Would you like to become a true expert in Estimation? Then this TECH program is perfect for you. What are you waiting for to enroll?"

Voting tendency polls, market analysis or medical epidemiology are three of the many sectors in which Statistical Inference plays a fundamental role in the deduction of conclusions and trends through the analysis of a sample of the whole. Thanks to the projection and comparison of data, it has been possible to determine the favorite candidate in an election, which product people prefer and in which context, or the public measures to be taken or avoided to prevent or control the development of a viral or infectious disease.

It is, therefore, a branch of the Social Sciences of vital importance for the advancement of society based on its needs and demands and in which its professionals must have a very high level of knowledge to work effectively in it. For this reason, and in order to provide those interested in this field with all the information that will allow them to keep up to date with its advances, TECH and its team of experts have developed a very complete program that is perfect for this purpose. It is a program distributed in 450 hours of theoretical, practical and additional material thanks to which the graduate will be able to delve into the latest aspects of estimation (hypothesis testing, Bayesian inference, factor analysis, etc.) and multivariate statistical techniques: principal component modeling, correspondence analysis, cluster analysis, etc.

All this 100% online and during 6 months of multidisciplinary training in which, in addition to a complete and dynamic syllabus, you will have access to additional high quality material: detailed videos, research articles, complementary readings and much more! Moreover, thanks to the use of theRelearning methodology in the development of the program, you will not have to invest extra hours in memorizing, but you will attend a natural and progressive updating of your knowledge.

The best program to specialize in Statistical Inference through a multidisciplinary and 100% online program”

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

• The development of case studies presented by experts in Applied Statistics
• The graphic, schematic and practical contents of the book provide technical and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to 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

A program that immerses you in hypothetical testing through a thorough knowledge of its techniques and strategies, such as Bayesian or goodness of fit estimation”

The program’s teaching staff includes professionals from the industry who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities.

The 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 immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

Each module includes an exclusive section in which you will find examples that will make it easier for you to visualize the concepts developed in the syllabus"

You will have 450 hours of the best theoretical-practical and additional content to delve into aspects such as the distributions associated with the norm or the properties of estimators"

For the development of the structure and content of this Postgraduate Diploma, TECH has taken into consideration the professional criteria of a team of specialists in the field of Applied Statistics. Thanks to this, it has been possible to shape a solid, complete, current and highly capacitating syllabus, which includes the latest developments in estimation and multivariate techniques. In addition, it is a program in which, although the theoretical content has an important weight, the additional and practical material represents a good part of the 450 hours in which it is distributed, providing dynamism and making it a unique and enjoyable academic experience.

Thanks to the thoroughness with which the syllabus of this program has been designed, you will acquire the most exhaustive knowledge on statistical modeling through cluster analysis" 

Module 1. Estimations I

1.1. Introduction to Inference Statistics

1.1.1. What Is Inference Statistics?
1.1.2. Examples

1.2. General Concepts

1.2.1. City
1.2.2. Sample
1.2.3. Sampling
1.2.4. Parameter

1.3. Statistical Inference Classification

1.3.1. Parametric
1.3.2. Non-Parametric
1.3.3. Classical Approach
1.3.4. Bayesian Approach

1.4. Statistical Inference Objective

1.4.1. What Objectives?
1.4.2. Statistical Inference Applications

1.5. Distributions Associated with Normal Distribution

1.5.1. Chi-Squared
1.5.2. T-Student
1.5.3. F- Snedecor

1.6. Introduction to Point Estimation

1.6.1. Definition of Simple Random Sample
1.6.2. Sample Space
1.6.3. Statistics and Estimators
1.6.4. Examples

1.7. Properties of Estimators

1.7.1. Sufficiency and Completeness
1.7.2. Factorization Theorem
1.7.3. Unbiased and Asymptotically Unbiased Estimators
1.7.4. Mean Square Error
1.7.5. Efficiency
1.7.6. Consistent Estimators
1.7.7. Estimating Mean, Variance, and Proportion of a Population

1.8. Procedures to Build estimators

1.8.1. Method of Moments
1.8.2. Maximum Likelihood Method
1.8.3. Properties of Maximum Likelihood Estimators

1.9. Introduction to Interval Estimation

1.9.1. Introduction to the Definition of Confidence Interval
1.9.2. Pivotal Quantity Method

1.10. Types of Confidence Intervals and their Properties

1.10.1. Confidence Intervals for the Mean of a Population
1.10.2. Confidence Interval for the Variance of a Population
1.10.3. Confidence Intervals for Proportions
1.10.4. Confidence Intervals for the Difference of Population Means. Independent Normal Populations. Paired Samples
1.10.5. Confidence Interval for the Variance Ratio of Two Independent Normal Populations
1.10.6. Confidence Interval for the Difference of Proportions of Two Independent Populations
1.10.7. Confidence Interval for a Parameter based on its Maximum Likelihood Estimator 
1.10.8. Use of a Confidence Interval to Reject Hypotheses or Not

Module 2. Estimations II

2.1. Introduction to Hypothesis Contrasting

2.1.1. Problem Statement
2.1.2. Null and Alternative Hypothesis
2.1.3. Contrast Statistics
2.1.4. Types of Error
2.1.5. Level of Significance
2.1.6. Critical Region. p-value
2.1.7. Power

2.2. Types of Hypothesis Contrasting

2.2.1. Likelihood Ratio Test
2.2.2. Contrasts on Means and Variances in Normal Populations
2.2.3. Contrasts on Proportions
2.2.4. Relationship between Confidence Intervals and Hypothesis Contrasting

2.3. Introduction to Bayesian Inference

2.3.1. A Priori Distributions
2.3.2. Conjugate Distributions
2.3.3. Reference Distributions

2.4. Bayesian Estimation

2.4.1. Point Estimation
2.4.2. Estimation of an Proportion
2.4.3. Mean Estimate in Normal Populations
2.4.4. Comparison to Classical Methods

2.5. Introduction to Non-Parametric Inference Statistics

2.5.1. Non-Parametric Statistical Methods: Concepts
2.5.2. Use of Non-Parametric Statistics

2.6. Non-Parametric Inference Compared to Parametric Inference

2.6.1. Differences between Inferences

2.7. Goodness-of-Fit Test

2.7.1. Introduction
2.7.2. Graphic Methods
2.7.3. Contrast of the Goodness-of-Fit Equation
2.7.4. Kolmogorov-Smirnov Test
2.7.5. Normality Contrasts

2.8. Independence Contrasts

2.8.1. Introduction
2.8.2. Randomness Contrasts. Contrast of Spurts
2.8.3. Independence Contrasts in Paired Samples

2.8.3.1. Kendall's Contrast
2.8.3.2. Spearman's Ranks Contrast
2.8.3.3. Independence Chi-Square Test
2.8.3.4. Generalization of the Chi-Square Contrast

2.8.4.  Independence Contrasts in K Related Samples

2.8.4.1. Generalization of the Chi-Square Contrast
2.8.4.2. Kendall's Coefficient of Concordance

2.9. Position Contrast

2.9.1. Introduction
2.9.2. Position Contrasts for a Single Sample and Paired Samples

2.9.2.1. Sign Test for a Single Sample. Median Test
2.9.2.2. Sign Test for Paired Samples
2.9.2.3. Wilcoxon Signed-Rank Test for a Single Sample
2.9.2.4. Wilcoxon Signed-Rank Test for Paired Samples

2.9.3. Non-Parametric Contrasts for Two Independent Samples

2.9.3.1. Wilcoxon-Mann-Whitney’s Test
2.9.3.2. Median Test
2.9.3.3. Chi-Square Contrast

2.9.4. Position Contrasts for K Independent Samples

2.9.4.1. Kruskal-Wallis Test

2.9.5. Independence Contrasts in K Related Samples

2.9.5.1. Friedman’s Test
2.9.5.2. Cochran Q Test
2.9.5.3. Kendall W Test

2.10. Homogeneity Contrast

2.10.1. Homogeneity Contrasts for Two Independent Samples

2.10.1.1. Wald-Wolfowitz Contrast
2.10.1.2. Kolmogorov-Smirnov Test
2.10.1.3. Chi-Square Contrast

Module 3. Multivariate Statistical Techniques

3.1. Factor Analysis

3.1.1. Introduction
3.1.2. Fundamentals of Factor Analysis
3.1.3. Factor Analysis
3.1.4. Factor Rotation Methods and Factor Analysis Interpretation

3.2. Factor Analysis Modeling

3.2.1. Examples
3.2.2. Statistical Software Modeling

3.3. Main Component Analysis

3.3.1. Introduction
3.3.2. Main Component Analysis
3.3.3. Systematic Principal Component Analysis

3.4. Principal Component Analysis Modeling

3.4.1. Examples
3.4.2. Statistical Software Modeling

3.5. Correspondence Analysis

3.5.1. Introduction
3.5.2. Independence Test
3.5.3. Row and Column Profiles
3.5.4. Inertia Analysis of a Point Cloud
3.5.5. Multiple Correspondence Analysis

3.6. Correspondence Analysis Modeling

3.6.1. Examples
3.6.2. Statistical Software Modeling

3.7. Discriminant Analysis

3.7.1. Introduction
3.7.2. Decision Rules for Two Groups
3.7.3. Classification over Several Populations
3.7.4. Fisher's Canonical Discriminant Analysis
3.7.5. Choice of Variables: Forwradand  BackwardProcedure
3.7.6. Systematic Discriminant Analysis

3.8. Discriminant Analysis Modeling

3.8.1. Examples
3.8.2. Statistical Software Modeling

3.9. Cluster Analysis

3.9.1. Introduction
3.9.2. Distance and Similarity Measures
3.9.3. Hierarchical Classification Algorithms
3.9.4. Non-Hierarchical Classification Algorithms
3.9.5. Procedures to Determine the Appropriate Number of Clusters
3.9.6. Characterization of Clusters
3.9.7. Systematic Cluster Analysis

3.10. Cluster Analysis Modeling

3.10.1. Examples
3.10.2. Statistical Software Modeling

Take a step further on your way to success in the statistical profession and specialize in a practical, dynamic and highly demanded field in today's job market in only 6 months of training"