Take advantage of the unique opportunity for professional and personal growth offered exclusively by this TECH Postgraduate certificate”

Linear Prediction Methods have become an essential tool in decision making in different fields of engineering. This is because they allow to analyze data and make future projections that are key in project planning and design of efficient solutions. Linear Prediction Methods are applied in different areas of engineering, such as mechanical, electrical, chemical, civil and many others, so it is important to have a solid knowledge in this subject.

In this sense, TECH has developed a complete and dynamic university program in Linear Prediction Methods, through which the graduate will be able to delve into the multiple linear regression model, as well as its estimation and contrasts. In addition, by acquiring solid knowledge in this subject and knowing how to apply it in decision making, engineers will be able to improve efficiency and reduce costs by predicting potential problems and opportunities in the future.

This fully online program is developed over six weeks, with unlimited access to the Virtual Campus and is compatible with any device that has an Internet connection. It also includes hours of high-quality additional material presented in a variety of formats, such as detailed videos, research articles, supplementary readings, self-assessment exercises, dynamic summaries and more. All material can be downloaded for later reference, even in areas without Internet connection, and for after the highly educational and enriching academic experience is over.

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

This Postgraduate certificate in Linear Prediction Methods 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

With the Relearning methodology you will acquire the knowledge in a progressive way and with total flexibility. A program that fits you”

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.

Give a significant boost to your professional career by including this Postgraduate certificate in your Resume"

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

The syllabus that composes this academic qualification has been elaborated by experts in Applied Statistics. In this sense, they have included 150 hours of the most avant-garde theoretical-practical and additional contents, presented in different audiovisual supports. In addition, thanks to the revolutionary methodology exclusive to TECH, the Relearning, the graduate will delve into the simple linear regression model through a flexible and totally online format. Therefore, the graduate will acquire the latest tools from any device with Internet connection and with access to the virtual campus 24 hours a day.

After a complex production process, TECH has transformed the best contents into a multimedia format of high pedagogical and audiovisual quality”

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

You will access content presented in an attractive and dynamic way in multimedia pills that include audio, videos, images, diagrams and concept maps in order to reinforce knowledge"