Refine your search
Availability
-
Authors
- Anderson, Rolf E
- Anderson, Rolph E
- Babin, Barry J
- Black, William C
- Brown, David S
- Dangeti, Pratap
- Fidell, Linda S
- Gorard, Stephen
- Hair, Joseph F
- Jaccard, James
- Johnson, R Burke (Ed.)
- Liu, Xing
- Mehmetoglu, Mehmet
- Menard, Scott
- Mittner, Matthias
- O'Connell, Ann A
- Pampel, Fred C
- Tabachnick, Barbara G
- Tatham, Ronald L
- Vogt, W Paul (Ed.)
- Show more
- Show less
-
Collections
-
Holding libraries
-
Item types
-
Locations
-
Series
-
Topics
- Analysis
- Applied Statistics
- Art
- Assumptions
- Basic Statistics
- Basic Technique
- Basic Techniques
- Bayesian Generalized Linear Models
- Bayesian Statistics
- Binary Data
- Bivariate Description Data
- Bivariate Regression
- Bivariate Statistics
- Categorical Data Analysis
- Central Dispersion
- Central Tendency
- Cluster Analysis
- Comparision
- Components Analysis
- Computer Language
- Computer Programme
- Computer Programme Language
- Concomitants
- Conjoint Analysis
- Coorelation Analysis
- Correspondence Analysis
- Covariance Analysis
- Data
- Data Analysis
- Data Management
- Data Reduction Techniques
- Data Visualization
- Dependence Technique
- Dependence Techniques
- Descriptive Statistics
- Discriminant Analysis
- Dummy Variable Regression
- Essentials
- Factor Analysis
- Failure Analysis
- Foundations
- General Linear Model
- Guide
- Inherently Nonlinear Models
- Interdependence Technique
- Interdependence Techniques
- K-Nearest Neighbors
- Language
- Latent Class Modeling
- Limitation
- Linear Regression
- Log Linear Models
- Logistic Regression
- Logistic Regression Analysis
- Logistic Regression Models
- Longitudinal Analysis
- Machine Learning
- Machine Learning Models
- Machine Learning Technology
- Machine Learning Terminology
- Methods
- MLM
- Monte Carlo Methods
- Multi Level Regression Modeling
- Multidimensional Scaling
- Multilevel Analysis
- Multilevel Linear Modeling
- Multilevel Modeling
- Multinomial Logistic Regression Models
- Multiple Discriminant Analysis
- Multiple Linear Regression
- Multiple Regression
- Multiple Regression Analysis
- Multivariate
- Multivariate Analysis
- Multivariate Data Analysis
- Multivariate Statistics
- Multiway Frequency Analysis
- Naive Bayes
- Negative Binomial Regression Models
- Numbers
- Ordinal Logistic Regression Models
- Ordinal Response Variables
- Packt
- Poisson Regression Models
- Practice
- Preparing
- Preparing
- Principles
- Probability Theory
- Probit Regression
- Profile Analysis
- Proportional Odd Models
- Python
- Quantitative Applications in the Social Sciences
- Quantitative Research
- R Computer Language
- R Computer Program Language
- R Computer Programme
- R Computer Programme Language
- R Language
- R Language and Statistics
- R Statistical Methods
- Regression
- Regression Analysis
- Reinforcement Learning
- Research Designs
- Sage University Paper
- SEM
- Simple Linear Regression
- Simple Regression
- Social Research
- Social Research Methodology
- Social Research Methods
- Social Science Research
- Social Science Research
- Social Science Statistics
- Social Sciences Research
- Social Sciences Statistical Methods
- Social Statistical Methods
- Statistical Methods
- Statistical Techniques
- Statistice
- Statistics
- Statistics Visualization
- Structural Equation Modeling
- Structural Equation Modeling
- Structural Equation Modelling
- Structural Technique
- Structure Equation Modeling
- Support Vector Machines
- Survival Analysis
- Techniques
- Testing Hypotheses
- Time Series Analysis
- Transfoming Data
- Tree Based Machine Learning
- Tree Based Machine Learning Models
- Univariate Discription Data
- Univariate Statistics
- Variance
- Visualization
- Zero Inflated Models
- Show more
- Show less