Would you like to master the most advanced multivariate statistical prediction techniques and don't have time to access face-to-face education? TECHputs the best 100% online program at your disposal to achieve it”

Thanks to the development of Multivariate Techniques, nowadays it is possible to define the level of relationship that exists between the variation of different weighted and/or combined factors with a very high degree of accuracy. Based on this, researchers can extract relevant information on the available data, allowing them to establish guidelines for action and more accurate and effective intervention strategies for the future of the project they are working on: social trends, economic regressions, political results, etc. It is a discipline that, due to its complexity, requires of a wide and exhaustive knowledge about its extensions and distributions, something the graduate will be able to work with throughout this program.

TECH presents the Postgraduate diploma in Multivariate Techniques as a unique opportunity so that the student can specialize in this area through an innovative, complete and exhaustive educational experience. The program includes 450 hours of theoretical, practical and additional content that will enable you to delve into the statistical techniques of factor analysis and principal component modeling, as well as in the discriminant study and in the hierarchical and non-hierarchical algorithms. Also, the program will delve into the advanced principles of prediction, focusing its study on the properties of their strategies, as well as recommendations for their use.

All of the above in a 100% online format and through a program that includes, in addition to the agenda, use cases to improve students skills in a practical way, as well as detailed videos, research articles, additional reading, news and much more additional material to delve into the different sections of the curriculum. All of this will be available on the Virtual Campus from the very start and can be downloaded to any device with an Internet connection, such as a PC, Tablet or mobile phone.

You will work conscientiously in the stratified analysis in 2x2 tables through the most innovative techniques and strategies”

This Postgraduate diploma in Multivariate Techniques contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Applied Statistics
• The graphic, schematic and eminently practical contents with which the program is designed to collect technical and practical information on those disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions for the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will have the most exhaustive and innovative educational material, made up of detailed videos, research articles, news, additional reading and much more!”

In its teaching staff, Tte program includes professionals from the sector who pour the experience of their work into this program, in addition to recognized specialists from reference societies and prestigious universities.

Its multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive education programmed to learn in real situations.

This program’s design focuses on Problem-Based Learning, through which the professional must try to solve different professional practice situations that are raised throughout the year. This will be done with the help of an innovative system of interactive videos made by renowned experts.

The perfect program to get up to date on problem formulation programs in loglinear models from wherever you want, and with a format adapted to your needs"

You will have numerous use cases with which you can put your skills in the classification of individuals and the formulation of problems into practice"

For the development of this Postgraduate diploma, TECH has taken into consideration the criteria of a team of professionals versed in Applied Statistics, more specifically in the area of ​​studies and research. Because of this, it has been possible to develop a dynamic and multidisciplinary study plan that is also complete and innovative, with which the graduate will be able to acquire unique knowledge about advanced forecasting techniques, as well as about different multivariate strategies. All this 100% online and through a degree fully adapted to the forefront of the university environment.

You will find detailed videos, research articles, complementary readings and much more in the Virtual CAMPUS! So you can personalize your study of the different sections of the syllabus”

Module 1. Multivariate Statistical Techniques I

1.1. Factor Analysis

1.1.1. Introduction
1.1.2. Fundamentals of Factor Analysis
1.1.3. Factor Analysis
1.1.4. Factor Rotation Methods and Factor Analysis Interpretation

1.2. Factor Analysis Modeling

1.2.1. Examples
1.2.2. Statistical Software Modeling

1.3. Main Component Analysis

1.3.1. Introduction
1.3.2. Main Component Analysis
1.3.3. Systematic Principal Component Analysis

1.4. Principal Component Analysis Modeling

1.4.1. Examples
1.4.2. Statistical Software Modeling

1.5. Correspondence Analysis

1.5.1. Introduction
1.5.2. Independence Test
1.5.3. Row and Column Profiles
1.5.4. Inertia Analysis of a Point Cloud
1.5.5. Multiple Correspondence Analysis

1.6. Correspondence Analysis Modeling

1.6.1. Examples
1.6.2. Statistical Software Modeling

1.7. Discriminant Analysis

1.7.1. Introduction
1.7.2. Decision Rules for Two Groups
1.7.3. Classification over Several Populations
1.7.4. Fisher's Canonical Discriminant Analysis
1.7.5. Selecting Variables: Forward and Backward Procedure
1.7.6. Systematic Discriminant Analysis

1.8. Discriminant Analysis Modeling

1.8.1. Examples
1.8.2. Statistical Software Modeling

1.9. Cluster Analysis

1.9.1. Introduction
1.9.2. Distance and Similarity Measures
1.9.3. Hierarchical Classification Algorithms
1.9.4. Non-Hierarchical Classification Algorithms
1.9.5. Procedures to Determine the Appropriate Number of Clusters
1.9.6. Characterization of Clusters
1.9.7. Systematic Cluster Analysis

1.10. Cluster Analysis Modeling

1.10.1. Examples
1.10.2. Statistical Software Modeling

Module 2. Multivariate Statistical Techniques II

2.1. Introduction
2.2. Nominal Scale

2.2.1. Measures of Association for 2x2 Tables

2.2.1.1. Phi Coefficient
2.2.1.2. Relative Risk
2.2.1.3. Cross-Product Ratio (Odds Ratio)

2.2.2. Measures of Association for IxJ Tables

2.2.2.1. Contingency Ratio
2.2.2.2. Cramer's V
2.2.2.3. Lambdas
2.2.2.4. Tau of Goodman and Kruskal
2.2.2.5. Uncertainty Coefficient

2.2.3. Kappa Coefficient

2.3. Ordinal Scale

2.3.1. Gamma Coefficients
2.3.2. Kendall's Tau-B and Tau-C
2.3.3. Sommers' D

2.4. Interval or Ratio Scale

2.4.1. Eta Coefficient
2.4.2. Pearson's and Spearman's Correlation Coefficients

2.5. Stratified Analysis in 2x2 Tables

2.5.1. Stratified Analysis
2.5.2. Stratified Analysis in 2x2 Tables

2.6. Problem Formulation in Log-linear Models

2.6.1. The Saturated Model for Two Variables
2.6.2. The General Saturated Model
2.6.3. Other Types of Models

2.7. The Saturated Model

2.7.1. Calculation of Effects
2.7.2. Goodness of Fit
2.7.3. Test of K effects
2.7.4. Partial Association Test

2.8. The Hierarchical Model

2.8.1. Backward Methods

2.9. Probit Response Models

2.9.1. Problem Formulation
2.9.2. Parameter Estimation
2.9.3. Chi-Square Goodness-of-Fit Test
2.9.4. Parallelism Test for Groups
2.9.5. Estimation of the Dose Required to Obtain a Given Response Ratio

2.10. Binary Logistic Regression

2.10.1. Problem Formulation
2.10.2. Qualitative Variables in Logistic Regression
2.10.3. Selection of Variables
2.10.4. Parameter Estimation
2.10.5. Goodness of Fit
2.10.6. Classification of Individuals
2.10.7. Prediction

Module 3. Advanced Prediction Techniques

3.1. General Linear Regression Model

3.1.1. Definition
3.1.2. Properties
3.1.3. Examples

3.2. Partial Least Squares Regression

3.2.1. Definition
3.2.2. Properties
3.2.3. Examples

3.3. Principal Component Regression

3.3.1. Definition
3.3.2. Properties
3.3.3. Examples

3.4. RRR Regression

3.4.1. Definition
3.4.2. Properties
3.4.3. Examples

3.5. Ridge Regression

3.5.1. Definition
3.5.2. Properties
3.5.3. Examples

3.6. Lasso Regression

3.6.1. Definition
3.6.2. Properties
3.6.3. Examples

3.7. Elasticnet Regression

3.7.1. Definition
3.7.2. Properties
3.7.3. Examples

3.8. Non-Linear Prediction Models

3.8.1. Non-Linear Regression Models
3.8.2. Non-Linear Least Squares
3.8.3. Conversion to a Linear Model

3.9. Parameter Estimation in a Non-Linear System

3.9.1. Linearization
3.9.2. Other Parameter Estimation Methods
3.9.3. Initial Values
3.9.4. Computer Programs

3.10. Statistical Inference in Non-Linear Regression

3.10.1. Statistical Inference in Non-Linear La Regression
3.10.2. Approximate Inference Validation
3.10.3. Examples

Do not think twice and choose an educational experience of the highest level and endorsed by one of the largest online universities in the world”