You will be able to download all the content to any electronic device from the Virtual Campus and access it whenever you need it, even without an Internet connection” 

The field of engineering is one of the fields that benefits most from data analysis and statistics, and estimation is one of the key tools for informed decision making in project pre-design and analysis. Therefore, a solid education in this area becomes a necessity for any engineer who wishes to advance in their professional career and stand out in the job market. 

The Postgraduate certificate in Estimation for Predesign and Analysis offers all the specialized knowledge in the different techniques and specialized knowledge in the different techniques and methods used in parameter estimation, providing engineers with the necessary skills to analyze and make informed decisions at the design and analysis stage of projects.  Therefore, the program adapts to the current needs of the market, providing first level information to students on topics such as statistical inference, point and interval estimation, and procedures for the construction of estimators, among others. 

For this reason, TECH has designed a program is developed in 100% online format, which allows students to access all content from anywhere and at any time, adapting to the needs of professionals who wish to continue their instruction without giving up their work activity. In addition, it uses the Relearning methodology, which allows a natural and progressive integration of the fundamental concepts, through repetition and presentation in different audiovisual supports. 

Distribute the teaching load according to your personal needs and make the university qualification compatible with professional work” 

This  Postgraduate certificate in Estimation 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 it is conceived provide sporting 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 

Motivational videos, case studies, graphical and schematic content, discussion forums... Everything you need to take a leap forward in your career. Don't wait any longer" 

The program’s teaching staff includes professionals from sector who contribute their work experience to this educational 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 student will be assisted by an innovative interactive video system created by renowned and experienced experts. 

Delve into the procedures for the construction of estimators, delving into the maximum likelihood methods"

Combine your personal and work responsibilities with your studies thanks to this Postgraduate certificate. 100% flexible and online"

The syllabus that composes this program has been elaborated by experts in Estimation. Therefore, they have included 300 hours of the best theoretical, practical and additional content presented in different audiovisual formats. In addition, with TECH exclusive revolutionary methodology, Relearning, the graduate will delve into statistics in a natural and progressive way. All of this is presented in a flexible and fully online format, allowing the student to acquire the most advanced tools from any device with an Internet connection and access to the virtual campus 24 hours a day. 

The most complete and up-to-date syllabus on the market is at your disposal thanks to this Postgraduate Certificate through 100% online learning” 

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. Population 
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  

A syllabus in which the contents are presented in an attractive and dynamic way to turn you into a high-level engineer”