If you need a program that guarantees you a very high degree of specialization in the field of Applied Statistics and its techniques, look no further: this program is perfect for you”

If there is one thing that Statistics has shown, it is the flexibility and possibilities of applying its techniques and strategies to all fields and areas. Medicine, architecture, biology, politics, economics, marketing, etc. Any field makes use of the processes of probability and estimation to determine future trends and patterns of action, which increases the chances of achieving the expected results based on previous analysis of the behaviors displayed by agents or entities involved in a particular issue, such as customers, pathogens, resistance of materials, inclination of the vote, etc.

Thanks to the progress made in the field of Mathematics and Computer Science, it is now possible to use countless strategies that facilitate the automatic collection and massive management of data, optimizing processes and guaranteeing a series of more concrete and reliable results. For professionals in this field to know these intricacies in detail, TECH has developed the Professional master’s degree in Statistical Techniques. This is a multidisciplinary and intensive program where students will be able to to immerse themselves in state-of-the-art features of chance and probability, data exploration and estimation. They will also comprehensively work on the main advanced linear and multivariate prediction methods to formulate problems with a high rate of computational success.

Therefore, the student will benefit from 1,500 hours of material distributed in different formats: mainly the syllabus, designed by experts in Statistics and Computer Science, use cases based on real situations and additional material such as detailed videos, research articles, complementary readings, dynamic summaries and much more. Everything will be available on the Virtual Campus, which can be accessed without schedules or limits from any device with an Internet connection. In this way, students will undertake training adapted to their needs that will undoubtedly increase their knowledge and statistical talent to the highest professional level.

A 100% online program where you will be able to work on the most innovative concepts related to chance and probability applied to statistical calculation”

This Professional master’s degree in Statistical 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 practical contents of the book provide 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 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

You will have a specific module specialized in databases, so you can implement the main strategies of design and management of information in your professional practice”

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 This will be done with the help of an innovative system of interactive videos made by renowned experts. 

You will have access to the Virtual Campus 24 hours a day, 7 days a week, without limits or schedules, and all you need is a device with an Internet connection"

You will work on the central position characteristics of one-dimensional descriptive statistics, focusing on optimized and accurate data exploration"

The Professional master’s degree in Statistical Techniques is composed of 1,500 hours of the best theoretical, practical and additional content, the latter presented in different formats: use cases, complementary readings, self-knowledge exercises, research articles, news, dynamic summaries and videos detailing each unit. Everything will be available on the Virtual Campus from the very beginning and can be downloaded to any device with an Internet connection. In this way, graduates will be able to access the material at their own convenience, even after the 12 months of education are over. 

The content on the program has been developed using the Relearning methodology, which will allow you to save hours of study without renouncing to high-level, extensive training”

Module 1. Chance and Probability

1.1. Probabilistic Models

1.1.1. Introduction
1.1.2. Random Phenomena
1.1.3. Probability Spaces
1.1.4. Properties of Probability
1.1.5. Combinatorial

1.2. Conditional Probability

1.2.1. Definition of Conditional Probability
1.2.2. Event Independence
1.2.3. Properties of Event Independence
1.2.4. The Total Probability Formula
1.2.5. Bayes' Formula

1.3. One-Dimensional Random Variables

1.3.1. Concept of One-Dimensional Random Variables
1.3.2. Operations with Random Variables
1.3.3. Distribution Function of a One-Dimensional Random Variable. Properties
1.3.4. Discrete, Continuous and Mixed Random Variables
1.3.5. Random Variables Transformation

1.4. Characteristics of One-Dimensional Random Variables

1.4.1. Mathematical Expectation. Properties of Expectation Operators
1.4.2. Moments with Respect to the Origin. Moments with Respect to the Mean
1.4.3. Relations between Moments
1.4.4. Measures of Position, Dispersion and Shape
1.4.5. Chebyshev's Theorem

1.5. Discrete Distributions

1.5.1. Degenerate Distribution
1.5.2. Uniform Distribution on n Points
1.5.3. Bernoulli’s Distribution
1.5.4. Binomial Distribution
1.5.5. Poisson distribution
1.5.6. Negative Binomial Distribution
1.5.7. Geometric Distribution
1.5.8. Hypergeometric Distribution

1.6. Normal Distribution

1.6.1. Introduction
1.6.2. Characteristics of Normal Distribution
1.6.3. Representation of Normal Distribution
1.6.4. Approximation of a Binomial by Normal Distribution

1.7. Other Continuous Distributions

1.7.1. Uniform Distribution
1.7.2. Gamma Distribution
1.7.3. Exponential Distributions
1.7.4. Beta Distribution

1.8. Two-Dimensional Random Variables

1.8.1. Introduction
1.8.2. Two-Dimensional Random Variables
1.8.3. Discrete Two-Dimensional Random Variables. Mass Function
1.8.4. Continuous Two-Dimensional Random Variables. Density Function

1.9. Two-Dimensional Random Variables Distributionsz

1.9.1. Joint Distribution Function. Properties
1.9.2. Marginal Distributions
1.9.3. Conditional Distributions
1.9.4. Independent Random Variables

1.10. Laws of Large Numbers and Central Limit Theorem

1.10.1. Sequence of Random Variable
1.10.2. Convergence of Sequences of Random Variables. Relations between the Different Types of Convergence

1.10.2.1. Pointwise Convergence
1.10.2.2. Almost Certain Convergence
1.10.2.3. Convergence in Probability
1.10.2.4. Convergence in Law or Distribution

1.10.3. Large Number Laws
1.10.4. Central Classical Limit Problem

Module 2. Data Description and Exploration

2.1. Introduction to Statistics

2.1.1. Basic Concepts of Statistics
2.1.2. The Purpose of Exploratory Data Analysis or Descriptive Statistics
2.1.3. Types of Variables and Measurement Scales
2.1.4. Rounding and Scientific Notation

2.2. Summary of Statistical Data

2.2.1. Frequency Distributions: Tables
2.2.2. Grouping in Intervals
2.2.3. Graphical Representations
2.2.4. Differential Diagram
2.2.5. Integral Diagram

2.3. One-Dimensional Descriptive Statistics

2.3.1. Central Position Characteristics: Mean, Median, Mode
2.3.2. Other Position Characteristics: Quartiles, Deciles and Percentiles
2.3.3. Dispersion Characteristics: Variance and Standard Deviation (Sample and Population), Range, Inter-Quartile Range
2.3.4. Relative Dispersion Characteristics
2.3.5. Typical Scores
2.3.6. Shape Characteristics: Symmetry and Kurtosis

2.4. Complements in the Study of a Variable

2.4.1. Exploratory Analysis: Box Plots and Other Graphs
2.4.2. Transforming Variables
2.4.3. Other Averages: Geometric, Harmonic, Quadratic
2.4.4. Chebyshev's Inequality

2.5. Two-Dimensional Descriptive Statistics

2.5.1. Two-Dimensional Frequency Distributions
2.5.2. Double-Entry Statistical Tables. Marginal and Conditional Distributions
2.5.3. Concepts of Independence and Functional Dependence
2.5.4. Graphical Representations

2.6. Complements in the Study of Two Variables

2.6.1. Numerical Characteristics of a Two-Dimensional Distribution
2.6.2. Joint, Marginal and Conditional Moments
2.6.3. Relationship between Marginal and Conditional Measures

2.7. Regression

2.7.1. General Regression Line
2.7.2. Regression Curves
2.7.3. Linear Adjustment
2.7.4. Prediction and Error

2.8. Correlation

2.8.1. Concept of Correlation
2.8.2. Correlation Ratios
2.8.3. Pearson's Correlation Coefficient
2.8.4. Correlation Analysis

2.9. Correlation between Attributes

2.9.1. Spearman's Coefficient
2.9.2. Kendall Coefficient
2.9.3. Chi-Squared Coefficient

2.10. Introduction to Time Series

2.10.1. Time Series
2.10.2. Stochastic Processes

2.10.2.1. Stationary Processes
2.10.2.2. Non-Stationary Processes

2.10.3. Models
2.10.4. Applications

Module 3. Databases: Design and Management

3.1. Introduction to Databases

3.1.1. What is a Database?
3.1.2. History of Database Systems

3.2. Information Systems and Databases

3.2.1. Concepts
3.2.2. Features
3.2.3. Evolution of Databases

3.3. Definition and Characteristics of a Database Management System

3.3.1. Definition
3.3.2. Features

3.4. Architecture of Database Management Systems

3.4.1. Centralized and Client-Server Architectures
3.4.2. Server Systems Architectures
3.4.3. Parallel Systems
3.4.4. Distributed Systems
3.4.5. Types of Networks

3.5. Main Database Management Systems

3.5.1. Types of DBMS

3.6. Development of Database Applications

3.6.1. Web Interfaces for Databases
3.6.2. Performance Tuning
3.6.3. Performance Testing
3.6.4. Standardization
3.6.5. E-Commerce
3.6.6. Inherited Systems

3.7. Database Design Stages

3.7.1. Conceptual Design
3.7.2. Logical Design
3.7.3. Application Design

3.8. Database Implementation

3.8.1. Structured Query Language (SQL)
3.8.2. Data Processing
3.8.3. Data Query
3.8.4. SQL Database Management
3.8.5. Working with SQLite Databases

3.9. Notions of HTML and Regular Expressions

3.9.1. Structure and Code of a Web Page
3.9.2. HTML and CSS Tags and Attributes
3.9.3. Text Searching with Regular Expressions
3.9.4. Special Characters, Sets, Groups and Repetitions

3.10. Collecting and Storing Data from Web Pages

3.10.1. Introduction to Web Scraping Tools
3.10.2. Programming Web Scraping Tools in Python
3.10.3. Searching and Obtaining Information with Regular Expressions
3.10.4. Searching and Obtaining Information with Beautiful Soup
3.10.5. Storing in Databases
3.10.6. Exporting Results in Comma-Separated Value Files

Module 4. Estimations I

4.1. Introduction to Inference Statistics

4.1.1. What Is Inference Statistics?
4.1.2. Examples

4.2. General concepts

4.2.1. City
4.2.2. Sample
4.2.3. Sampling
4.2.4. Parameter

4.3. Statistical Inference Classification

4.3.1. Parametric
4.3.2. Non-Parametric
4.3.3. Classical Approach
4.3.4. Bayesian Approach

4.4. Statistical Inference Objective

4.4.1. What Objectives?
4.4.2. Statistical Inference Applications

4.5. Distributions Associated with Normal Distribution

4.5.1. Chi-Squared
4.5.2. T-Student
4.5.3. F- Snedecor

4.6. Introduction to Point Estimation

4.6.1. Definition of Simple Random Sample
4.6.2. Sample Space
4.6.3. Statistics and Estimators
4.6.4. Examples

4.7. Properties of Estimators

4.7.1. Sufficiency and Completeness
4.7.2. Factorization Theorem
4.7.3. Unbiased and Asymptotically Unbiased Estimators
4.7.4. Mean Square Error
4.7.5. Efficiency
4.7.6. Consistent Estimators
4.7.7. Estimating Mean, Variance, and Proportion of a Population

4.8. Procedures to Build estimators

4.8.1. Method of Moments
4.8.2. Maximum Likelihood Method
4.8.3. Properties of Maximum Likelihood Estimators

4.9. Introduction to Interval Estimation

4.9.1. Introduction to the Definition of Confidence Interval
4.9.2. Pivotal Quantity Method

4.10. Types of Confidence Intervals and their Properties

4.10.1. Confidence Intervals for the Mean of a Population
4.10.2. Confidence Interval for the Variance of a Population
4.10.3. Confidence Intervals for Proportions
4.10.4. Confidence Intervals for the Difference of Population Means. Independent Normal Populations. Paired Samples
4.10.5. Confidence Interval for the Variance Ratio of Two Independent Normal Populations
4.10.6. Confidence Interval for the Difference of Proportions of Two Independent Populations
4.10.7. Confidence Interval for a Parameter based on its Maximum Likelihood Estimator
4.10.8. Use of a Confidence Interval to Reject Hypotheses or Not

Module 5. Estimations II

5.1. Introduction to Hypothesis Contrasting

5.1.1. Problem Statement
5.1.2. Null and Alternative Hypothesis
5.1.3. Contrast Statistics
5.1.4. Types of Error
5.1.5. Level of Significance
5.1.6. Critical Region. p-value
5.1.7. Power

5.2. Types of Hypothesis Contrasting

5.2.1. Likelihood Ratio Test
5.2.2. Contrasts on Means and Variances in Normal Populations
5.2.3. Contrasts on Proportions
5.2.4. Relationship between Confidence Intervals and Hypothesis Contrasting

5.3. Introduction to Bayesian Inference

5.3.1. A Priori Distributions
5.3.2. Conjugate Distributions
5.3.3. Reference Distributions

5.4. Bayesian Estimation

5.4.1. Point Estimation
5.4.2. Estimation of an Proportion
5.4.3. Mean Estimate in Normal Populations
5.4.4. Comparison to Classical Methods

5.5. Introduction to Non-Parametric Inference Statistics

5.5.1. Non-Parametric Statistical Methods: Concepts
5.5.2. Use of Non-Parametric Statistics

5.6. Non-Parametric Inference Compared to Parametric Inference

5.6.1. Differences between Inferences

5.7. Goodness-of-Fit Test

5.7.1. Introduction
5.7.2. Graphic Methods
5.7.3. Contrast of the Goodness-of-Fit Equation
5.7.4. Kolmogorov-Smirnov Test
5.7.5. Normality Contrasts

5.8. Independence Contrasts

5.8.1. Introduction
5.8.2. Randomness Contrasts. Contrast of Spurts
5.8.3. Independence Contrasts in Paired Samples

5.8.3.1. Kendall's Contrast
5.8.3.2. Spearman's Ranks Contrast
5.8.3.3. Independence Chi-Square Test
5.8.3.4. Generalization of the Chi-Square Contrast

5.8.4. Independence Contrasts in K Related Samples

5.8.4.1. Generalization of the Chi-Square Contrast
5.8.4.2. Kendall's Coefficient of Concordance

5.9. Position Contrast

5.9.1. Introduction
5.9.2. Position Contrasts for a Single Sample and Paired Samples

5.9.2.1. Sign Test for a Single Sample. Median Test
5.9.2.2. Sign Test for Paired Samples
5.9.2.3. Wilcoxon Signed-Rank Test for a Single Sample
5.9.2.4. Wilcoxon Signed-Rank Test for Paired Samples

5.9.3. Non-Parametric Contrasts for Two Independent Samples

5.9.3.1. Wilcoxon-Mann-Whitney’s Test
5.9.3.2. Median Test
5.9.3.3. Chi-Square Contrast

5.9.4. Position Contrasts for K Independent Samples

5.9.4.1. Kruskal-Wallis Test

5.9.5. Independence Contrasts in K Related Samples

5.9.5.1. Friedman’s Test
5.9.5.2. Cochran Q Test
5.9.5.3. Kendall W Test

5.10. Homogeneity Contrast

5.10.1. Homogeneity Contrasts for Two Independent Samples

5.10.1.1. Wald-Wolfowitz Contrast
5.10.1.2. Kolmogorov-Smirnov Test
5.10.1.3. Chi-Square Contrast

Module 6. Mathematics with Computers

6.1. Introduction to MATLAB

6.1.1. What Is MATLAB?
6.1.2. Main Functions and Commands in MATLAB
6.1.3. Statistical Applications in MATLAB

6.2. Linear Algebra in MATLAB

6.2.1. Concepts of Linear Algebra
6.2.2. Main Functions and Commands
6.2.3. Examples

6.3. Examples

6.3.1. Concepts of Numerical and Functional Series
6.3.2. Main Functions and Commands
6.3.3. Examples

6.4. Functions of One and Several Variables in MATLAB

6.4.1. Concepts of Functions of One and Several Variables
6.4.2. Main Functions and Commands
6.4.3. Examples

6.5. Introduction to LaTex

6.5.1. What Is LaTex?
6.5.2. Main Functions and Commands in LaTex
6.5.3. Statistical Applications in LaTex

6.6. Introduction to R

6.6.1. What is R?
6.6.2. Main Functions and Commands in R
6.6.3. Statistical Applications in R

6.7. Introduction to Sage

6.7.1. What Is Sage?
6.7.2. Main Functions and Commands in Sage
6.7.3. Statistical Applications in Sage

6.8. Introduction to the Bash Operating System

6.8.1. What Is Bash?
6.8.2. Main Functions and Commands in Bash
6.8.3. Statistical Applications in Bash

6.9. Introduction to Python

6.9.1. What Is Python?
6.9.2. Main Functions and Commands in Python
6.9.3. Statistical Applications in Python

6.10. Introduction to SAS

6.10.1. What is SAS?
6.10.2. Main Functions and Commands in SAS
6.10.3. Statistical Applications in SAS

Module 7. Linear Prediction Methods

7.1. Simple Linear Regression Models

7.1.1. Introduction to Regression Models and Preliminary Steps in Simple Regression: Data Exploration
7.1.2. Models
7.1.3. Hypotheses
7.1.4. Parameters

7.2. Simple Linear Regression Estimation and Contrasts

7.2.1. Point Estimation of Model Parameters

7.2.1.1. Least Squares Method
7.2.1.2. Maximum Likelihood Estimators

7.2.2. Inference on Model Parameters under the Gauss-Markov Hypothesis

7.2.2.1. Intervals
7.2.2.2. Test

7.2.3. Confidence Interval for the Mean Response and Prediction Interval for New Observations
7.2.4. Simultaneous Inferences in Simple Regression
7.2.5. Confidence and Prediction Bands

7.3. Simple Linear Regression Models Diagnosis and Validation

7.3.1. Analysis of Variance (ANOVA) of Simple Regression Models
7.3.2. Model Diagnostics

7.3.2.1. Graphical Assessment of Linearity and Verification of the Hypotheses by Residuals Analysis
7.3.2.2. Linear Lack-of-Fit Test

7.4. Multiple Linear Regression Models

7.4.1. Data Exploration with Multidimensional Visualization Tools
7.4.2. Matrix Expression of Models and Coefficient Estimators
7.4.3. Interpreting Coefficients of Multiple Models

7.5. Multiple Linear Regression Estimation and Contrasts

7.5.1. Laws of Estimation for Coefficients, Predictions, and Residuals
7.5.2. Applying Properties of Idempotent Matrices
7.5.3. Inference in Multiple Linear Models
7.5.4. Anova Models

7.6. Multiple Linear Regression Models Diagnosis and Validation

7.6.1. "Ligatures" Test to Solve Linear Constraints on Coefficients

7.6.1.1. The Principle of Incremental Variability

7.6.2. Waste Analysis
7.6.3. Box-Cox Transformation

7.7. The Problem of Multicollinearity

7.7.1. Detection
7.7.2. Solutions

7.8. Polynomial Regression

7.8.1. Definition and Example
7.8.2. Matrix Form and Calculating Estimates
7.8.3. Interpretation
7.8.4. Alternative Approaches

7.9. Regression with Qualitative Variables

7.9.1. Dummy Variables in Regression
7.9.2. Interpreting Coefficients
7.9.3. Applications

7.10. Criteria for Models Selection

7.10.1. Mallows Cp Statistics
7.10.2. Model Cross Validation
7.10.3. Automatic Stepwise Selection

Module 8. Multivariate Statistical Techniques I

8.1. Factor Analysis

8.1.1. Introduction
8.1.2. Fundamentals of Factor Analysis
8.1.3. Factor Analysis
8.1.4. Factor Rotation Methods and Factor Analysis Interpretation

8.2. Factor Analysis Modeling

8.2.1. Examples
8.2.2. Statistical Software Modeling

8.3. Main Component Analysis

8.3.1. Introduction
8.3.2. Main Component Analysis
8.3.3. Systematic Principal Component Analysis

8.4. Principal Component Analysis Modeling

8.4.1. Examples
8.4.2. Statistical Software Modeling

8.5. Correspondence Analysis

8.5.1. Introduction
8.5.2. Independence Test
8.5.3. Row and Column Profiles
8.5.4. Inertia Analysis of a Point Cloud
8.5.5. Multiple Correspondence Analysis

8.6. Correspondence Analysis Modeling

8.6.1. Examples
8.6.2. Statistical Software Modeling

8.7. Discriminant Analysis

8.7.1. Introduction
8.7.2. Decision Rules for Two Groups
8.7.3. Classification over Several Populations
8.7.4. Fisher's Canonical Discriminant Analysis
8.7.5. Selecting Variables: Forward and Backward Procedure
8.7.6. Systematic Discriminant Analysis

8.8. Discriminant Analysis Modeling

8.8.1. Examples
8.8.2. Statistical Software Modeling

8.9. Cluster Analysis

8.9.1. Introduction
8.9.2. Distance and Similarity Measures
8.9.3. Hierarchical Classification Algorithms
8.9.4. Non-Hierarchical Classification Algorithms
8.9.5. Procedures to Determine the Appropriate Number of Clusters
8.9.6. Characterization of Clusters
8.9.7. Systematic Cluster Analysis

8.10. Cluster Analysis Modeling

8.10.1. Examples
8.10.2. Statistical Software Modeling

Module 9. Multivariate Statistical Techniques II

9.1. Introduction
9.2. Nominal Scale

9.2.1. Measures of Association for 2x2 Tables

9.2.1.1. Phi Coefficient
9.2.1.2. Relative Risk
9.2.1.3. Cross-Product Ratio (Odds Ratio)

9.2.2. Measures of Association for IxJ Tables

9.2.2.1. Contingency Ratio
9.2.2.2. Cramer's V
9.2.2.3. Lambdas
9.2.2.4. Tau of Goodman and Kruskal
9.2.2.5. Uncertainty Coefficient

9.2.3. Kappa Coefficient

9.3. Ordinal Scale

9.3.1. Gamma Coefficients
9.3.2. Kendall's Tau-B and Tau-C
9.3.3. Sommers' D

9.4. Interval or Ratio Scale

9.4.1. Eta Coefficient
9.4.2. Pearson's and Spearman's Correlation Coefficients

9.5. Stratified Analysis in 2x2 Tables

9.5.1. Stratified Analysis
9.5.2. Stratified Analysis in 2x2 Tables

9.6. Problem Formulation in Log-linear Models

9.6.1. The Saturated Model for Two Variables
9.6.2. The General Saturated Model
9.6.3. Other Types of Models

9.7. The Saturated Model

9.7.1. Calculation of Effects
9.7.2. Goodness of Fit
9.7.3. Test of K effects
9.7.4. Partial Association Test

9.8. The Hierarchical Model

9.8.1. Backward Methods

9.9. Probit Response Models

9.9.1. Problem Formulation
9.9.2. Parameter Estimation
9.9.3. Chi-Square Goodness-of-Fit Test
9.9.4. Parallelism Test for Groups
9.9.5. Estimation of the Dose Required to Obtain a Given Response Ratio

9.10. Binary Logistic Regression

9.10.1. Problem Formulation
9.10.2. Qualitative Variables in Logistic Regression
9.10.3. Selection of Variables
9.10.4. Parameter Estimation
9.10.5. Goodness of Fit
9.10.6. Classification of Individuals
9.10.7. Prediction

Module 10. Advanced Prediction Techniques

10.1. General Linear Regression Model

10.1.1. Definition
10.1.2. Properties
10.1.3. Examples

10.2. Partial Least Squares Regression

10.2.1. Definition
10.2.2. Properties
10.2.3. Examples

10.3. Principal Component Regression

10.3.1. Definition
10.3.2. Properties
10.3.3. Examples

10.4. RRR Regression

10.4.1. Definition
10.4.2. Properties
10.4.3. Examples

10.5. Ridge Regression

10.5.1. Definition
10.5.2. Properties
10.5.3. Examples

10.6. Lasso Regression

10.6.1. Definition
10.6.2. Properties
10.6.3. Examples

10.7. Elasticnet Regression

10.7.1. Definition
10.7.2. Properties
10.7.3. Examples

10.8. Non-Linear Prediction Models

10.8.1. Non-Linear Regression Models
10.8.2. Non-Linear Least Squares
10.8.3. Conversion to a Linear Model

10.9. Parameter Estimation in a Non-Linear System

10.9.1. Linearization
10.9.2. Other Parameter Estimation Methods
10.9.3. Initial Values
10.9.4. Computer Programs

10.10. Statistical Inference in Non-Linear Regression

10.10.1. Statistical Inference in Non-Linear La Regression
10.10.2. Approximate Inference Validation
10.10.3.    Examples

A program designed to enhance your professional quality and elevate your talent in managing the most complex Statistical Techniques to the top of the current industrial sector”