Category:Automation Online Library
This page links to the contents of the online automation library available from within Q version 4.8 and later.
These items can be used in Q by selecting Automate > Browse Online Library. Those items which are Rules are marked with 
and those items which are QScripts are marked with 
Pages in category ‘Automation Online Library’
The following 200 pages are in this category, out of 394 total.
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C
- Choice Modeling - Compare Models
- Choice Modeling - Diagnostic - Class Parameters
- Choice Modeling - Diagnostic - Experimental Design - Balances and Overlaps
- Choice Modeling - Diagnostic - Experimental Design - Numeric Design
- Choice Modeling - Diagnostic - Experimental Design - Standard Errors
- Choice Modeling - Diagnostic - Parameter Statistics
- Choice Modeling - Diagnostic - Posterior Intervals
- Choice Modeling - Diagnostic - Trace Plots
- Choice Modeling - Ensemble
- Choice Modeling - Experimental Design
- Choice Modeling - Export Design To Qualtrics
- Choice Modeling - Hierarchical Bayes
- Choice Modeling - Latent Class Analysis
- Choice Modeling - Legacy Choice Modeling - Choice-Based Conjoint (CBC) Setup - JMP
- Choice Modeling - Legacy Choice Modeling - Choice-Based Conjoint (CBC) Setup - Sawtooth CHO Format
- Choice Modeling - Legacy Choice Modeling - Choice-Based Conjoint (CBC) Setup - Sawtooth Dual File Format
- Choice Modeling - Legacy Choice Modeling - Compute Importance
- Choice Modeling - Legacy Choice Modeling - Compute Utilities
- Choice Modeling - Legacy Choice Modeling - Compute Utilities with 0 to 100 Scaling
- Choice Modeling - Legacy Choice Modeling - Compute Within-Attribute Preference Shares (Utilities)
- Choice Modeling - Legacy Choice Modeling - Compute Zero-Centered Diffs (Utilities)
- Choice Modeling - Multinomial Logit
- Choice Modeling - Preview Choice Questionnaire
- Choice Modeling - Save Variable(s) - Class Membership Probabilities
- Choice Modeling - Save Variable(s) - Individual-level Coefficients
- Choice Modeling - Save Variable(s) - Membership
- Choice Modeling - Save Variable(s) - Proportion Correct
- Choice Modeling - Save Variable(s) - RLH
- Choice Modeling - Save Variable(s) - Utilities (Mean 0)
- Choice Modeling - Save Variable(s) - Utilities (Mean 0, Max range 100)
- Choice Modeling - Save Variable(s) - Utilities (Mean 0, Mean range 100)
- Choice Modeling - Save Variable(s) - Utilities (Min 0)
- Choice Modeling - Save Variable(s) - Utilities (Min 0, Max range 100)
- Choice Modeling - Save Variable(s) - Utilities (Min 0, Mean range 100)
- Choice Modeling - Utilities Plot
- Classifier - Classification And Regression Trees (CART)
- Classifier - Compare Models
- Classifier - Deep Learning
- Classifier - Diagnostic - Discriminant Functions
- Classifier - Diagnostic - Prediction-Accuracy Table
- Classifier - Ensemble
- Classifier - Gradient Boosting
- Classifier - Linear Discriminant Analysis
- Classifier - Random Forest
- Classifier - Save Variable(s) - Discriminant Variables
- Classifier - Save Variable(s) - Predicted Values
- Classifier - Save Variable(s) - Probabilities
- Classifier - Support Vector Machine
- Computations - Add Naive Total For Each Column
- Computations - Add Naive Total For Each Row
- Computations - Adding Average Columns to Tables Containing Spans
- Computations - Adding Average Rows to Tables Containing Spans
- Computations - Adding Rows to Display the Effective Column n
- Computations - The Average of Each Column
- Computations - The Average of Each Row
- Computations - The Difference Between a Pair of Columns
- Computations - The Difference Between a Pair of Rows
- Computations - The Weighted Average of Each Column
- Correlation - Correlation Matrix
- Correlation - Distances
- Correlation - Scatterplot Matrix
- Create New Variables - Binary
- Create New Variables - Case-Level Shares
- Create New Variables - Combine Several Questions as a Grid
- Create New Variables - Create New Bottom 2 Category Variables (That is, Bottom 2 Boxes)
- Create New Variables - Create New Bottom 3 Category Variables (That is, Bottom 3 Boxes)
- Create New Variables - Create New Bottom K Category Variables (That is, Bottom K Boxes)
- Create New Variables - Create New Top 2 Category Variables (That is, Top 2 Boxes)
- Create New Variables - Create New Top 3 Category Variables (That is, Top 3 Boxes)
- Create New Variables - Create New Top K Category Variables (That is, Top K Boxes)
- Create New Variables - Create Variables from Merged Categories
- Create New Variables - Flatten
- Create New Variables - Identifying and Removing Outliers
- Create New Variables - Log
- Create New Variables - Midpoint Coding and Quantification
- Create New Variables - NPS Recoding
- Create New Variables - Rebase Multiple Response Data to NET
- Create New Variables - Scale Within Case - Center
- Create New Variables - Scale Within Case - Rank
- Create New Variables - Scale Within Case - Standardize
- Create New Variables - Scale Within Case - Unit
- Create New Variables - Scale Within Variable - Center
- Create New Variables - Scale Within Variable - Rank
- Create New Variables - Scale Within Variable - Standardize
- Create New Variables - Scale Within Variable - Unit
- Create New Variables - Square-root
- Create New Variables - Translate
- Custom Data Files - Survey Gizmo Conjoint
- Custom Data Files - Survey Gizmo MaxDiff
D
- Data - Geocode IPs
- Data - Google Trends
- Data - Sample Size Description
- Data - Stock Prices
- Delete tables and plots - Broken tables
- Delete tables and plots - If not significant at a specified level
- Delete tables and plots - If not significant at the 0.001 level (99.9%)
- Delete tables and plots - If not significant at the 0.01 level (99%)
- Delete tables and plots - If not significant at the 0.05 level (95%)
- Delete tables and plots - Remove Tables With Sample Size Lower Than Specified Value
- Dimension Reduction - Correspondence Analysis of a Square Table
- Dimension Reduction - Correspondence Analysis of a Table
- Dimension Reduction - Diagnostic - Quality
- Dimension Reduction - Dimension Reduction Scatterplot
- Dimension Reduction - Multidimensional Scaling (MDS)
- Dimension Reduction - Multiple Correspondence Analysis
- Dimension Reduction - Plot - Component Plot
- Dimension Reduction - Plot - Goodness of Fit
- Dimension Reduction - Plot - Scree
- Dimension Reduction - Principal Components Analysis
- Dimension Reduction - Principal Components Analysis Biplot
- Dimension Reduction - Save Variable(s)
- Dimension Reduction - t-SNE
F
H
M
- Machine Learning - Classification And Regression Trees (CART)
- Machine Learning - Compare Models
- Machine Learning - Deep Learning
- Machine Learning - Diagnostic - Discriminant Functions
- Machine Learning - Diagnostic - Prediction-Accuracy Table
- Machine Learning - Ensemble
- Machine Learning - Gradient Boosting
- Machine Learning - Linear Discriminant Analysis
- Machine Learning - Random Forest
- Machine Learning - Save Variable(s) - Discriminant Variables
- Machine Learning - Save Variable(s) - Predicted Values
- Machine Learning - Save Variable(s) - Probabilities
- Machine Learning - Support Vector Machine
- Marketing - Brand Health Table
- Marketing - Legacy TURF (Total Unduplicated Reach and Frequency)
- Marketing - MaxDiff - Analyze as a Ranking Question - Compute Preference Shares from Individual-Level Parameter Means (All Alternatives)
- Marketing - MaxDiff - Analyze as a Ranking Question - Compute Sawtooth-Style Preference Shares from Individual-Level Parameter Means (K Alternatives)
- Marketing - MaxDiff - Analyze as a Ranking Question - Compute Zero-Centered Utilities from Individual-Level Parameter Means (All Alternatives)
- Marketing - MaxDiff - Analyze as a Ranking Question - MaxDiff Setup from an Experimental Design
- Marketing - MaxDiff - Compare Models
- Marketing - MaxDiff - Diagnostic - Class Parameters
- Marketing - MaxDiff - Diagnostic - Class Preference Shares
- Marketing - MaxDiff - Diagnostic - Parameter Statistics
- Marketing - MaxDiff - Diagnostic - Posterior Intervals
- Marketing - MaxDiff - Diagnostic - Trace Plots
- Marketing - MaxDiff - Ensemble
- Marketing - MaxDiff - Experimental Design
- Marketing - MaxDiff - Hierarchical Bayes
- Marketing - MaxDiff - Latent Class Analysis
- Marketing - MaxDiff - Multinomial Logit
- Marketing - MaxDiff - Save Variable(s) - Class Membership Probabilities
- Marketing - MaxDiff - Save Variable(s) - Compute Preference Shares
- Marketing - MaxDiff - Save Variable(s) - Compute Sawtooth-Style Preference Shares (K Alternatives)
- Marketing - MaxDiff - Save Variable(s) - Compute Zero-Centered Utilities
- Marketing - MaxDiff - Save Variable(s) - Individual-level Coefficients
- Marketing - MaxDiff - Save Variable(s) - Membership
- Marketing - MaxDiff - Save Variable(s) - Proportion Correct
- Marketing - MaxDiff - Save Variable(s) - RLH
- Marketing - MaxDiff - Varying Coefficients
- Marketing - Price Sensitivity Meter
- Missing Data - Little's MCAR Test
- Missing Data - Plot by Case
- Missing Data - Plot of Patterns
- Missing Data - Save Variable(s) - Complete Cases
- Modifying Cell Content - Add Symbols to Significant Cells
- Modifying Cell Content - Adding Text to Cells with Large Numbers
- Modifying Cell Content - Blanking Cells With Small Sample Sizes
- Modifying Cell Content - Blanking Cells With Small Values
- Modifying Cell Content - Changing the Color of Cells With Small Sample Sizes
- Modifying Cell Content - Changing the Missing Data (NaN) Symbol
- Modifying Cell Content - Color - Color Selected Rows or Columns
- Modifying Cell Content - Color - Shade Rows or Columns
- Modifying Cell Content - Color - Shade Spans
- Modifying Cell Content - Highlighting Cells Above and Below NET
- Modifying Cell Content - Remove Decimals Without Rounding
- Modifying Cell Content - Replace Small Values With Text
- Modifying Cell Content - Round Data
- Modifying Cell Content - Shading NET Rows and Columns
- Modifying Data - Apply Custom Merges to Scales
- Modifying Data - Create New Bottom K Category NETs (That is, Bottom K Boxes)
- Modifying Data - Create New Top K Category NETs (That is, Top K Boxes)
- Modifying Data - Merge 10 Point Scales Into 3 Categories
- Modifying Data - Merge 7 Point Scales Into 3 Categories
- Modifying Data - Merge and Flatten Scales
- Modifying Data - Merge Categories Less Than or Equal To 0.5%
- Modifying Data - Merge Categories Less Than or Equal To 1%
- Modifying Data - Merge Categories Less Than or Equal To 2%
- Modifying Data - Merge Categories Less Than or Equal To 3%
- Modifying Data - Merge Categories Less Than or Equal To 4%
- Modifying Data - Merge Categories Less Than or Equal To 5%
- Modifying Data - Move the NET to the Top for all Questions
