Extracting Seasonality and Trend from Data: Decomposition Using R

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Uma excelente descrição da decomposição clássica com Python e R.

Time series decomposition works by splitting a time series into three components: seasonality, trends and random fluctiation. To show how this works, we will study the decompose( ) and STL( ) functions in the R language.

Understanding Decomposition

Decompose One Time Series into Multiple Series

Time series decomposition is a mathematical procedure which transforms a time series into multiple different time series. The original time series is often split into 3 component series:

  • Seasonal: Patterns that repeat with a fixed period of time. For example, a website might receive more visits during weekends; this would produce data with a seasonality of 7 days.
  • Trend: The underlying trend of the metrics. A website increasing in popularity should show a general trend that goes up.
  • Random: Also call “noise”, “irregular” or “remainder,” this is the residuals of the original time series after the seasonal and trend series are removed.

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The 5 Computer Vision Techniques

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Boa introdução ao tema da visão por computador

The 5 Computer Vision Techniques That Will Change How You See The World

Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science). As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery.

So what is Computer Vision?

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Free Hadoop Tutorial: Master BigData

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BigData is the latest buzzword in the IT Industry. Apache’s Hadoop is a leading Big Data platform used by IT giants Yahoo, Facebook & Google. This course is geared to make a Hadoop Expert.

What should I know?

This is an absolute beginner guide to Hadoop. But knowledge of 1) Java 2) Linux will help


Tutorial Introduction to BIG DATA: Types, Characteristics & Benefits
Tutorial Hadoop Tutorial: Features, Components, Cluster & Topology
Tutorial Hadoop Setup Tutorial – Installation & Configuration
Tutorial HDFS Tutorial: Read & Write Commands using Java API
Tutorial What is MapReduce? How it Works – Hadoop MapReduce Tutorial
Tutorial Hadoop & Mapreduce Examples: Create your First Program
Tutorial Hadoop MapReduce Tutorial: Counters & Joins with Example
Tutorial What is Sqoop? What is FLUME – Hadoop Tutorial
Tutorial Sqoop vs Flume vs HDFS in Hadoop
Tutorial Create Your First FLUME Program – Beginner’s Tutorial
Tutorial Hadoop PIG Tutorial: Introduction, Installation & Example
Tutorial Learn OOZIE in 5 Minutes – Hadoop Tutorial
Tutorial Big Data Testing: Functional & Performance
Tutorial Hadoop & MapReduce Interview Questions & Answers


Controlling for test variables

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alguns apontamentos sobre testes com vars de controlo

3.2  Three (or more) variables

[Page last updated 3 August 2016]

Introducing a third variable. Controlling for test variables. Elaboration.

Logical model is X Y . T (the effect of X on Y controlling for T) where:

Y = Dependent variable
X = Independent variable
T = Test variable(s)

3.2.1 Elaboration

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How to create a slicer in Excel

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Bom tutorial de como usar umas das novas funcionalidades do Excel

For dashboards and quick filtering, you can’t beat Excel slicers. They’re easy to implement and even easier to use. Here are the basics–plus a few power tips.


Best Data Science Learning podcasts


Muito bons podcasts tem temas introdutórios

We present the top 12 Data Science & Machine Learning related Podcasts by popularity on iTunes. Check out latest episodes to stay up-to-date & become a part of the data conversations!

By Bhavya Geethika Peddibhotla.

Learn Data science the new way by listening to these compelling story tellers, interviewers, educators and experts in the field. Data suggests that podcasting about Data Science is only growing!

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Straightforward Statistics Videos

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Montes de vídeos sobre todos os temas abordados em P&E

Video and Multimedia

Click on the following links. Please note these will open in a new window.

Descriptive Versus Inferential Statistics
Illustrates the differential purposes served by descriptive and inferential techniques in conducting statistical analyses.

Practical examples of descriptive and inferential statistics

Simple Random Sampling, Convenience Sampling, Systematic Sampling, Cluster Sampling, Stratified Sampling

Types of Variables
Describes the concepts of; a) unit of observation and b) variables and consequently the differences amongst the three major levels of measurement of variables, nominal, ordinal and interval/ratio.

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Um webBook sobre estatística com exercícios e exemplos em SPSS


3rd Web Edition

David W. Stockburger

Missouri State University

@Copyright 2013 by David W. Stockburger

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A New View of Statistics

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Um webBook com montes de temas bem explicados

Mar 2013. Coming very soon: a slideshow and Excel workbook for an introductory course of 10 lectures on statistics. Aug 2011. Check out the following 2010 articles at Sportscience: assigning subjects to treatments in a controlled trial; regression vs limits of agreement in measure-comparison studies; magnitudes of effects derived from linear models. See the frame at right for links to much more, including the progressive statistics and research design articles. Previous updates…
New original approaches to statistics for researchers: the examples are taken from exercise and sport science, but the principles apply to all empirical sciences. Read more in the preface.
Feedback wanted: if you can’t understand something here, it’s my fault. Email me.
Become a license holder…eventually! Not yet. More…
Full Contents
Short Contents:
Preface: About These Pages
Summarizing Data
Simple Statistics & Effect Statistics
Dimension Reduction
Precision of Measurement
Generalizing to a Population
Confidence Limits & Statistical Significance
Statistical Models
Estimating Sample Size
Summary: The Most Important Points
Reference: Hopkins, W. G. (2000). A new view of statistics. Internet Society for Sport Science: http://www.sportsci.org/resource/stats/.

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HyperStat Online Statistics Textbook

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Mais um eBook com montes de links para outros recursos

RVLS Home | Glossary | Free Statistical Analysis Tools | Instructional Demos | Exercises and Problems | Statistics Help | Privacy policy


  1. Introduction to Statistics
  2. Describing Univariate Data
  3. Describing Bivariate Data
  4. Introduction to Probability (elementary)
  5. Normal Distribution
  6. Sampling Distributions
  7. Point Estimation
  8. Confidence Intervals
  9. The Logic of Hypothesis Testing
  10. Testing Hypotheses with Standard Errors
  11. Power
  12. Introduction to Between-Subjects ANOVA
  13. Factorial Between-Subjects ANOVA
  14. Within-Subjects/Repeated Measures ANOVA
  15. Prediction
  16. Chi Square
  17. Distribution-Free Tests
  18. Measuring Effect Size

© 1993-2013 David M. Lane

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