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The Resource A User's Guide to Business Analytics

A User's Guide to Business Analytics

Label
A User's Guide to Business Analytics
Title
A User's Guide to Business Analytics
Creator
Contributor
Subject
Language
eng
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
A User's Guide to Business Analytics
Label
A User's Guide to Business Analytics
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4683303
Publication
Copyright
Related Contributor
Related Location
Related Agents
Related Authorities
Related Subjects
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- 1: What Is Analytics? -- 1.1 Emergence and Application of Analytics -- 1.2 Comparison with Classical Statistical Analysis -- 1.3 Theory versus Computational Power -- 1.4 Fact versus Knowledge: Report versus Prediction -- 1.5 Actionable Insight -- 1.6 Suggested Further Reading -- 2: Introducing R-An Analytics Software -- 2.1 Basic System of R -- 2.2 Reading, Writing and Extracting Data in R -- 2.3 Statistics in R -- 2.4 Graphics in R -- 2.5 Further Notes about R -- 2.6 Suggested Further Reading -- 3: Reporting Data -- 3.1 What Is Data? -- 3.2 Types of Data -- 3.2.1 Qualitative versus Quantitative Variable -- 3.2.2 Discrete versus Continuous Data -- 3.2.3 Interval versus Ratio Type Data -- 3.3 Data Collection and Presentation -- 3.4 Reporting Current Status -- 3.4.1 Measures of Central Tendency -- 3.4.2 Measures of Dispersion -- 3.4.3 Measures of Association -- 3.5 Measures of Association for Categorical Variables -- 3.6 Suggested Further Reading -- 4: Statistical Graphics and Visual Analytics -- 4.1 Univariate and Bivariate Visualization -- 4.1.1 Univariate Visualization -- 4.1.2 Bivariate Visualization -- 4.2 Multivariate Visualization -- 4.3 Mapping Techniques -- 4.4 Scopes and Challenges of Visualization -- 4.5 Suggested Further Reading -- 5: Probability -- 5.1 Basic Set Theory -- 5.2 Classical Definition of Probability -- 5.3 Counting Rules -- 5.3.1 Multiplicative Rule (Basic Principle) of Counting -- 5.3.2 Permutations -- 5.4 Axiomatic Definition of Probability -- 5.5 Conditional Probability and Independence -- 5.5.1 Multiplicative Rule of Probability -- 5.5.2 Theorem of Total Probability -- 5.6 Bayes' Theorem -- 5.7 A Comprehensive Example -- 5.8 Appendix
  • 5.8.1 Criticisms of the Classical Definition -- 5.8.2 The Permutation Formula -- 5.8.3 Permutation with Non-Distinct Objects -- 5.8.4 Proof of the Theorem of Total Probability -- 5.8.5 Proof of Bayes' Theorem -- 5.9 Suggested Further Reading -- 6: Random Variables and Probability Distributions -- 6.1 Discrete and Continuous Random Variables -- 6.2 Some Special Discrete Distributions -- 6.2.1 Binomial Distribution -- 6.2.2 Hypergeometric Distribution -- 6.2.3 Poisson Distribution -- 6.3 Distribution Functions -- 6.4 Bivariate and Multivariate Distributions -- 6.4.1 Marginal Distributions -- 6.4.2 Independence -- 6.4.3 Conditional Distributions -- 6.5 Expectation -- 6.5.1 Moments -- 6.5.2 Covariance and Correlation -- 6.5.3 Moment Generating Functions -- 6.6 Appendix -- 6.6.1 Binomial PMF -- 6.6.2 Geometric Distribution -- 6.6.3 Negative Binomial Distribution -- 6.6.4 Poisson Distribution -- 6.6.5 Distribution Functions -- 6.6.6 Proof of Theorem 6.1 -- 6.6.7 Moment Generating Functions -- 6.6.8 Table of Parameters -- 6.7 Suggested Further Reading -- 7: Continuous Random Variables -- 7.1 PDF and CDF -- 7.2 Special Continuous Distributions -- 7.3 Expectation -- 7.3.1 Transformations of Random Variables -- 7.3.2 Moment Generating Function -- 7.4 Normal Distribution -- 7.5 Continuous Bivariate Distributions -- 7.5.1 Marginal and Conditional Distributions -- 7.6 Independence -- 7.7 Bivariate Normal Distribution -- 7.8 Sampling Distributions -- 7.8.1 Linear Combinations of Independent Variables -- 7.8.2 Distribution of the Sample Mean -- 7.8.3 Bias and Standard Error -- 7.9 Central Limit Theorem -- 7.9.1 Central Limit Theorem with Continuity Correction -- 7.10 Sampling Distributions Arising from the Normal -- 7.10.1 .2 Distribution -- 7.10.2 Student's t Distribution -- 7.10.3 The F Distribution
  • 7.11 Sampling from Two Independent Normals -- 7.12 Normal Q-Q Plots -- 7.13 Summary -- 7.14 Appendix -- 7.14.1 PDF and CDF -- 7.14.2 Other Continuous Distributions -- 7.14.3 Transformations of Random Variables -- 7.14.4 Continuous Bivariate Distributions -- 7.14.5 Linear Combinations of Independent Variables -- 7.14.6 Sums of Random Variables in Some Special Cases -- 7.14.7 Central Limit Theorem -- 7.15 Suggested Further Reading -- 8: Statistical Inference -- 8.1 Inference about a Single Mean -- 8.1.1 Point Estimation -- 8.1.2 Interval Estimation -- 8.1.3 Hypothesis Testing -- 8.2 Single-Population Mean with Unknown Variance -- 8.3 Two Sample t-Test: Independent Samples -- 8.4 Two Sample t-Test: Dependent (Paired) Samples -- 8.5 Analysis of Variance -- 8.6 Chi-Square Tests -- 8.6.1 Goodness-of-Fit Tests -- 8.6.2 Tests of Independence -- 8.7 Inference about Proportions -- 8.7.1 Inference about a Single Proportion -- 8.7.2 Inference about Two Proportions -- 8.8 Appendix -- 8.8.1 Maximum Likelihood Estimator -- 8.8.2 Levene's Test for Equality of Variances -- 8.8.3 Unbiasedness of the Pooled Estimator -- 8.9 Suggested Further Reading -- 9: Regression for Predictive Model Building -- 9.1 Simple Linear Regression -- 9.1.1 Regression ANOVA -- 9.1.2 Inference for Simple Linear Regression -- 9.1.3 Predicted Values, Confidence and Prediction Intervals -- 9.1.4 Regression Assumptions and Model Diagnostics -- 9.1.5 Outliers and Leverage Points -- 9.1.6 Transformation of Variables -- 9.2 Multiple Linear Regression -- 9.3 ANOVA for Multiple Linear Regression -- 9.3.1 Multicollinearity and Variance Inflation Factor -- 9.4 Hypotheses of Interest in Multiple Linear Regression -- 9.4.1 Categorical Predictors -- 9.5 Interaction -- 9.6 Regression Diagnostics -- 9.7 Regression Model Building
  • 9.7.1 Stepwise Procedure-Forward Selection and Backward Elimination -- 9.7.2 All Possible Regressions -- 9.8 Other Regression Techniques -- 9.9 Logistic Regression -- 9.10 Interpreting the Logistic Regression Model -- 9.11 Inference for the Logistic Regression Model -- 9.12 Goodness-of-Fit for the Logistic Regression Model -- 9.13 Hosmer-Lemeshow Statistics -- 9.14 Classification Table and Receiver Operating Curve -- 9.15 Suggested Further Reading -- 10: Decision Trees -- 10.1 Algorithm for Tree-Based Methods -- 10.2 Impurity Measures -- 10.3 Pruning a Tree -- 10.4 Aggregation Method: Bagging -- 10.5 Random Forest -- 10.6 Variable Importance -- 10.7 Decision Tree and Interaction among Predictors -- 10.8 Suggested Further Reading -- 11: Data Mining and Multivariate Methods -- 11.1 Principal Component Analysis -- 11.1.1 Total Variation in a Multivariate Dataset -- 11.1.2 Construction of Principal Components -- 11.2 Factor Analysis -- 11.3 Classification Problems -- 11.4 Discriminant Analysis -- 11.4.1 K Nearest Neighbor (KNN) Algorithm -- 11.5 Clustering Problem -- 11.5.1 Similarity and Dissimilarity -- 11.5.2 Hierarchical Clustering -- 11.5.3 K-Means Clustering -- 11.6 Suggested Further Reading -- 12: Modeling Time Series Data for Forecasting -- 12.1 Characteristics and Components of Time Series Data -- 12.1.1 Time Series Analysis Techniques -- 12.2 Time Series Decomposition -- 12.2.1 Trend Estimation in a Time Series: Moving Average -- 12.2.2 Decomposition of a Time Series -- 12.2.3 Single Exponential Smoothing -- 12.2.4 Double Exponential Smoothing -- 12.2.5 Triple Exponential Smoothing (Holt-Winters Model) -- 12.3 Autoregression Models -- 12.3.1 Introducing ARIMA (p, d, q) Models -- 12.3.2 Special Cases of ARIMA (p, d, q) Models -- 12.3.3 Stationary Time Series
  • 12.3.4 Identi cation of ARIMA (p, d, q) Parameters -- 12.3.5 Fitting of ARIMA Models -- 12.4 Forecasting Time Series Data -- 12.5 Other Time Series -- 12.6 Suggested Further Reading -- References -- Index
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{'f': 'http://opac.lib.rpi.edu/record=b4266567'}
Extent
1 online resource (401 pages)
Form of item
online
Isbn
9781466591660
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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