by Using PROC PLM 401 Details 00:00:00
2.7 Practice 6 Performing Two-Way ANOVA
2.7 Practice 6 Performing Two-Way ANOVA Details 00:00:00
2.7 Practice 6 Performing Two-Way ANOVA With Solution
2.7 Practice 6 Performing Two-Way ANOVA With Solution Details 00:00:00
Summary Lesson 2 Analysis of Variance (ANOVA)
Summary Lesson 2 Analysis of Variance (ANOVA) Details 00:00:00
Lession 2 Quiz
Lession 2 Quiz Details 00:00:00
Lession 2 Quiz Feedback
Lession 2 Quiz Feedback Details 00:00:00
3.1 Regression Lesson OverView
3.1 Regression Lesson OverView Details 00:00:00
3.2a Exploratory Data Analysis
3.2a Exploratory Data Analysis Details 00:00:00
3.2b Using Scatter Plots to Describe Relationships between Continuous Variables
3.2b Using Scatter Plots to Describe Relationships between Continuous Variables Details 00:00:00
3.2c Scenario Exploring Associations in Ames Housing Data
3.2c Scenario Exploring Associations in Ames Housing Data Details 00:00:00
3.2d Exploring Associations with Scatter Plots
3.2d Exploring Associations with Scatter Plots Details 00:00:00
3.2e Using Correlation to Measure Relationships between Continuous Variables
3.2e Using Correlation to Measure Relationships between Continuous Variables Details 00:00:00
3.2f Hypothesis Testing for a Correlation
3.2f Hypothesis Testing for a Correlation Details 00:00:00
3.2g Avoiding Common Errors in Interpreting Correlations
3.2g Avoiding Common Errors in Interpreting Correlations Details 00:00:00
3.2h Avoiding Common Errors Cause and Effect
3.2h Avoiding Common Errors Cause and Effect Details 00:00:00
3.2i Avoiding Common Errors Types of Relationships
3.2i Avoiding Common Errors Types of Relationships Details 00:00:00
3.2j Avoiding Common Errors Outliers
3.2j Avoiding Common Errors Outliers Details 00:00:00
3.2k Scenario Exploring Data Using Correlation and Scatter Plots
3.2k Scenario Exploring Data Using Correlation and Scatter Plots Details 00:00:00
3.2l Producing Correlation Statistics and Scatter Plots Using PROC CORR
3.2l Producing Correlation Statistics and Scatter Plots Using PROC CORR Details 00:00:00
3.2m Using PROC CORR to Produce Correlation Statistics and Scatter Plots
3.2m Using PROC CORR to Produce Correlation Statistics and Scatter Plots Details 00:00:00
3.2n Producing a Correlation Matrix and a Scatter Plot Matrix
3.2n Producing a Correlation Matrix and a Scatter Plot Matrix Details 00:00:00
3.2o Examining Correlations between Predictor Variables
3.2o Examining Correlations between Predictor Variables Details 00:00:00
3.2 Practice 1 Describe Relationships between Continuous Variables
3.2 Practice 1 Describe Relationships between Continuous Variables Details 00:00:00
3.3a Simple Linear Regression Introduction
3.3a Simple Linear Regression Introduction Details 00:00:00
3.3b Objectives of Simple Linear Regression
3.3b Objectives of Simple Linear Regression Details 00:00:00
3.3c Scenario Performing Simple Linear Regression
3.3c Scenario Performing Simple Linear Regression Details 00:00:00
3.3d The Simple Linear Regression Model
3.3d The Simple Linear Regression Model Details 00:00:00
3.3e How SAS Performs Linear Regression
3.3e How SAS Performs Linear Regression Details 00:00:00
3.3f Scenario Measuring How Well a Model Fits the Data
3.3f Scenario Measuring How Well a Model Fits the Data Details 00:00:00
3.3g Comparing the Regression Model to a Baseline Model
3.3g Comparing the Regression Model to a Baseline Model Details 00:00:00
3.3h Hypothesis Testing for Linear Regression
3.3h Hypothesis Testing for Linear Regression Details 00:00:00
3.3i Assumptions of Simple Linear Regression
3.3i Assumptions of Simple Linear Regression Details 00:00:00
3.3j The REG Procedure Performing Simple Linear Regression
3.3j The REG Procedure Performing Simple Linear Regression Details 00:00:00
3.3k Performing Simple Linear Regression
3.3k Performing Simple Linear Regression Details 00:00:00
3.3l Confidence and Prediction Intervals
3.3l Confidence and Prediction Intervals Details 00:00:00
3.3m Specifying Confidence and Prediction Intervals in SAS
3.3m Specifying Confidence and Prediction Intervals in SAS Details 00:00:00
3.3n Viewing and Printing Confidence Intervals and Prediction Intervals
3.3n Viewing and Printing Confidence Intervals and Prediction Intervals Details 00:00:00
3.3o The REG Procedure Producing Predicted Values
3.3o The REG Procedure Producing Predicted Values Details 00:00:00
3.3p Producing Predicted Values of the Response Variable
3.3p Producing Predicted Values of the Response Variable Details 00:00:00
3.3q Storing Parameter Estimates Using PROC REG and Scoring Using PROC SCORE
3.3r Storing Parameter Estimates Using PROC REG and Scoring Using PROC SCORE Details 00:00:00
3.3 Practice 2 Fit a Simple Linear Regression Model
3.3 Practice 2 Fit a Simple Linear Regression Model Details 00:00:00
3.3 Practice 2 Fit a Simple Linear Regression Model With Solution
3.3 Practice 2 Fit a Simple Linear Regression Model With Solution Details 00:00:00
3.4a Multiple Regression Introduction
3.4a Multiple Regression Introduction Details 00:00:00
3.4b Advantages and Disadvantages of Multiple Regression
3.4b Advantages and Disadvantages of Multiple Regression Details 00:00:00
3.4c Common Applications for Multiple Linear Regression
3.4c Common Applications for Multiple Linear Regression Details 00:00:00
3.4d Picturing the Model for Multiple Regression
3.4d Picturing the Model for Multiple Regression Details 00:00:00
3.4e Analysis versus Prediction in Multiple Regression
3.4e Analysis versus Prediction in Multiple Regression Details 00:00:00
3.4f Hypothesis Testing for Multiple Regression
3.4f Hypothesis Testing for Multiple Regression Details 00:00:00
3.4g Assumptions for Multiple Regression
3.4g Assumptions for Multiple Regression Details 00:00:00
3.4h Scenario Using Multiple Regression to Explain Sale Price
3.4h Scenario Using Multiple Regression to Explain Sale Price Details 00:00:00
3.4 Practice 3 Perform Multiple Linear Regression
3.4 Practice 3 Perform Multiple Linear Regression Details 00:00:00
3.4 Practice 3 Perform Multiple Linear Regression With Solution
3.4 Practice 3 Perform Multiple Linear Regression With Solution Details 00:00:00
3.5a Model Building and Interpretation Introduction
3.5a Model Building and Interpretation Introduction Details 00:00:00
3.5b Approaches to Selecting Models
3.5b Approaches to Selecting Models Details 00:00:00
3.5c SAS and Automated Approaches to Modeling
3.5c SAS and Automated Approaches to Modeling Details 00:00:00
3.5d The All-Possible Regressions Approach to Model Building
3.5d The All-Possible Regressions Approach to Model Building Details 00:00:00
3.5e SAS and the All-Possible Regressions Approach
3.5e SAS and the All-Possible Regressions Approach Details 00:00:00
3.5f Evaluating Models Using Mallows' Cp Statistic
3.5f Evaluating Models Using Mallows’ Cp Statistic Details 00:00:00
3.5g Viewing Mallows' Cp Statistic in PROC REG
3.5g Viewing Mallows’ Cp Statistic in PROC REG Details 00:00:00
3.5h The REG Procedure Using the All-Possible Regressions Technique
3.5h The REG Procedure Using the All-Possible Regressions Technique Details 00:00:00
3.5i The REG Procedure Using Automatic Model Selection
3.5i The REG Procedure Using Automatic Model Selection Details 00:00:00
3.5j The REG Procedure Estimating and Testing Coefficients for Selected Models
3.5j The REG Procedure Estimating and Testing Coefficients for Selected Models Details 00:00:00
3.5 Practice 4 Use All-Possible Regression Techniques
3.5 Practice 4 Use All-Possible Regression Techniques Details 00:00:00
3.5 Practice 4 Use All-Possible Regression Techniques With Solutiion
3.5 Practice 4 Use All-Possible Regression Techniques With Solution Details 00:00:00
3.5k The Stepwise Selection Approach to Model Building
3.5k The Stepwise Selection Approach to Model Building Details 00:00:00
3.5l The GLMSELECT Procedure
3.5l The GLMSELECT Procedure Details 00:00:00
3.5m The GLMSELECT Procedure Performing Stepwise Regression
3.5m The GLMSELECT Procedure Performing Stepwise Regression Details 00:00:00
3.5n Using Alternative Significance Criteria for Stepwise Models
3.5n Using Alternative Significance Criteria for Stepwise Models Details 00:00:00
3.5 Practice 5 Using Significance Level Model Selection Techniques
3.5 Practice 5 Using Significance Level Model Selection Techniques Details 00:00:00
3.5 Practice 5 Using Significance Level Model Selection Techniques With Solution
3.5 Practice 5 Using Significance Level Model Selection Techniques With Solution Details 00:00:00
3.6a Information Criterion and Other Selection Options Information Criteria
3.6a Information Criterion and Other Selection Options Information Criteria Details 00:00:00
3.6b Adjusted R-Square and Mallows’ Cp
3.6b Adjusted R-Square and Mallows’ Cp Details 00:00:00
3.6c More Model Selection Using PROC GLMSELECT
3.6c More Model Selection Using PROC GLMSELECT Details 00:00:00
3.6 Practice 6 Using Other Model Selection Techniques
3.6 Practice 6 Using Other Model Selection Techniques Details 00:00:00
3.6 Practice 6 Using Other Model Selection Techniques With Solution
3.6 Practice 6 Using Other Model Selection Techniques With Solution Details 00:00:00
Summary Lesson 3 Regression
Summary Lesson 3 Regression Details 00:00:00
Lession 3 Quiz
Lession 3 Quiz Details 00:00:00
Lession 3 Quiz Feedback
Lession 3 Quiz Feedback Details 00:00:00
4.1 Model Post-Fitting for Inference Lesson Overview
4.1 Model Post-Fitting for Inference Lesson Overview Details 00:00:00
4.2a Examining Residuals Introduction
4.2a Examining Residuals Introduction Details 00:00:00
4.2b Assumptions for Regression
4.2b Assumptions for Regression Details 00:00:00
4.2c The Importance of Plotting Your Data and Checking Assumptions
4.2c The Importance of Plotting Your Data and Checking Assumptions Details 00:00:00
4.2d Verifying Assumptions Using Residual Plots
4.2d Verifying Assumptions Using Residual Plots Details 00:00:00
4.2e Detecting Outliers Using Residual Plots
4.2e Detecting Outliers Using Residual Plots Details 00:00:00
4.2f The REG Procedure Producing Default Diagnostic Plots
4.2f The REG Procedure Producing Default Diagnostic Plots Details 00:00:00
4.2g The REG Procedure Requesting Specific Diagnostic Plots
4.2g The REG Procedure Requesting Specific Diagnostic Plots Details 00:00:00
4.2 Practice 1 Examining Residuals
4.2 Practice 1 Examining Residuals Details 00:00:00
4.2 Practice 1 Examining Residuals With Soultion
4.2 Practice 1 Examining Residuals With Soultion Details 00:00:00
4.3a Identifying Influential Observations Introduction
4.3a Identifying Influential Observations Introduction Details 00:00:00
4.3b Using Diagnostic Statistics to Identify Influential Observations
4.3b Using Diagnostic Statistics to Identify Influential Observations Details 00:00:00
4.3c Using Diagnostic Statistics STUDENT
4.3c Using Diagnostic Statistics STUDENT Details 00:00:00
4.3d Using Diagnostic Statistics COOKD
4.3d Using Diagnostic Statistics COOKD Details 00:00:00
4.3e Using Diagnostic Statistics RSTUDENT
4.3e Using Diagnostic Statistics RSTUDENT Details 00:00:00
4.3f Using Diagnostic Statistics DFFITS
4.3f Using Diagnostic Statistics DFFITS Details 00:00:00
4.3g Using Diagnostic Statistics DFBETAS
4.3g Using Diagnostic Statistics DFBETAS Details 00:00:00
4.3h Looking for Influential Observations, Part 1
4.3h Looking for Influential Observations, Part 1 Details 00:00:00
4.3i Looking for Influential Observations, Part 2
4.3i Looking for Influential Observations, Part 2 Details 00:00:00
4.3j Handling Influential Observations
4.3j Handling Influential Observations Details 00:00:00
4.3 Practice 2 Generating Potential Outliers
4.3 Practice 2 Generating Potential Outliers Details 00:00:00
4.3 Practice 2 Generating Potential Outliers With Solution
4.3 Practice 2 Generating Potential Outliers With Solution Details 00:00:00
4.4a Detecting Collinearity Introduction
4.4a Detecting Collinearity Introduction Details 00:00:00
4.4b Understanding Collinearity
4.4b Understanding Collinearity Details 00:00:00
4.4c The REG Procedure Detecting Collinearity
4.4c The REG Procedure Detecting Collinearity Details 00:00:00
4.4d Using Diagnostic Statistics to Detect Collinearity
4.4d Using Diagnostic Statistics to Detect Collinearity Details 00:00:00
4.4e The REG Procedure Calculating Collinearity Diagnostics
4.4e The REG Procedure Calculating Collinearity Diagnostics Details 00:00:00
4.4f The REG Procedure Dealing with Collinearity
4.4f The REG Procedure Dealing with Collinearity Details 00:00:00
4.4g Using an Effective Modeling Cycle
4.4g Using an Effective Modeling Cycle Details 00:00:00
4.4 Practice 3 Assessing Collinearity
4.4 Practice 3 Assessing Collinearity Details 00:00:00
4.4 Practice 3 Assessing Collinearity With Solution
4.4 Practice 3 Assessing Collinearity With Solution Details 00:00:00
Summary Lesson 4 Model Post-Fitting for Inference
Summary Lesson 4 Model Post-Fitting for Inference Details 00:00:00
Lession 4 Quiz
Lession 4 Quiz Details 00:00:00
Lession 4 Quiz Feedback
Lession 4 Quiz Feedback Details 00:00:00
5.1 Categorical Data Analysis Lesson OverView
5.1 Categorical Data Analysis Lesson OverView Details 00:00:00
5.2a Describing Categorical Data Introduction
5.2a Describing Categorical Data Introduction Details 00:00:00
5.2b One-Way Frequency Tables
5.2b One-Way Frequency Tables Details 00:00:00
5.2c Association between Categorical Variables
5.2c Association between Categorical Variables Details 00:00:00
5.2d Crosstabulation Tables
5.2d Crosstabulation Tables Details 00:00:00
5.2e Scenario Testing for Associations and Fitting a Logistic Model
5.2e Scenario Testing for Associations and Fitting a Logistic Model Details 00:00:00
5.2f The TABLES Statement in PROC FREQ
5.2f The TABLES Statement in PROC FREQ Details 00:00:00
5.2g Examining the Distribution of Categorical Variables
5.2g Examining the Distribution of Categorical Variables Details 00:00:00
5.2 Code Challenge
5.2 Code Challenge Details 00:00:00
5.2h Ordering the Values of an Ordinal Variable
5.2h Ordering the Values of an Ordinal Variable Details 00:00:00
5.2i Ordering the Values in a Frequency or Crosstabulation Table
5.2i Ordering the Values in a Frequency or Crosstabulation Table Details 00:00:00
5.2 Print VersioPractice 1 Examining Distributions
5.2 Print VersioPractice 1 Examining Distributions Details 00:00:00
5.2 Print VersioPractice 1 Examining Distributions With Solution
5.2 Print VersioPractice 1 Examining Distributions With Solution Details 00:00:00
5.3a Tests of Association Introduction
5.3a Tests of Association Introduction Details 00:00:00
5.3b The Pearson Chi-Square Test
5.3b The Pearson Chi-Square Test Details 00:00:00
5.3c Cramer's V Statistic
5.3c Cramer’s V Statistic Details 00:00:00
5.3d Odds Ratios
5.3d Odds Ratios Details 00:00:00
5.3e Performing a Pearson Chi-Square Test of Association
5.3e Performing a Pearson Chi-Square Test of Association Details 00:00:00
5.3f The Mantel-Haenszel Chi-Square Test
5.3f The Mantel-Haenszel Chi-Square Test Details 00:00:00
5.3 Code Challenge
5.3 Code Challenge Details 00:00:00
5.3g The Spearman Correlation Statistic
5.3g The Spearman Correlation Statistic Details 00:00:00
5.3h Performing a Mantel-Haenszel Chi-Square Test of Ordinal Association
5.3h Performing a Mantel-Haenszel Chi-Square Test of Ordinal Association Details 00:00:00
5.3b Code Challenge
5.3b Code Challenge Details 00:00:00
5.3 Practice 2 Performing Tests and Measures of Association
5.3 Practice 2 Performing Tests and Measures of Association Details 00:00:00
5.3 Practice 2 Performing Tests and Measures of Association with Solution
5.3 Practice 2 Performing Tests and Measures of Association with Solution Details 00:00:00
5.4a Introduction to Logistic Regression Introduction
5.4a Introduction to Logistic Regression Introduction Details 00:00:00
5.4b Logistic Regression
5.4b Logistic Regression Details 00:00:00
5.4c Modeling a Binary Response
5.4c Modeling a Binary Response Details 00:00:00
5.4d The LOGISTIC Procedure
5.4d The LOGISTIC Procedure Details 00:00:00
5.4e Specifying a Parameterization Method in the CLASS Statement
5.4e Specifying a Parameterization Method in the CLASS Statement Details 00:00:00
5.4f Effect Coding
5.4f Effect Coding Details 00:00:00
5.4g Reference Cell Coding
5.4g Reference Cell Coding Details 00:00:00
5.4h Fitting a Binary Logistic Regression Model
5.4h Fitting a Binary Logistic Regression Model Details 00:00:00
5.4 Code Challenge
5.4 Code Challenge Details 00:00:00
5.4i Interpreting the Odds Ratio for a Categorical Predictor
5.4i Interpreting the Odds Ratio for a Categorical Predictor Details 00:00:00
5.4j Interpreting the Odds Ratio for a Continuous Predictor
5.4j Interpreting the Odds Ratio for a Continuous Predictor Details 00:00:00
5.4k Comparing Pairs to Assess the Fit of a Logistic Regression Model
5.4k Comparing Pairs to Assess the Fit of a Logistic Regression Model Details 00:00:00
5.4 Practice 3 Performing a Binary Logistic Regression Analysis
5.4 Practice 3 Performing a Binary Logistic Regression Analysis Details 00:00:00
5.4 Practice 3 Performing a Binary Logistic Regression Analysis With Solution
5.4 Practice 3 Performing a Binary Logistic Regression Analysis With Solution Details 00:00:00
5.5a Multiple Logistic Regression Introduction
5.5a Multiple Logistic Regression Introduction Details 00:00:00
5.5b Multiple Logistic Regression
5.5b Multiple Logistic Regression Details 00:00:00
5.5c The Backward Elimination Method of Variable Selection
5.5c The Backward Elimination Method of Variable Selection Details 00:00:00
5.5d Adjusted Odds Ratios
5.5d Adjusted Odds Ratios Details 00:00:00
5.5e Specifying the Variable Selection Method in the MODEL Statement
5.5e Specifying the Variable Selection Method in the MODEL Statement Details 00:00:00
5.5f The UNITS Statement
5.5f The UNITS Statement Details 00:00:00
5.5g Fitting a Multiple Logistic Regression Model
5.5g Fitting a Multiple Logistic Regression Model Details 00:00:00
5.5h Comparing the Binary and Multiple Logistic Regression Models
5.5h Comparing the Binary and Multiple Logistic Regression Models Details 00:00:00
5.5i Specifying a Formatted Value as a Reference Level
5.5i Specifying a Formatted Value as a Reference Level Details 00:00:00
5.5a Code Challenge
5.5a Code Challenge Details 00:00:00
5.5j Interactions between Variables
5.5j Interactions between Variables Details 00:00:00
5.5k The Backward Elimination Method with Interactions in the Model
5.5k The Backward Elimination Method with Interactions in the Model Details 00:00:00
5.5l Specifying Interactions in the MODEL Statement
5.5l Specifying Interactions in the MODEL Statement Details 00:00:00
5.5b Code Challenge
5.5b Code Challenge Details 00:00:00
5.5m Fitting a Multiple Logistic Regression Model with Interactions
5.5m Fitting a Multiple Logistic Regression Model with Interactions Details 00:00:00
5.5n The ODDSRATIO Statement
5.5n The ODDSRATIO Statement Details 00:00:00
5.5o Fitting a Multiple Logistic Regression Model with All Odds Ratios
5.5o Fitting a Multiple Logistic Regression Model with All Odds Ratios Details 00:00:00
5.5p Comparing the Multiple Logistic Regression Models
5.5p Comparing the Multiple Logistic Regression Models Details 00:00:00
5.5q Interaction Plots
5.5q Interaction Plots Details 00:00:00
5.5c Code Challenge
5.5c Code Challenge Details 00:00:00
5.5r Saving Analysis Results with the STORE Statement
5.5r Saving Analysis Results with the STORE Statement Details 00:00:00
5.5s Generating Predictions Using PROC PLM
5.5s Generating Predictions Using PROC PLM Details 00:00:00
5.5 Practice 4 Fitting a Multiple Logistic Regression Model, Saving Analysis Results, and Generating Predictions
5.5 Practice 4 Fitting a Multiple Logistic Regression Model, Saving Analysis Results, and Generating Predictions Details 00:00:00
5.5 Practice 4 Fitting a Multiple Logistic Regression Model, Saving Analysis Results, and Generating Predictions With Solution
5.5 Practice 4 Fitting a Multiple Logistic Regression Model, Saving Analysis Results, and Generating Predictions With Solution Details 00:00:00
Summary Lesson 5 Categorical Data Analysis
Summary Lesson 5 Categorical Data Analysis Details 00:00:00
Lession 5 Quiz
Lession 5 Quiz Details 00:00:00
Lession 5 Quiz Feedback
Lession 5 Quiz Feedback Details 00:00:00
6.1 Model Building and Scoring for Prediction Lesson Overview
6.1 Model Building and Scoring for Prediction Lesson Overview Details 00:00:00
6.2a Introduction to Predictive Modeling Introduction
6.2a Introduction to Predictive Modeling Introduction Details 00:00:00
6.2b What Is Predictive Modeling
6.2b What Is Predictive Modeling Details 00:00:00
6.2c Model Complexity
6.2c Model Complexity Details 00:00:00
6.2d Building a Predictive Model
6.2d Building a Predictive Model Details 00:00:00
6.2e PROC GLMSELECT to Build a Predictive Model
6.2e PROC GLMSELECT to Build a Predictive Model Details 00:00:00
6.2f Scenario Building a Predictive Model
6.2f Scenario Building a Predictive Model Details 00:00:00
6.2g Building a Predictive Model
6.2g Building a Predictive Model Details 00:00:00
6.2 Practice 1 Building a Predictive Mode
6.2 Practice 1 Building a Predictive Mode Details 00:00:00
6.2 Practice 1 Building a Predictive Mode With Solution
6.2 Practice 1 Building a Predictive Mode With Solution Details 00:00:00
6.3a Scoring Predictive Models Introduction
6.3a Scoring Predictive Models Introduction Details 00:00:00
6.3b Preparing for Scoring
6.3b Preparing for Scoring Details 00:00:00
6.3c Methods of Scoring
6.3c Methods of Scoring Details 00:00:00
6.3d Scenario Scoring Data
6.3d Scenario Scoring Data Details 00:00:00
6.3e Scoring Data
6.3e Scoring Data Details 00:00:00
6.3 Practice 2 Using a Predictive Model to Score New Data
6.3 Practice 2 Using a Predictive Model to Score New Data Details 00:00:00
6.3 Practice 2 Using a Predictive Model to Score New Data With Solution
6.3 Practice 2 Using a Predictive Model to Score New Data With Solution Details 00:00:00
Summary Lesson 6 Model Building and Scoring Prediction
Summary Lesson 6 Model Building and Scoring Prediction Details 00:00:00
Lession 6 Quiz
Lession 6 Quiz Details 00:00:00
Lession 6 Quiz Feedback
Lession 6 Quiz Feedback Details 00:00:00
2.5g Performing ANOVA with Blocking
2.5g Performing ANOVA with Blocking Details 00:00:00
Course Notes: Statistics 1
Course Notes: Statistics 1 Details 00:00:00
3.2 Practice 1 Describe Relationships between Continuous Variables With Solution
3.2 Practice 1 Describe Relationships between Continuous Variables With Solution Details 00:00:00
3.3q The SCORE Procedure Scoring Predicted Values Using Parameter Estimates
3.3q The SCORE Procedure Scoring Predicted Values Using Parameter Estimates Details 00:00:00
3.4i Fitting a Multiple Linear Regression Model
3.4i Fitting a Multiple Linear Regression Model Details 00:00:00

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