Statistics for big data for dummies:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
[2015]
|
| Schriftenreihe: | ... for dummies
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Auf dem Cover: "Learn to: Collect, clean, and interpret data; effectively communicate data analysis; make good predictions" |
| Beschreibung: | xii, 366 Seiten Illustrationen, Diagramme |
| ISBN: | 1118940016 9781118940013 |
Internformat
MARC
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| 245 | 1 | 0 | |a Statistics for big data for dummies |c by Alan Anderson, PhD with David Semmelroth |
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Datensatz im Suchindex
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Titel: Statistics for big data for dummies
Autor: Anderson, Alan
Jahr: 2015
Contents at a Glance Introduction . 1 Part I: Introducing Big Bata Statistics . 7 Chapter 1: What Is Big Data and What Do You Do With It?.9 Chapter 2: Characteristics of Big Data: The Three Vs.19 Chapter 3: Using Big Data: The Hot Applications.27 Chapter 4: Understanding Probabilities.41 Chapter 5: Basic Statistical Ideas.57 Part II: Preparing and Cleaning Bata . 81 Chapter 6: Dirty Work: Preparing Your Data for Analysis.83 Chapter 7: Figuring the Format: Important Computer File Formats.99 Chapter 8: Checking Assumptions:
Testing for Normality.107 Chapter 9: Dealing with Missing or Incomplete Data.119 Chapter 10: Sending Out a Posse: Searching for Outliers.129 Part III: Exploratory Bata Analysis (EBA) . lb 1 Chapter 11: An Overview of Exploratory Data Analysis (EDA).143 Chapter 12: A Plot to Get Graphical: Graphical Techniques.155 Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques.173 Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques.191 Chapter 15: Regression Analysis.215 Chapter 16: When
You’ve Got the Time: Time Series Analysis.243 Part W: Big Bata Applications . 269 Chapter 17: Using Your Crystal Ball: Forecasting with Big Data.271 Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer.297 Chapter 19: Seeking Free Sources of Financial Data.319 Part V: The Part of Tens . 331 Chapter 20: Ten (or So) Best Practices in Data Preparation.333 Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA).339 Index . 369
Table of Contents ^ ?: t -v. « 4’ «. t © Introduction . 1 About This Book.2 Foolish Assumptions.3 Icons Used in This Book.4 Beyond the Book.4 Where to Go From Here.5 Part /: Introducing Big Bata Statistics . 7 Chapter 1: What Is Big Data and What Do You Do With It?.9 Characteristics of Big Data.9 Exploratory Data Analysis (EDA).10
Graphical EDA techniques.10 Quantitative EDA techniques.11 Statistical Analysis of Big Data.11 Probability distributions.12 Regression analysis.13 Time series analysis.14 Forecasting techniques.14 Chapter 2: Characteristics of Big Data: The Three Vs.19 Characteristics of Big Data.19 Volume.20 Velocity.21
Variety.22 Traditional Database Management Systems (DBMS).22 Relational model databases.22 Hierarchical model databases.24 Network model databases.25 Alternatives to traditional database systems.26 Chapter 3: Using Big Data: The Hot Applications.27 Big Data and Weather Forecasting.28 Big Data and Healthcare Services.30 Big Data and Insurance.31 Big Data and Finance.33 Big Data and Electric Utilities.34 Big Data and Higher Education.35
Statistics For Big Data For Dummies Big Data and Retailers.36 Nordstrom.36 Walmart.37 Amazon.com.37 Big Data and Search Engines.38 Big Data and Social Media.39 Chapter 4: Understanding Probabilities.41 The Core Structure: Probability Spaces.41 Discrete Probability Distributions.43 Counting outcomes.44
When only two things can happen: The binomial distribution.45 Continuous Probability Distributions.49 The normal distribution.49 Introducing Multivariate Probability Distributions.53 Joint probabilities.54 Unconditional probabilities.54 Conditional probabilities.55 Chapter 5: Basic Statistical Ideas.57 Some Preliminaries Regarding Data.57 Nominal data.58 Ordinal data.58
Summary Statistical Measures.58 Measures of central tendency.59 Measures of dispersion.63 Overview of Hypothesis Testing.66 The null hypothesis.66 The alternative hypothesis.67 The level of significance.67 The test statistic.68 The critical value (s).68 To reject or not to reject, that is the question.69 Measures of association.70 Higher-Order Measures.74 Skewness.75 Kurtosis.77 Part II: Preparing and Cleaning Data . 81 Chapter 6: Dirty Work: Preparing Your Data for Analysis.83 Passing the Eye Test: Does Your Data Look Correct?.84 Checking your sources.84 Verifying formats.85 Typecasting your data.86
Table of Contents Being Careful with Dates.87 Dealing with datetime formats.87 Taking geography into account.88 How your software thinks about dates.89 Does the Data Make Sense?.90 Checking discrete data.90 Checking continuous data.91 Frequently Encountered Data Headaches.93 Missing values.93 Duplicate records.95 Other Common Data Transformations.95
Percentiles.96 Standard scores.96 Dummy variables.97 Chapter 7: Figuring the Format: Important Computer File Formats . .99 Spreadsheet Formats.99 Comma-separated variables (.csv).100 Text.101 Microsoft Excel.102 Web formats.104 Database Formats.105 Microsoft
Access (.accdb).105 MySQL (.frm).106 Chapter 8: Checking Assumptions: Testing for Normality.107 Goodness of fit test.107 The chi-square distribution.108 The null and alternative hypotheses.109 The level of significance.109 Computing the test statistic.109 The critical value.113 The decision.114 Jarque-Bera test.115
Skewness.115 Kurtosis.115 Excess kurtosis.116 The null and alternative hypotheses.116 Computing the test statistic.116 The critical value.117 Chapter 9: Dealing with Missing or Incomplete Data.119 Missing Data: What’s the Problem?.119 Nonresponse.120 Missingness.120
Statistics For Big Data For Dummies i/iii Techniques for Dealing with Missing Data.122 Deletion techniques.122 Imputation techniques.124 Expectation-maximization (EM).127 Chapter 10: Sending Out a Posse: Searching for Outliers.129 Testing for Outliers.130 Graphical tests of outliers.131 Hypothesis tests for outliers.135 Robust Statistics.138 Dealing with Outliers.139 Part
III: Exploratory Data Analysis (EDA) . / 4 1 Chapter 11: An Overview of Exploratory Data Analysis (EDA).143 Graphical EDA Techniques.144 Box plots.145 Histograms.145 Scatter plots.146 Normal probability plots.148 EDA Techniques for Testing Assumptions.148 Run sequence plot.148 Lag plot.149 Histogram.150
Normal probability plot.151 Quantitative EDA Techniques.152 Interval estimation.152 Hypothesis testing.153 Chapter 12: A Plot to Get Graphical: Graphical Techniques.155 Stem-and-Leaf Plots.155 Scatter Plots.157 Box Plots.161 Histograms.163 Quantile-Quantile (QQ) Plots.165 Autocorrelation Plots.168 Chapter 13: You're the Only Variable for Me: Univariate Statistical Techniques.173 Counting Events Over a Time Interval: The Poisson Distribution.174 Continuous Probability Distributions.176 The Student’s t-distribution.177 The lognormal distribution.181 The chi-square distribution.184 The F-distribution.187
Table of Contents Chapter 14: To All the Variables We've Encountered: Multivariate Statistical Techniques.191 Testing Hypotheses about Two Population Means.192 The null hypothesis for two population means.192 Alternative hypotheses for two population means.193 Level of significance.193 Test statistics and critical values for testing hypotheses about two population means.193 Independent populations.194 The decision.196 The case of dependent populations.196 Using Analysis of Variance (ANOVA) to Test Hypotheses about Population Means.199
The F-Distribution.204 Finding the critical values using the F-table.205 Making a decision.206 F-Test for the Equality of Two Population Variances.206 Null hypothesis.207 Alternative hypothesis.207 Level of significance.207 Test statistic.207 Critical values.208 Decision rule.208 Correlation.209
Pearson’s product-moment correlation coefficient.210 Spearman’s rank correlation coefficient.212 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. The coefficient of determination (R 2 ). Computing the coefficient of determination. The t-test. Using Statistical Software. Excel. Using the p-value. Assumptions of Simple Linear Regression. Violations of the assumptions. Multiple Regression Analysis. Predicting the value of Y . Testing the results of the multiple regression equation Multicollinearity. 215 216 216 217 222 222 224 224 228 228 229 230 230 231 233 234 241
X Statistics For Big Data For Dummies Chapter 16: When You've Got the Time: Time Series Analysis.243 Key Properties of a Time Series.243 Trend component.244 Seasonal component.244 Cyclical component.244 Irregular component.245 Forecasting with Decomposition Methods.245 Multiplicative decomposition.245 Additive decomposition.247 Smoothing Techniques.247
Moving averages.248 Centered moving averages with an odd period size.250 Centered moving averages with an even period size.250 Exponential smoothing.252 Seasonal Components.255 Modeling a Time Series with Regression Analysis.257 Identifying the trend.258 Estimating the trend.260 Forecasting with time series regression.262 Estimating a quadratic trend.263 Comparing Different Models: MAD and MSE.267 Mean absolute
deviation (MAD).267 Mean square error (MSE).268 Part W: 8iq Data Applications . 269 Chapter 17: Using Your Crystal Ball: Forecasting with Big Data.271 ARIMA Modeling.271 Testing for stationarity.272 Adjustments for nonstationarity.275 Steps used in ARIMA modeling.276 Moving average (MA) processes.279 Autoregressive (AR) processes.281 Autoregressive moving average (ARMA) processes.283 Autoregressive integrated moving average (ARIMA) processes.285 Simulation Techniques.286 Historical simulation.287 Monte Carlo simulation.290
Table of Contents Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer.297 Excelling at Excel.298 Key Excel statistical functions.298 Updated statistical functions.304 Analysis ToolPak.307 Programming with Visual Basic for Applications (VBA).310 R, Matey!.314 Chapter 19: Seeking Free Sources of Financial Data.319 Yahoo! Finance.320 The ticker symbol.320
Downloading historical stock prices.321 Finding stock option prices.322 Analyzing options strategies.323 Finding key statistics for a corporation.325 Other information on Yahoo! Finance.325 Federal Reserve Economic Data (FRED).326 Finding macroeconomic data on FRED.327 Finding financial data on FRED.327 Board of Governors of the Federal Reserve System.328 U.S. Department of the Treasury.329 Other Useful Financial Websites.330 Part V: The Part
of Tens . 331 Chapter 20: Ten (or So) Best Practices in Data Preparation.333 Check Data Formats.333 Verify Data Types.334 Graph Your Data.334 Verify Data Accuracy.334 Identify Outliers.335 Deal with Missing Values.335 Check Your Assumptions about How the Data Is Distributed.336 Back Up and Document Everything You Do.337 Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA).339 What Are the Key Properties of a Dataset?.339 What’s the Center of the Data?.340 How Much Spread Is There in the Data?.341 Is the Data Skewed?.343
Statistics For Big Data For Dummies What Distribution Does the Data Follow?.344 Are the Elements in the Dataset Uncorrelated?.345 Does the Center of the Dataset Change Over Time?.346 Does the Spread of the Dataset Change Over Time?.347 Are There Outliers in the Data?.347 Does the Data Conform to Our Assumptions?.348 Index . 349 |
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| spelling | Anderson, Alan H. 1950- Verfasser (DE-588)14333140X aut Statistics for big data for dummies by Alan Anderson, PhD with David Semmelroth Hoboken, NJ Wiley [2015] © 2015 xii, 366 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier ... for dummies Auf dem Cover: "Learn to: Collect, clean, and interpret data; effectively communicate data analysis; make good predictions" Big Data (DE-588)4802620-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Big Data Data mining Statistik Big Data (DE-588)4802620-7 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s Data Mining (DE-588)4428654-5 s DE-604 Semmelroth, David Verfasser (DE-588)1081040084 aut Erscheint auch als Online-Ausgabe, EPUB 978-1-118-94002-0 Erscheint auch als Online-Ausgabe, PDF 978-1-118-94003-7 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=5164703&prov=M&dok_var=1&dok_ext=htm Inhaltstext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028107590&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Anderson, Alan H. 1950- Semmelroth, David Statistics for big data for dummies Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd |
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| title | Statistics for big data for dummies |
| title_auth | Statistics for big data for dummies |
| title_exact_search | Statistics for big data for dummies |
| title_full | Statistics for big data for dummies by Alan Anderson, PhD with David Semmelroth |
| title_fullStr | Statistics for big data for dummies by Alan Anderson, PhD with David Semmelroth |
| title_full_unstemmed | Statistics for big data for dummies by Alan Anderson, PhD with David Semmelroth |
| title_short | Statistics for big data for dummies |
| title_sort | statistics for big data for dummies |
| topic | Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Big Data Data Mining Datenanalyse Statistik |
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| work_keys_str_mv | AT andersonalanh statisticsforbigdatafordummies AT semmelrothdavid statisticsforbigdatafordummies |