[Dummies 01] • Statistics for Big Data for Dummies by Anderson, Alan -- Read -- Imperial Library of Trantor

Index

Cover Title Page Table of Contents Introduction

About This Book Foolish Assumptions Icons Used in This Book Beyond the Book Where to Go From Here

Part I: Introducing Big Data Statistics

Chapter 1: What Is Big Data and What Do You Do with It?

Characteristics of Big Data Exploratory Data Analysis (EDA) Statistical Analysis of Big Data

Chapter 2: Characteristics of Big Data: The Three Vs

Characteristics of Big Data Traditional Database Management Systems (DBMS)

Chapter 3: Using Big Data: The Hot Applications

Big Data and Weather Forecasting Big Data and Healthcare Services Big Data and Insurance Big Data and Finance Big Data and Electric Utilities Big Data and Higher Education Big Data and Retailers Big Data and Search Engines Big Data and Social Media

Chapter 4: Understanding Probabilities

The Core Structure: Probability Spaces Discrete Probability Distributions Continuous Probability Distributions Introducing Multivariate Probability Distributions

Chapter 5: Basic Statistical Ideas

Some Preliminaries Regarding Data Summary Statistical Measures Overview of Hypothesis Testing Higher-Order Measures

Part II: Preparing and Cleaning Data

Chapter 6: Dirty Work: Preparing Your Data for Analysis

Passing the Eye Test: Does Your Data Look Correct? Being Careful with Dates Does the Data Make Sense? Frequently Encountered Data Headaches Other Common Data Transformations

Chapter 7: Figuring the Format: Important Computer File Formats

Spreadsheet Formats Database Formats

Chapter 8: Checking Assumptions: Testing for Normality

Goodness of fit test Jarque-Bera test

Chapter 9: Dealing with Missing or Incomplete Data

Missing Data: What’s the Problem? Techniques for Dealing with Missing Data

Chapter 10: Sending Out a Posse: Searching for Outliers

Testing for Outliers Robust Statistics Dealing with Outliers

Part III: Exploratory Data Analysis (EDA)

Chapter 11: An Overview of Exploratory Data Analysis (EDA)

Graphical EDA Techniques EDA Techniques for Testing Assumptions Quantitative EDA Techniques

Chapter 12: A Plot to Get Graphical: Graphical Techniques

Stem-and-Leaf Plots Scatter Plots Box Plots Histograms Quantile-Quantile (QQ) Plots Autocorrelation Plots

Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques

Counting Events Over a Time Interval: The Poisson Distribution Continuous Probability Distributions

Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques

Testing Hypotheses about Two Population Means Using Analysis of Variance (ANOVA) to Test Hypotheses about Population Means The F-Distribution F-Test for the Equality of Two Population Variances Correlation

Chapter 15: Regression Analysis

The Fundamental Assumption: Variables Have a Linear Relationship Defining the Population Regression Equation Estimating the Population Regression Equation Testing the Estimated Regression Equation Using Statistical Software Assumptions of Simple Linear Regression Multiple Regression Analysis Multicollinearity

Chapter 16: When You’ve Got the Time: Time Series Analysis

Key Properties of a Time Series Forecasting with Decomposition Methods Smoothing Techniques Seasonal Components Modeling a Time Series with Regression Analysis Comparing Different Models: MAD and MSE

Part IV: Big Data Applications

Chapter 17: Using Your Crystal Ball: Forecasting with Big Data

ARIMA Modeling Simulation Techniques

Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer

Excelling at Excel Programming with Visual Basic for Applications (VBA) R, Matey!

Chapter 19: Seeking Free Sources of Financial Data

Yahoo! Finance Federal Reserve Economic Data (FRED) Board of Governors of the Federal Reserve System U.S. Department of the Treasury Other Useful Financial Websites

Part V: The Part of Tens

Chapter 20: Ten (or So) Best Practices in Data Preparation

Check Data Formats Verify Data Types Graph Your Data Verify Data Accuracy Identify Outliers Deal with Missing Values Check Your Assumptions about How the Data Is Distributed Back Up and Document Everything You Do

Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA)

What Are the Key Properties of a Dataset? What’s the Center of the Data? How Much Spread Is There in the Data? Is the Data Skewed? What Distribution Does the Data Follow? Are the Elements in the Dataset Uncorrelated? Does the Center of the Dataset Change Over Time? Does the Spread of the Dataset Change Over Time? Are There Outliers in the Data? Does the Data Conform to Our Assumptions?

About the Authors Cheat Sheet Advertisement Page Connect with Dummies End User License Agreement

← Prev
Back
Next →

← Prev
Back
Next →