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.

## Extracting Seasonality and Trend from Data: Decomposition Using R

Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, lições, linguagens de programação, materiais ensino, materiais para profissionais

Uma excelente descrição da decomposição clássica com Python e R.

## 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.

Tags: engenharia, inferência, otimização, previsão

## Controlling for test variables

Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino, software

## alguns apontamentos sobre testes com vars de controlo

## 3.2 Three (or more) variables

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

Tags: inferência, software estatístico

## INTRODUCTORY STATISTICS book

Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino

**Um webBook sobre estatística com exercícios e exemplos em SPSS**

**INTRODUCTORY STATISTICS:
CONCEPTS, MODELS, AND APPLICATIONS**

3rd Web Edition

David W. Stockburger

*Missouri State University*

@Copyright 2013 by David W. Stockburger

Tags: Estat Descritiva, IBM SPSS Statistics, inferência

## Research Methods Knowledge Base

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, materiais para profissionais

**Um webBook pensado para investigadores sobre temas de estatística**

## What is the Research Methods Knowledge Base?

The Research Methods Knowledge Base is a comprehensive web-based textbook that addresses all of the topics in a typical introductory undergraduate or graduate course in social research methods. It covers the entire research process including: formulating research questions; sampling (probability and nonprobability); measurement (surveys, scaling, qualitative, unobtrusive); research design (experimental and quasi-experimental); data analysis; and, writing the research paper. It also addresses the major theoretical and philosophical underpinnings of research including: the idea of validity in research; reliability of measures; and ethics. The Knowledge Base was designed to be different from the many typical commercially-available research methods texts. It uses an informal, conversational style to engage both the newcomer and the more experienced student of research. It is a fully hyperlinked text that can be integrated easily into an existing course structure or used as a sourcebook for the experienced researcher who simply wants to browse.

- Home
- Table of Contents
- Navigating
- Foundations
- Sampling
- Measurement
- Design
- Analysis
- Write-Up
- Appendices
- Search

Tags: análise de dados, b-learning, Estat Descritiva, inferência

## A New View of Statistics

Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino

**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

Quiz

Reference: Hopkins, W. G. (2000). A new view of statistics. Internet Society for Sport Science: http://www.sportsci.org/resource/stats/.

Tags: análise de dados, Estat Descritiva, inferência, previsão

## Probability and statistics EBook

Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino

**Um bom ebook com boas animações**

SOCR Books: This is a General Statistics Curriculum E-Book, which includes Advanced-Placement (AP) materials.

## Contents

- 1 Preface
- 2 Chapter I: Introduction to Statistics
- 3 Chapter II: Describing, Exploring, and Comparing Data
- 4 Chapter III: Probability
- 5 Chapter IV: Probability Distributions
- 6 Chapter V: Normal Probability Distribution
- 7 Chapter VI: Relations Between Distributions
- 8 Chapter VII: Point and Interval Estimates
- 9 Chapter VIII: Hypothesis Testing
- 10 Chapter IX: Inferences From Two Samples
- 11 Chapter X: Correlation and Regression
- 12 Chapter XI: Analysis of Variance (ANOVA)
- 13 Chapter XII: Non-Parametric Inference
- 13.1 Differences of Medians (Centers) of Two Paired Samples
- 13.2 Differences of Medians (Centers) of Two Independent Samples
- 13.3 Differences of Proportions of Two Samples
- 13.4 Differences of Means of Several Independent Samples
- 13.5 Differences of Variances of Independent Samples (Variance Homogeneity)

- 14 Chapter XIII: Multinomial Experiments and Contingency Tables
- 15 Chapter XIV:Bayesian Statistics
- 16 Chapter XV: Other Common Continuous Distributions
- 16.1 Gamma Distribution
- 16.2 Exponential Distribution
- 16.3 Pareto Distribution
- 16.4 Beta Distribution
- 16.5 Laplace (Double Exponential) Distribution
- 16.6 Cauchy Distribution
- 16.7 Chi-Square Distribution
- 16.8 Fisher’s F Distribution
- 16.9 Johnson SB Distribution
- 16.10 Rice Distribution
- 16.11 Uniform Distribution

- 17 Additional EBook Chapters (under Development)

Tags: análise de dados, Estat Descritiva, inferência

## Seeing Statistics

Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino, visualização

**Uma excelente maneira de aprender estatística com um webBook dinâmico e muito visual**

**Seeing Statistics ® ** is a new approach to teaching statistics using the World Wide Web. This webbook is based on three premises:

- The important principles of statistics are remarkably easy if they can be seen.

- Active involvement of the student facilitates learning.
- Statistics can be fun!

Most of the graphs and figures in this textbook are dynamic and under the control of the reader. Interacting with the graphs allows you to see the important statistical principles in action.

Rather than talk about it, it is best to view **Seeing Statistics** in action. To access Seeing Statistics, click on the “Enter Seeing Statistics” button to begin your exploration of **Seeing Statistics!**

**Table of Contents**

0. Introduction

1. Data & Comparisons

2. Seeing Data

3. Describing the Center

4. Describing the Spread

5. Seeing Data, Again

6. Probability

7. Normal Distribution

** 7. Outline**

7.0 Introduction

7.1 Origins of Normal

7.2 Size and Shape

7.3 Working with the Normal

7.4 Means have Normal Distributions

7.5 Evaluating Normality (optional)

7.6 Review

7.7 Exercises

8. Inference & Confidence

9. One-Sample Comparisons

10. Two-Sample Comparisons

Tags: análise de dados, Estat Descritiva, inferência

## Chance Lecture Video Series

Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino

### Bons vídeos ainda q antigos de alguns temas em probabilidade e estatística

### This page has links to:

The talks featured below require the latest version of the Realplayer software. More particularly, they require that the “Realplayer plug-in” be installed in the plug-ins folder of your browser. If you do not have the “Realplayer plug-in,” a free version of Realplayer (which includes the plug-in) is available here

Tags: inferência, problemas

## Science Isn’t Broken

Posted by Armando Brito Mendes | Filed under estatística, materiais para profissionais, visualização

Um bom blog com discussões interessantes e ilustrações muito boas

The Scientific Method 7:00 AM Aug 19, 2015

# Science Isn’t Broken

## It’s just a hell of a lot harder than we give it credit for.

Graphics by Ritchie King

If you follow the headlines, your confidence in science may have taken a hit lately.

Peer review? More like self-review. An investigation in November uncovered a scam in which researchers were rubber-stamping their own work, circumventing peer review at five high-profile publishers.

## Hack Your Way To Scientific Glory

**The U.S. economy is affected by whether Republicans or Democrats are in office.**Try to show that a connection exists, using real data going back to 1948. For your results to be publishable in an academic journal, you’ll need to prove that they are “statistically significant” by achieving a low enough p-value.

Tags: belo, inferência

## visualização do intervalo de confiança

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, visualização

Boa forma de visualizar o conceito de Intervalo de Confiança Aleatório.

## About the visualization

Some say that a shift from hypothesis testing to confidence intervals and estimation will lead to fewer statistical misinterpretations. Personally, I am not sure about that. But I agree with the sentiment that we should stop reducing statistical analysis to binary decision-making. The problem with CIs is that they are as unintuitive and as misunderstood *p*-values and null hypothesis significance testing. Moreover, CIs are often used to perform hypothesis tests and are therefore prone to the same misuses as *p*-values.

Tags: belo, definição, inferência