Postgraduate certificate Prediction

The ability to predict results and trends is increasingly important for companies in a competitive marketplace. In fact, more and more companies are using predictive analytics to improve their efficiency and competitiveness. Therefore, more and more highly qualified engineers are demonstrating their in-depth knowledge of linear regression models. For this reason, TECH has created an online program that offers state-of-the-art knowledge in simple linear regression estimation and contrasts, as well as confidence and prediction bands. And to facilitate the elite and contemporary education of graduates, the program is fully online. It also includes multimedia teaching resources and case studies provided by experts in the field.

University certificate

duration

12 weeks

Modality

Online

Schedule

At your own pace

Exams

Online

start date

Credits

12 ECTS

financing up to

3 months

Price(first year)

See price

The world's largest faculty of engineering”

Introduction to the Program

Thanks to this Postgraduate certificate you will be able to contribute to your professional career and increase the competitiveness of your company”

Prediction has become a fundamental tool for decision making in various fields, from industry to medicine. Information and communication technologies have allowed an exponential growth in the amount of data generated, which has led to the need for increasingly accurate and efficient forecasting methods. Therefore, it is expected that the global market for data analysis and prediction software will continue to grow, which will generate a greater demand for this discipline.

Given this reality, it is essential for professionals to have solid knowledge in prediction to be able to apply it in their field of work. This is where the university program that TECH has created responds to the current needs of engineers. In this way, it provides cutting-edge and complete education in prediction techniques, covering relevant topics such as the diagnosis and validation of the multiple linear regression model.

One of the great advantages of this program is that it is developed in a 100% online format, which allows students to access the contents from anywhere in the world, without geographical or time restrictions. In addition, the Relearning methodology is used, which is based on learning by solving real problems, making the learning process more dynamic and effective.

Enroll in a university qualification in the applications of the properties of idempotent matrices”

This Postgraduate certificate in Prediction 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

You will only need a device with an Internet connection to access the most comprehensive academic program in the current academic panorama”

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.

Choose your schedule, pace of study and location. TECH provides the resources and gives you access to them 24 hours a day"

A 100% online qualification that will provide you with the most comprehensive knowledge of the principle of incremental variability"

Syllabus

A team of experts in the field of Engineering, specifically in Applied Statistics, has been in charge of designing the syllabus for this program. As a result, TECH has created a complete and rigorous program that covers all the information needed to master this discipline within 12 weeks. In addition to the full syllabus, hours of additional varied materials have been included so that graduates can work in a personalized manner according to their level of demand. All of this is presented in a 100% online format that is convenient and flexible and is compatible with any device that has an Internet connection.

A syllabus covering Ridge, Lasso and Elasticnet regression with which you will delve into predictive models for effective statistical inference”

Module 1. Linear Prediction Methods

1.1. Simple Linear Regression Models

1.1.1. Introduction to Regression Models and Preliminary Steps in Simple Regression: Data Exploration
1.1.2. Models
1.1.3. Hypotheses
1.1.4. Parameters

1.2. Simple Linear Regression Estimation and Contrasts

1.2.1. Point Estimation of Model Parameters

1.2.1.1. Least Squares Method
1.2.1.2. Maximum Likelihood Estimators

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

1.2.2.1. Intervals
1.2.2.2. Test

1.2.3. Confidence Interval for the Mean Response and Prediction Interval for New Observations
1.2.4. Simultaneous Inferences in Simple Regression
1.2.5. Confidence and Prediction Bands

1.3. Simple Linear Regression Models Diagnosis and Validation

1.3.1. Analysis of Variance (ANOVA) of Simple Regression Models
1.3.2. Model Diagnostics

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

1.4. Multiple Linear Regression Models

1.4.1. Data Exploration with Multidimensional Visualization Tools
1.4.2. Matrix Expression of Models and Coefficient Estimators
1.4.3. Interpreting Coefficients of Multiple Models

1.5. Multiple Linear Regression Estimation and Contrasts

1.5.1. Laws of Estimation for Coefficients, Predictions, and Residuals
1.5.2. Applying Properties of Idempotent Matrices
1.5.3. Inference in Multiple Linear Models
1.5.4. Anova Models

1.6. Multiple Linear Regression Models Diagnosis and Validation

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

1.6.1.1. The Principle of Incremental Variability

1.6.2. Waste Analysis
1.6.3. Box-Cox Transformation

1.7. The Problem of Multicollinearity

1.7.1. Detection
1.7.2. Solutions

1.8. Polynomial Regression

1.8.1. Definition and Example
1.8.2. Matrix Form and Calculating Estimates
1.8.3. Interpretation
1.8.4. Alternative Approaches

1.9. Regression with Qualitative Variables

1.9.1. Dummy Variables in Regression
1.9.2. Interpreting Coefficients
1.9.3. Applications

1.10. Criteria for Models Selection

1.10.1. Mallows Cp Statistics
1.10.2. Model Cross Validation
1.10.3. Automatic Stepwise Selection

Module 2. Advanced Prediction Techniques

2.1. General Linear Regression Model

2.1.1. Definition
2.1.2. Properties
2.1.3. Examples

2.2. Partial Least Squares Regression

2.2.1. Definition
2.2.2. Properties
2.2.3. Examples

2.3. Principal Component Regression

2.3.1. Definition
2.3.2. Properties
2.3.3. Examples

2.4. RRR Regression

2.4.1. Definition
2.4.2. Properties
2.4.3. Examples

2.5. Ridge Regression

2.5.1. Definition
2.5.2. Properties
2.5.3. Examples

2.6. Lasso Regression

2.6.1. Definition
2.6.2. Properties
2.6.3. Examples

2.7. Elasticnet Regression

2.7.1. Definition
2.7.2. Properties
2.7.3. Examples

2.8. Non-Linear Prediction Models

2.8.1. Non-Linear Regression Models
2.8.2. Non-Linear Least Squares
2.8.3. Conversion to a Linear Model

2.9. Parameter Estimation in a Non-Linear System

2.9.1. Linearization
2.9.2. Other Parameter Estimation Methods
2.9.3. Initial Values
2.9.4. Computer Programs

2.10. Statistical Inference in Non-Linear Regression

2.10.1. Statistical Inference in Non-Linear La Regression
2.10.2. Approximate Inference Validation
2.10.3. Examples

Progress through the syllabus of this program in a much more agile way thanks to the Relearning method used by TECH”

Postgraduate Certificate in Prediction

Prediction is a technique used in engineering to estimate the future behavior of a system or process. It involves making assumptions based on available information and previous experience to project how a structure, solution or system will perform in the future under specific conditions. It is a valuable tool for risk analysis, decision making and continuous improvement of the efficiency and effectiveness of processes and structures in engineering. At TECH Global University we have this specialized program designed to provide knowledge in system, structure and process.

Prediction in engineering is based on data analysis and mathematical modeling of the problem to be solved. It uses simulation and modeling techniques to establish an often complex framework that can be used to forecast the evolution of a system or process. Different techniques are used in forecasting, such as computational simulation, mathematical and statistical modeling. Each of these tools has advantages and disadvantages, which depend on the nature and complexity of the problem being addressed. It is an excellent option for those who wish to acquire specialized skills and develop a successful career in this field.