Канал: Statistics of DOOM

Python - Chatbots (2024)

Python - Chatbots (2024)

R & Python - Random Forests and Conditional Inference Trees 2 (2022)

R & Python - Random Forests and Conditional Inference Trees 2 (2022)

R & Python - Random Forests and Conditional Inference Trees (2022)

R & Python - Random Forests and Conditional Inference Trees (2022)

R & Python - Introduction to Human Language Modeling (2022)

R & Python - Introduction to Human Language Modeling (2022)

R & Python - Sentiment Analysis Part 1 (2022)

R & Python - Sentiment Analysis Part 1 (2022)

R & Python - Classification Part 2 (2022)

R & Python - Classification Part 2 (2022)

R & Python - Parsing Part 2 (2022)

R & Python - Parsing Part 2 (2022)

R & Python - Parsing Part 1.1 (2022)

R & Python - Parsing Part 1.1 (2022)

R & Python - Parsing Part 1 (2022)

R & Python - Parsing Part 1 (2022)

R & Python - Entity Recognition Part 2 (2022)

R & Python - Entity Recognition Part 2 (2022)

R & Python - Entity Recognition Part 1 (2022)

R & Python - Entity Recognition Part 1 (2022)

R & Python - Part of Speech Tagging Part 2 (2022)

R & Python - Part of Speech Tagging Part 2 (2022)

R & Python - Part of Speech Tagging Part 1.1 (2022)

R & Python - Part of Speech Tagging Part 1.1 (2022)

R & Python - Part of Speech Tagging Part 1 (2022)

R & Python - Part of Speech Tagging Part 1 (2022)

R & Python - Processing Raw Text Part 2.1 (2022)

R & Python - Processing Raw Text Part 2.1 (2022)

R & Python - Processing Raw Text Part 2 (2022)

R & Python - Processing Raw Text Part 2 (2022)

R & Python - Processing Raw Text Part 1 (2022)

R & Python - Processing Raw Text Part 1 (2022)

R & Python - Introduction to Natural Language Processing (2022)

R & Python - Introduction to Natural Language Processing (2022)

R - ANOVA (12.3 Flip)

R - ANOVA (12.3 Flip)

R - ANOVA (12.2 Flip)

R - ANOVA (12.2 Flip)

R - ANOVA (12.1 Flip)

R - ANOVA (12.1 Flip)

R - Dependent t-Test (11.2 Flip)

R - Dependent t-Test (11.2 Flip)

R - Independent t-Test (11.1 Flip)

R - Independent t-Test (11.1 Flip)

R - Moderation (10.2 Flip)

R - Moderation (10.2 Flip)

R - Mediation (10.1 Flip)

R - Mediation (10.1 Flip)

R - Regression (9.3 Flip)

R - Regression (9.3 Flip)

R - Regression (9.2 Flip)

R - Regression (9.2 Flip)

R - Regression (9.1 Flip)

R - Regression (9.1 Flip)

Psychonomics 2022: The SPAML and PSA

Psychonomics 2022: The SPAML and PSA

R - Correlation (8.2 Flip)

R - Correlation (8.2 Flip)

R - Correlation (8.1 Flip)

R - Correlation (8.1 Flip)

R - Data Screening Assumptions (7.2 Flip)

R - Data Screening Assumptions (7.2 Flip)

R - Data Screening Assumptions (7.1 Flip)

R - Data Screening Assumptions (7.1 Flip)

R - Data Screening Outliers (6.3 Flip)

R - Data Screening Outliers (6.3 Flip)

R - Data Screening Missing (6.2 Flip)

R - Data Screening Missing (6.2 Flip)

R & Python - Deep Learning

R & Python - Deep Learning

R - Data Screening Accuracy (6.1 Flip)

R - Data Screening Accuracy (6.1 Flip)

R & Python - word2vec

R & Python - word2vec

R - Data Visualization (5.2 Flip)

R - Data Visualization (5.2 Flip)

R & Python - Similarity

R & Python - Similarity

R - Data Visualization (5.1 Flip)

R - Data Visualization (5.1 Flip)

R & Python - Network Analysis

R & Python - Network Analysis

R - More Statistics (4.2 Flip)

R - More Statistics (4.2 Flip)

R & Python - Topic Models

R & Python - Topic Models

R - More Statistics (4.1 Flip)

R - More Statistics (4.1 Flip)

R & Python - Latent Semantic Analysis

R & Python - Latent Semantic Analysis

R - Basic Statistics (3.2 Flip)

R - Basic Statistics (3.2 Flip)

R & Python - Linear Regression

R & Python - Linear Regression

R - Basic Statistics (3.1 Flip)

R - Basic Statistics (3.1 Flip)

R & Python - Exploratory Factor Analysis

R & Python - Exploratory Factor Analysis

R - Introduction to Data Analytics (2.3 Flip)

R - Introduction to Data Analytics (2.3 Flip)

R & Python - Cluster Analysis

R & Python - Cluster Analysis

R - Introduction to Data Analytics (2.2 Flip)

R - Introduction to Data Analytics (2.2 Flip)

R & Python - Logistic Regression

R & Python - Logistic Regression

R - Introduction to Data Analytics (2.1 Flip)

R - Introduction to Data Analytics (2.1 Flip)

R & Python - Correspondence Analysis

R & Python - Correspondence Analysis

R - Introduction to R (1.3 Flip)

R - Introduction to R (1.3 Flip)

R & Python - Conditional Inference Trees

R & Python - Conditional Inference Trees

R - Introduction to R (1.2 Flip)

R - Introduction to R (1.2 Flip)

R & Python - Sentiment Analysis Part 2 (2022)

R & Python - Sentiment Analysis Part 2 (2022)

R & Python - Correspondence Analysis

R & Python - Correspondence Analysis

R - Introduction to R (1.1 Flip)

R - Introduction to R (1.1 Flip)

R & Python - Introduction to Human Language Modeling

R & Python - Introduction to Human Language Modeling

R & Python - Sentiment Analysis Part 2

R & Python - Sentiment Analysis Part 2

R & Python - Sentiment Analysis Part 1

R & Python - Sentiment Analysis Part 1

R & Python - Classification Part 2

R & Python - Classification Part 2

R & Python - Classification Part 1

R & Python - Classification Part 1

R & Python - Parsing Part 2

R & Python - Parsing Part 2

R & Python - Parsing Part 1

R & Python - Parsing Part 1

R & Python - Entity Recognition Part 2

R & Python - Entity Recognition Part 2

R & Python - Entity Recognition Part 1

R & Python - Entity Recognition Part 1

R & Python - Part of Speech Tagging Part 2

R & Python - Part of Speech Tagging Part 2

R & Python - Part of Speech Tagging Part 1

R & Python - Part of Speech Tagging Part 1

R & Python - Classification Part 1 (2022)

R & Python - Classification Part 1 (2022)

R & Python - Processing Raw Text Part 2

R & Python - Processing Raw Text Part 2

R & Python - Processing Raw Text Part 1

R & Python - Processing Raw Text Part 1

R & Python - Introduction to Natural Language Processing

R & Python - Introduction to Natural Language Processing

Psychonomics 2021 - Creating a cross-referenced multi-linguistic dataset

Psychonomics 2021 - Creating a cross-referenced multi-linguistic dataset

SCIP 2021 - Adaptive Algorithms for Stimuli Sampling in Cognitive Studies

SCIP 2021 - Adaptive Algorithms for Stimuli Sampling in Cognitive Studies

R - Register Variation with Exploratory Factor Analysis

R - Register Variation with Exploratory Factor Analysis

R - Linguistic Geography with Multidimensional Scaling

R - Linguistic Geography with Multidimensional Scaling

R - Latent Semantic Analysis

R - Latent Semantic Analysis

R - Behavioral Profiles and Clustering

R - Behavioral Profiles and Clustering

R - Conditional Inference Trees and Random Forests

R - Conditional Inference Trees and Random Forests

R - Grammar Analysis with Logistic Regression

R - Grammar Analysis with Logistic Regression

R - Distinctive Collexeme Analysis

R - Distinctive Collexeme Analysis

R - Association Measures: Collocations, PMI, and Co-occurrence

R - Association Measures: Collocations, PMI, and Co-occurrence

R - Frequency and Response Latency with Linear Regression

R - Frequency and Response Latency with Linear Regression

Python - Part of Speech Tagging Part 1

Python - Part of Speech Tagging Part 1

R - Item Response Theory Lecture

R - Item Response Theory Lecture

R - Latent Growth Models Lecture

R - Latent Growth Models Lecture

R - Multigroup CFA Lecture

R - Multigroup CFA Lecture

R - Multitrait Multimethod (MTMM) Lecture

R - Multitrait Multimethod (MTMM) Lecture

R - Full Structural Equation Models Lecture

R - Full Structural Equation Models Lecture

R - Hierarchical Confirmatory Factor Analysis Lecture

R - Hierarchical Confirmatory Factor Analysis Lecture

R - Confirmatory Factor Analysis Lecture

R - Confirmatory Factor Analysis Lecture

R - Path Analysis, Estimation, Fit Indices Part 2

R - Path Analysis, Estimation, Fit Indices Part 2

R - Path Analysis, Estimation, Fit Indices Part 1

R - Path Analysis, Estimation, Fit Indices Part 1

R - Human Language Introduction + Descriptive Statistics

R - Human Language Introduction + Descriptive Statistics

Python - Building Feature Grammars Part 2

Python - Building Feature Grammars Part 2

R - Multigroup CFA Class Assignment (2020)

R - Multigroup CFA Class Assignment (2020)

Python - Building Feature Grammars Part 1

Python - Building Feature Grammars Part 1

R - Multitrait Multimethod (MTMM) Class Assignment

R - Multitrait Multimethod (MTMM) Class Assignment

Python - Analyzing Sentence Structure

Python - Analyzing Sentence Structure

Python - Extracting Information from Text

Python - Extracting Information from Text

Python - Classifying Text Part 2

Python - Classifying Text Part 2

Python - Part of Speech Tagging Part 2

Python - Part of Speech Tagging Part 2

Python - Processing Raw Text Part 2

Python - Processing Raw Text Part 2

Python - Processing Raw Text Part 1

Python - Processing Raw Text Part 1

Psychonomics 2020 - The Psychological Science Accelerator

Psychonomics 2020 - The Psychological Science Accelerator

Python - Text Corpora and Lexical Resources Part 2

Python - Text Corpora and Lexical Resources Part 2

SCIP 2020 - Testing the Assumption of Model Similarity Across Languages

SCIP 2020 - Testing the Assumption of Model Similarity Across Languages

R - Terminology Lecture

R - Terminology Lecture

Python - Text Corpora and Lexical Resources Part 1

Python - Text Corpora and Lexical Resources Part 1

SCIP 2020 - lrd: An R Package and Shiny Application for Lexical Response Data

SCIP 2020 - lrd: An R Package and Shiny Application for Lexical Response Data

R - Exploratory Factor Analysis Lecture

R - Exploratory Factor Analysis Lecture

SCIP 2020 - Using STRUDEL for Semantic Concept-Feature Norms

SCIP 2020 - Using STRUDEL for Semantic Concept-Feature Norms

R - Data Screening Lecture

R - Data Screening Lecture

R - SEM Terminology Assignment Worked

R - SEM Terminology Assignment Worked

Python - Introduction to Natural Language Processing, Part 2

Python - Introduction to Natural Language Processing, Part 2

Psychonomics 2020 - Power Simulations for Linguistic Data

Psychonomics 2020 - Power Simulations for Linguistic Data

R - Introduction to R Part 2

R - Introduction to R Part 2

R - Introduction to R Part 1

R - Introduction to R Part 1

Python - Introduction to Natural Language Processing, Part 1

Python - Introduction to Natural Language Processing, Part 1

R - learnSEM package now available!

R - learnSEM package now available!

JASP - Chi-Square of Independence

JASP - Chi-Square of Independence

JASP - Multiple Linear Regression

JASP - Multiple Linear Regression

JASP - Simple Linear Regression

JASP - Simple Linear Regression

JASP - Pearson's Correlation

JASP - Pearson's Correlation

JASP - One Way Repeated Measures ANOVA

JASP - One Way Repeated Measures ANOVA

JASP - One Way Between Subjects ANOVA

JASP - One Way Between Subjects ANOVA

JASP - Single Sample t-test

JASP - Single Sample t-test

JASP - Independent t

JASP - Independent t

Creating Open Data Part 3 - Upload and Share

Creating Open Data Part 3 - Upload and Share

Creating Open Data Part 2 - Data Dictionaries

Creating Open Data Part 2 - Data Dictionaries

Creating Open Data Part 2 - Codebooks

Creating Open Data Part 2 - Codebooks

Creating Open Data Part 1 - Download Data

Creating Open Data Part 1 - Download Data

JASP - Dependent t-tests

JASP - Dependent t-tests

JASP - How to use JASP with RollApp (2020 Update)

JASP - How to use JASP with RollApp (2020 Update)

R & Python in RStudio Cloud

R & Python in RStudio Cloud

R - Practical Primer on Text Processing

R - Practical Primer on Text Processing

R - Multigroup CFA Class Assignment 2

R - Multigroup CFA Class Assignment 2

R - Multigroup CFA Class Assignment 1

R - Multigroup CFA Class Assignment 1

R - Multigroup CFA with lavaan Example

R - Multigroup CFA with lavaan Example

R - Binary Logistic Multilevel Models

R - Binary Logistic Multilevel Models

R - Correspondence Analysis

R - Correspondence Analysis

R - Topics Models

R - Topics Models

Python - Classifying Text Part 1

Python - Classifying Text Part 1

R - Multilevel Models Workshop Part 2

R - Multilevel Models Workshop Part 2

R - Multilevel Models Workshop Part 1

R - Multilevel Models Workshop Part 1

R - Constructions and Distinctive Collexeme Analysis

R - Constructions and Distinctive Collexeme Analysis

R - Grammar Analysis with Logistic Regression

R - Grammar Analysis with Logistic Regression

NLP - What is Natural Language Processing?

NLP - What is Natural Language Processing?

Python - Installing Add-Ons for Jupyter (Mac)

Python - Installing Add-Ons for Jupyter (Mac)

Python - Installing Add-Ons for Jupyter (Windows)

Python - Installing Add-Ons for Jupyter (Windows)

Python - How to Install Anaconda Distribution

Python - How to Install Anaconda Distribution

JASP - How to Calculate Descriptives

JASP - How to Calculate Descriptives

JASP - Entering/Edit Data and Excel Graphs

JASP - Entering/Edit Data and Excel Graphs

JASP - Basics of JASP and Histograms

JASP - Basics of JASP and Histograms

JASP - How to use JASP with RollApp

JASP - How to use JASP with RollApp

JASP - How to Install JASP

JASP - How to Install JASP

Excel - One Way ANOVA (Excel for Data Science 7)

Excel - One Way ANOVA (Excel for Data Science 7)

R - Double Moderation with Covariates MeMoBootR

R - Double Moderation with Covariates MeMoBootR

SPSS - Double Moderation with PROCESS and Covariates (Model 2)

SPSS - Double Moderation with PROCESS and Covariates (Model 2)

Excel - Independent t (Excel for Data Science 6)

Excel - Independent t (Excel for Data Science 6)

R - Serial Two Part Mediation with Covariates MeMoBootR

R - Serial Two Part Mediation with Covariates MeMoBootR

SPSS - Serial Mediation with PROCESS and Covariates (Model 6)

SPSS - Serial Mediation with PROCESS and Covariates (Model 6)

JASP - Structural Equation Modeling

JASP - Structural Equation Modeling

Excel - Dependent t (Excel for Data Science 5)

Excel - Dependent t (Excel for Data Science 5)

Excel - Single Sample t (Excel for Data Science 4)

Excel - Single Sample t (Excel for Data Science 4)

R - Moderation with Categorical M MeMoBootR

R - Moderation with Categorical M MeMoBootR

SPSS - Moderation with PROCESS Categorical M (Model 1)

SPSS - Moderation with PROCESS Categorical M (Model 1)

Excel - Correlation (Excel for Data Science 3)

Excel - Correlation (Excel for Data Science 3)

R - Moderation and Simple Slopes MeMoBootR

R - Moderation and Simple Slopes MeMoBootR

SPSS - Moderation with PROCESS with Covariates (Model 1)

SPSS - Moderation with PROCESS with Covariates (Model 1)

Excel - Functions, Data Manipulation (Excel for Data Science 2)

Excel - Functions, Data Manipulation (Excel for Data Science 2)

R - Mediation with Categorical X MeMoBootR

R - Mediation with Categorical X MeMoBootR

SPSS - Mediation with PROCESS Categorical Variables (Model 4)

SPSS - Mediation with PROCESS Categorical Variables (Model 4)

Excel - Tidy Data (Excel for Data Science 1)

Excel - Tidy Data (Excel for Data Science 1)

R - Mediation with Covariates MeMoBootR

R - Mediation with Covariates MeMoBootR

SPSS - Mediation with PROCESS and Covariates (Model 4)

SPSS - Mediation with PROCESS and Covariates (Model 4)

SPSS - Moderation with PROCESSv3 MPA Talk

SPSS - Moderation with PROCESSv3 MPA Talk

SPSS - Mediation with PROCESSv3 MPA Talk

SPSS - Mediation with PROCESSv3 MPA Talk

MOTE - Dependent t (Shiny App)

MOTE - Dependent t (Shiny App)

MOTE - Independent t (Shiny App)

MOTE - Independent t (Shiny App)

MOTE - Z Tests (Shiny App)

MOTE - Z Tests (Shiny App)

MOTE - Chi-Square (Shiny App)

MOTE - Chi-Square (Shiny App)

MOTE - d to r/R2 (Shiny App)

MOTE - d to r/R2 (Shiny App)

MOTE - Eta/Omega from F values (Shiny App)

MOTE - Eta/Omega from F values (Shiny App)

MOTE - Eta/Omega/Epsilon from SS (Shiny App)

MOTE - Eta/Omega/Epsilon from SS (Shiny App)

MOTE - Partial Omega (Shiny App)

MOTE - Partial Omega (Shiny App)

MOTE - Partial Eta (Shiny App)

MOTE - Partial Eta (Shiny App)

MOTE - Single t (Shiny App)

MOTE - Single t (Shiny App)

MOTE - Independent Proportions (Shiny App)

MOTE - Independent Proportions (Shiny App)

MOTE - Partial GES/Omega (Shiny App)

MOTE - Partial GES/Omega (Shiny App)

Screen Low Quality Data in Browser (Shiny R)

Screen Low Quality Data in Browser (Shiny R)

R - Screen Low Quality Data

R - Screen Low Quality Data

R - Exploratory Factor Analysis Example

R - Exploratory Factor Analysis Example

R - Multinomial Logisitic Regression Example

R - Multinomial Logisitic Regression Example

R - Mediation Analyses with the multilevel package

R - Mediation Analyses with the multilevel package