Tabla de Contenidos: “…Methodology -- 6.2. International rankings -- 6.3. Cybercrime and related legislation -- 6.4. …”

Tabla de Contenidos: “…Preface to the Second Edition xiii -- Preface to the First Edition xv -- 1 DATA-MINING CONCEPTS 1 -- 1.1 Introduction 1 -- 1.2 Data-Mining Roots 4 -- 1.3 Data-Mining Process 6 -- 1.4 Large Data Sets 9 -- 1.5 Data Warehouses for Data Mining 14 -- 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 -- 1.7 Organization of This Book 21 -- 1.8 Review Questions and Problems 23 -- 1.9 References for Further Study 24 -- 2 PREPARING THE DATA 26 -- 2.1 Representation of Raw Data 26 -- 2.2 Characteristics of Raw Data 31 -- 2.3 Transformation of Raw Data 33 -- 2.4 Missing Data 36 -- 2.5 Time-Dependent Data 37 -- 2.6 Outlier Analysis 41 -- 2.7 Review Questions and Problems 48 -- 2.8 References for Further Study 51 -- 3 DATA REDUCTION 53 -- 3.1 Dimensions of Large Data Sets 54 -- 3.2 Feature Reduction 56 -- 3.3 Relief Algorithm 66 -- 3.4 Entropy Measure for Ranking Features 68 -- 3.5 PCA 70 -- 3.6 Value Reduction 73 -- 3.7 Feature Discretization: ChiMerge Technique 77 -- 3.8 Case Reduction 80 -- 3.9 Review Questions and Problems 83 -- 3.10 References for Further Study 85 -- 4 LEARNING FROM DATA 87 -- 4.1 Learning Machine 89 -- 4.2 SLT 93 -- 4.3 Types of Learning Methods 99 -- 4.4 Common Learning Tasks 101 -- 4.5 SVMs 105 -- 4.6 kNN: Nearest Neighbor Classifi er 118 -- 4.7 Model Selection versus Generalization 122 -- 4.8 Model Estimation 126 -- 4.9 90% Accuracy: Now What? …”

Tabla de Contenidos: “…Understanding additional btree features -- Combined indexes -- Adding functional indexes -- Reducing space consumption -- Adding data while indexing -- Introducing operator classes -- Hacking up an operator class for a btree -- Creating new operators -- Creating operator classes -- Testing custom operator classes -- Understanding PostgreSQL index types -- Hash indexes -- GiST indexes -- Understanding how GiST works -- Extending GiST -- GIN indexes -- Extending GIN -- SP-GiST indexes -- BRIN indexes -- Extending BRIN indexes -- Adding additional indexes -- Achieving better answers with fuzzy searching -- Taking advantage of pg_trgm -- Speeding up LIKE queries -- Handling regular expressions -- Understanding full-text search -- Comparing strings -- Defining GIN indexes -- Debugging your search -- Gathering word statistics -- Taking advantage of exclusion operators -- Summary -- Q&amp -- A -- Chapter 4: Handling Advanced SQL -- Introducing grouping sets -- Loading some sample data -- Applying grouping sets -- Investigating performance -- Combining grouping sets with the FILTER clause -- Making use of ordered sets -- Understanding hypothetical aggregates -- Utilizing windowing functions and analytics -- Partitioning data -- Ordering data inside a window -- Using sliding windows -- Abstracting window clauses -- Making use of on-board windowing functions -- The rank and dense_rank functions -- The ntile() function -- The lead() and lag() functions -- The first_value(), nth_value(), and last_value() functions -- The row_number() function -- Writing your own aggregates -- Creating simple aggregates -- Adding support for parallel queries -- Improving efficiency -- Writing hypothetical aggregates -- Summary -- Chapter 5: Log Files and System Statistics -- Gathering runtime statistics -- Working with PostgreSQL system views -- Checking live traffic…”

Tabla de Contenidos: “…Selecting algorithms based on function -- Using Spark to generate real-time big data analytics -- Chapter 4 Math, Probability, and Statistical Modeling -- Exploring Probability and Inferential Statistics -- Probability distributions -- Conditional probability with Naïve Bayes -- Quantifying Correlation -- Calculating correlation with Pearson's r -- Ranking variable-pairs using Spearman's rank correlation -- Reducing Data Dimensionality with Linear Algebra -- Decomposing data to reduce dimensionality -- Reducing dimensionality with factor analysis -- Decreasing dimensionality and removing outliers with PCA -- Modeling Decisions with Multiple Criteria Decision-Making -- Turning to traditional MCDM -- Focusing on fuzzy MCDM -- Introducing Regression Methods -- Linear regression -- Logistic regression -- Ordinary least squares (OLS) regression methods -- Detecting Outliers -- Analyzing extreme values -- Detecting outliers with univariate analysis -- Detecting outliers with multivariate analysis -- Introducing Time Series Analysis -- Identifying patterns in time series -- Modeling univariate time series data -- Chapter 5 Grouping Your Way into Accurate Predictions -- Starting with Clustering Basics -- Getting to know clustering algorithms -- Examining clustering similarity metrics -- Identifying Clusters in Your Data -- Clustering with the k-means algorithm -- Estimating clusters with kernel density estimation (KDE) -- Clustering with hierarchical algorithms -- Dabbling in the DBScan neighborhood -- Categorizing Data with Decision Tree and Random Forest Algorithms -- Drawing a Line between Clustering and Classification -- Introducing instance-based learning classifiers -- Getting to know classification algorithms -- Making Sense of Data with Nearest Neighbor Analysis -- Classifying Data with Average Nearest Neighbor Algorithms…”

Tabla de Contenidos: “…Using indexes in an intelligent way -- Improving speed using clustered tables -- Clustering tables -- Making use of index only scans -- Understanding additional B-tree features -- Combined indexes -- Adding functional indexes -- Reducing space consumption -- Adding data while indexing -- Introducing operator classes -- Hacking up an operator class for a B-tree -- Creating new operators -- Creating operator classes -- Testing custom operator classes -- Understanding PostgreSQL index types -- Hash indexes -- GiST indexes -- Understanding how GiST works -- Extending GiST -- GIN indexes -- Extending GIN -- SP-GiST indexes -- BRIN indexes -- Extending BRIN indexes -- Adding additional indexes -- Achieving better answers with fuzzy searching -- Taking advantage of pg_trgm -- Speed up LIKE queries -- Handling regular expressions -- Understanding full-text search - FTS -- Comparing strings -- Defining GIN indexes -- Debugging your search -- Gathering word statistics -- Taking advantage of exclusion operators -- Summary -- Chapter 4: Handling Advanced SQL -- Introducing grouping sets -- Loading some sample data -- Applying grouping sets -- Investigating performance -- Combining grouping sets with the FILTER clause -- Making use of ordered sets -- Understanding hypothetical aggregates -- Utilizing windowing functions and analytics -- Partitioning data -- Ordering data inside a window -- Using sliding windows -- Abstracting window clauses -- Making use of onboard windowing functions -- rank and dense_rank functions -- ntile() function -- lead() and lag() functions -- first_value(), nth_value(), and last_value() functions -- row_number() function -- Writing your own aggregates -- Creating simple aggregates -- Adding support for parallel queries -- Improving efficiency -- Writing hypothetical aggregates -- Summary -- Chapter 5: Log Files and System Statistics…”

Tabla de Contenidos: “…Sampling by time window -- Extracting audio signatures -- Building a song analyzer -- Selling data science is all about selling cupcakes -- Using Cassandra -- Using the Play framework -- Building a recommender -- The PageRank algorithm -- Building a Graph of Frequency Co-occurrence -- Running PageRank -- Building personalized playlists -- Expanding our cupcake factory -- Building a playlist service -- Leveraging the Spark job server -- User interface -- Summary -- Chapter 9: News Dictionary and Real-Time Tagging System -- The mechanical Turk -- Human intelligence tasks -- Bootstrapping a classification model -- Learning from Stack Exchange -- Building text features -- Training a Naive Bayes model -- Laziness, impatience, and hubris -- Designing a Spark Streaming application -- A tale of two architectures -- The CAP theorem -- The Greeks are here to help -- Importance of the Lambda architecture -- Importance of the Kappa architecture -- Consuming data streams -- Creating a GDELT data stream -- Creating a Kafka topic -- Publishing content to a Kafka topic -- Consuming Kafka from Spark Streaming -- Creating a Twitter data stream -- Processing Twitter data -- Extracting URLs and hashtags -- Keeping popular hashtags -- Expanding shortened URLs -- Fetching HTML content -- Using Elasticsearch as a caching layer -- Classifying data -- Training a Naive Bayes model -- Thread safety -- Predict the GDELT data -- Our Twitter mechanical Turk -- Summary -- Chapter 10: Story De-duplication and Mutation -- Detecting near duplicates -- First steps with hashing -- Standing on the shoulders of the Internet giants -- Simhashing -- The hamming weight -- Detecting near duplicates in GDELT -- Indexing the GDELT database -- Persisting our RDDs -- Building a REST API -- Area of improvement -- Building stories -- Building term frequency vectors…”

Tabla de Contenidos: “…Eigenvector centrality; 1.4.8. Page rank; 1.4.9. Clustering d'un node; 1.5. L'estructura de les xarxes; 1.5.1. …”

Tabla de Contenidos: “…2.3 Random Shuffling2.4 Creating Game Worlds; 2.4.1 Starmap generation; 2.4.2 Terrain generation; 2.5 Summary; Exercises; 3 Tournaments; 3.1 Rank Adjustment Tournaments; 3.2 Elimination Tournaments; 3.3 Scoring Tournaments; 3.4 Summary; Exercises; 4 Game Trees; 4.1 Minimax; 4.1.1 Analysis; 4.1.2 Partial minimax; 4.2 Alpha-Beta Pruning; 4.2.1 Analysis; 4.2.2 Principal variation search; 4.3 Games of Chance; 4.4 Summary; Exercises; 5 Path Finding; 5.1 Discretization of the Game World; 5.1.1 Grid; 5.1.2 Navigation mesh; 5.2 Finding the Minimum Path; 5.2.1 Evaluation function; 5.2.2 Properties…”

Tabla de Contenidos: “…1 - Second General Artificial Intelligence Workshop (GAIW 2007) -- Towards Tractable Local Closed World Reasoning for the Semantic Web -- Optimal Brain Surgeon for General Dynamic Neural Networks -- Answer-Set Programming Based Dynamic User Modelling for Recommender Systems -- Application of Logic Wrappers to Hierarchical Data Extraction from HTML -- Relaxing Feature Selection in Spam Filtering by Using Case-Based Reasoning Systems -- Gödel and Computability -- Prospective Logic Agents -- An Iterative Process for Building Learning Curves and Predicting Relative Performance of Classifiers -- Modelling Morality with Prospective Logic -- Change Detection in Learning Histograms from Data Streams -- Real-Time Intelligent Decision Support System for Bridges Structures Behavior Prediction -- Semi-fuzzy Splitting in Online Divisive-Agglomerative Clustering -- On the Use of Rough Sets for User Authentication Via Keystroke Dynamics -- The Halt Condition in Genetic Programming -- Two Puzzles Concerning Measures of Uncertainty and the Positive Boolean Connectives -- 2 - First Workshop on AI Applications for Sustainable Transportation Systems (AIASTS 2007) -- Nonlinear Models for Determining Mode Choice -- Adaptation in Games with Many Co-evolving Agents -- 3 - Third Workshop on Artificial Life and Evolutionary Algorithms (ALEA 2007) -- Symmetry at the Genotypic Level and the Simple Inversion Operator -- A Genetic Programming Approach to the Generation of Hyper-Heuristics for the Incapacitated Examination Timetabling Problem -- Asynchronous Stochastic Dynamics and the Spatial Prisoner’s Dilemma Game -- Improving Evolutionary Algorithms with Scouting -- Stochastic Barycenters and Beta Distribution for Gaussian Particle Swarms -- Exploiting Second Order Information in Computational Multi-objective Evolutionary Optimization -- 4 - First Workshop on Ambient Intelligence Technologies and Applications (AMITA 2007) -- Ambient Intelligence – A State of the Art from Artificial Intelligence Perspective -- Ubiquitous Ambient Intelligence in a Flight Decision Assistance System -- Argumentation-Based Decision Making in Ambient Intelligence Environments -- Intelligent Mixed Reality for the Creation of Ambient Assisted Living -- Medical Imaging Environment – A Multi-Agent System for a Computer Clustering Based Multi-display -- 5 - Second Workshop on Building and Applying Ontologies for the Semantic Web (BAOSW 2007) -- Partial and Dynamic Ontology Mapping Model in Dialogs of Agents -- Using Ontologies for Software Development Knowledge Reuse -- 6 - First Workshop on Business Intelligence (BI 2007) -- Analysis of the Day-of-the-Week Anomaly for the Case of Emerging Stock Market -- A Metamorphosis Algorithm for the Optimization of a Multi-node OLAP System -- Experiments for the Number of Clusters in K-Means -- A Network Algorithm to Discover Sequential Patterns -- Adaptive Decision Support for Intensive Care -- A Tool for Interactive Subgroup Discovery Using Distribution Rules -- Quantitative Evaluation of Clustering’s for Marketing Applications: A Web Portal Case Study -- Resource-Bounded Fraud Detection -- 7 - First Workshop on Computational Methods in Bioinformatics and Systems Biology (CMBSB 2007) -- System Stability Via Stepping Optimal Control: Theory and Applications -- Evaluating Simulated Annealing Algorithms in the Optimization of Bacterial Strains -- Feature Extraction from Tumor Gene Expression Profiles Using DCT and DFT -- 8 - Second Workshop on Intelligent Robotics (IROBOT 2007) -- An Omnidirectional Vision System for Soccer Robots -- Generalization and Transfer Learning in Noise-Affected Robot Navigation Tasks -- Heuristic Q-Learning Soccer Players: A New Reinforcement Learning Approach to RoboCup Simulation -- Human Robot Interaction Based on Bayesian Analysis of Human Movements -- Understanding Dynamic Agent’s Reasoning -- 9 - Fourth Workshop on Multi-agent Systems: Theory and Applications (MASTA 2007) -- Convergence of Independent Adaptive Learners -- Multi-agent Learning: How to Interact to Improve Collective Results -- A Basis for an Exchange Value-Based Operational Notion of Morality for Multiagent Systems -- Intelligent Farmer Agent for Multi-agent Ecological Simulations Optimization -- Tax Compliance Through MABS: The Case of Indirect Taxes -- 10 - First Workshop on Search Techniques for Constraint Satisfaction (STCS 2007) -- Efficient and Tight Upper Bounds for Haplotype Inference by Pure Parsimony Using Delayed Haplotype Selection -- GRASPER -- 11 - Second Workshop on Text Mining and Applications (TEMA 2007) -- Text Segmentation Using Context Overlap -- Automatic Extraction of Definitions in Portuguese: A Rule-Based Approach -- N-Grams and Morphological Normalization in Text Classification: A Comparison on a Croatian-English Parallel Corpus -- Detection of Strange and Wrong Automatic Part-of-Speech Tagging -- New Techniques for Relevant Word Ranking and Extraction…”

Tabla de Contenidos: “…-- Writing a more complex program -- Packages and Cargo -- Cargo package layout -- Using third-party crates -- Tools and getting help -- Tools -- Text editor -- Getting help and documentation -- Summary -- Chapter 2: Building Our First Rocket Web Application -- Technical requirements -- Introducing Rocket - a web framework written in the Rust language -- The HTTP request life cycle in Rocket -- Rocket launch sequence -- Creating our first Rocket web application -- An asynchronous application -- Configuring our Rocket web application -- Starting the Rocket application in different profiles -- Configuring the Rocket web application -- Configuring the Rocket application using Rocket.toml -- Overriding the configuration with environment variables -- Getting help -- Summary -- Chapter 3: Rocket Requests and Responses -- Technical requirements -- Understanding Rocket routes -- HTTP methods -- URI -- Path -- Query -- Rank -- Format -- Data -- Implementing route handlers -- Creating responses -- Wrapping Responder -- Built-in implementations -- Making default error catchers -- Summary -- Chapter 4: Building, Igniting, and Launching Rocket -- Technical requirements -- Managing state -- Working with a database -- Attaching Rocket fairings -- Rocket phases -- Fairing callbacks -- Implementing and attaching fairings…”

Tabla de Contenidos: “…; 4.3 SPECIFYING CORRELATIONS; 4.3.1 Pearson Correlation Statistic; 4.3.2 Spearman (Rank) Correlation Statistic; 4.3.3 Using Crystal Ball to Calculate Correlations Between Two Assumptions; 4.3.4 Batch Fit; 4.3.5 Correlation Tool; CHAPTER 5 Using Decision Variables; 5.1 DEFINING DECISION VARIABLES…”

Tabla de Contenidos: “…Front Cover; Fossil Fuel Emissions Control Technologies; Copyright Page; Dedication; Contents; Preface; 1 Introduction; 1.1 Organization of this book; 1.2 Overview of energy usage in the United States; 1.3 Fossil fuel usage in boilers for heat and power production; 1.4 Characteristics and composition of fossil fuels; 1.4.1 Coal; 1.4.1.1 Introduction to coal; 1.4.1.2 Coal classification; 1.4.1.2.1 Basic coal analysis; 1.4.1.2.2 Coal rank; 1.4.1.2.3 Coal type; 1.4.1.2.4 Coal grade; 1.4.1.2.5 Classification systems; 1.4.1.2.5.1 The ASTM classification system…”

Tabla de Contenidos: “…Ecosystem value -- Measuring success -- Chapter summary -- CHAPTER 6 Systems of intelligence -- The rise of machine learning -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- The perfect companion to your data -- Five crucial factors in applying AI successfully -- Creating a system of intelligence -- Understand -- Identify -- Rank and plan -- Execute and monitor -- Assembling your team -- The five Vs of big data -- Constructing a highly defensible moat -- Vertical expertise -- Personalisation at scale -- Man and machine -- Network intelligence -- Integration intelligence -- Chapter summary -- Part III Accelerating change -- CHAPTER 7 Investment management -- The three types of innovation -- Disruptive innovation -- Sustaining innovation -- Efficiency-BASE D innovation -- Balancing short-term returns and long-term investments -- The investor's mindset -- Create a culture of innovation -- Move faster -- Encourage investment efficiency -- Chapter summary -- CHAPTER 8 Technology risk management -- Navigating the new rules of cybersecurity…”

Tabla de Contenidos: “…Mixture models and clustering -- Non-finite mixture model -- Dirichlet process -- Continuous mixtures -- Some common distributions are mixtures -- Summary -- Exercises -- Chapter 8: Gaussian Processes -- Linear models and non-linear data -- Modeling functions -- Multivariate Gaussians and functions -- Covariance functions and kernels -- Gaussian processes -- Gaussian process regression -- Gaussian process regression with PyMC -- Setting priors for the length scale -- Gaussian process classification -- GPs for space flu -- Cox processes -- Coal mining disasters -- Red wood -- Regression with spatial autocorrelation -- Hilbert space GPs -- HSGP with Bambi -- Summary -- Exercises -- Chapter 9: Bayesian Additive Regression Trees -- Decision trees -- BART models -- Bartian penguins -- Partial dependence plots -- Individual conditional plots -- Variable selection with BART -- Distributional BART models -- Constant and linear response -- Choosing the number of trees -- Summary -- Exercises -- Chapter 10: Inference Engines -- Inference engines -- The grid method -- Quadratic method -- Markovian methods -- Monte Carlo -- Markov chain -- Metropolis-Hastings -- Hamiltonian Monte Carlo -- Sequential Monte Carlo -- Diagnosing the samples -- Convergence -- Trace plot -- Rank plot -- , (R hat) -- Effective Sample Size (ESS) -- Monte Carlo standard error -- Divergences -- Keep calm and keep trying -- Summary -- Exercises -- Chapter 11: Where to Go Next -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos:

Tabla de Contenidos: “…Using the P/E ratio to judge company market value (stock price) -- Understanding variation among ratios -- The Dividend Payout Ratio -- Determining dividend payout -- Digging into companies' profits with dividends -- Return on Sales -- Figuring out ROS -- Reaching the truth about profits with ROS -- Return on Assets -- Doing some dividing to get ROA -- Ranking companies with the help of ROA -- Return on Equity -- Calculating ROE -- Testing companies using ROE -- The Big Three: Margins -- Dissecting gross margin -- Investigating operating margin -- Catching the leftover money: Net profit margin -- Chapter 12 Looking at Liquidity -- Finding the Current Ratio -- Calculating the current ratio -- What do the numbers mean? …”

Tabla de Contenidos: “…Derived column data types -- Product dimension integration -- R script transformation -- M editing tools -- Advanced Editor -- Visual Studio Code -- Visual Studio -- Summary -- Chapter 3: Designing Import and DirectQuery Data Models -- Dataset layers -- Dataset objectives -- Competing objectives -- External factors -- The Data Model -- The Relationships View -- The Data View -- The Report View -- Fact tables -- Fact table columns -- Fact column data types -- Fact-to-dimension relationships -- Dimension tables -- Hierarchies -- Custom sort -- Bridge tables -- Parameter tables -- Measure groups -- Last refreshed date -- Measure support logic -- Relationships -- Uniqueness -- Ambiguity -- Single-direction relationships -- Direct flights only -- Bidirectional relationships -- Shared dimensions -- Date dimensions -- The CROSSFILTER function -- Model metadata -- Visibility -- Column metadata -- Default Summarization -- Data format -- Data category -- Field descriptions -- Optimizing performance -- Import -- Columnar compression -- Memory analysis via DMVs -- DirectQuery -- Optimized DAX functions -- Columnstore and HTAP -- Summary -- Chapter 4: Developing DAX Measures and Security Roles -- DAX measures -- Filter context -- SQL equivalent -- Measure evaluation process -- Row context -- Scalar and table functions -- The CALCULATE() function -- Related tables -- The FILTER() function -- DAX variables -- Base measures -- Measure support expressions -- KPI Targets -- Current and prior periods -- Date intelligence metrics -- Current versus prior and growth rates -- Rolling periods -- Dimension metrics -- Missing dimensions -- Ranking metrics -- Dynamic ranking measures -- Security roles -- Dynamic row-level security -- Performance testing -- DAX Studio -- Tracing a Power BI dataset via DAX Studio -- Summary -- Chapter 5: Creating and Formatting Power BI Reports…”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Book 1 The Social Media Mix -- Chapter 1 Making the Business Case for Social Media -- Making Your Social Debut -- Defining Social Media Marketing -- Understanding the Benefits of Social Media -- Casting a wide net to catch your target market -- Branding -- Building relationships -- Improving business processes -- Improving search engine rankings -- Selling in the social media marketplace -- Finding alternative advertising opportunities -- Understanding the Cons of Social Media -- Integrating Social Media into Your Overall Marketing Effort -- Developing a Strategic Social Media Marketing Plan -- Establishing goals -- Setting quantifiable objectives -- Identifying your target markets -- Estimating costs -- Valuing social media ROI -- Chapter 2 Tallying the Bottom Line -- Preparing to Calculate Return on Investment -- Accounting for Customers Acquired Online -- Comparing the costs of customer acquisition -- One is silver and the other gold -- Establishing Key Performance Indicators for Sales -- Tracking Leads -- Understanding Other Common Business Metrics -- Break-even point -- Profit margin -- Revenue versus profit -- Determining Return on Investment -- Chapter 3 Plotting Your Social Media Marketing Strategy -- Locating Your Target Market Online -- Segmenting Your B2C Market -- Demographics -- Geographic location -- Purchasing behavior in different life stages -- Psychographics or lifestyle -- Affinity groups -- Researching B2B Markets -- Conducting Other Types of Market Research Online -- Identifying influencers -- Understanding why people use social media services -- Setting Up Your Social Media Marketing Plan -- Chapter 4 Managing Your Cybersocial Campaign…”

Tabla de Contenidos: “…Creating a PivotChart from a PivotTable -- Embedding a PivotChart on a PivotTable's worksheet -- Creating a PivotChart from an Excel table -- Working with PivotCharts -- Moving a PivotChart to another sheet -- Filtering a PivotChart -- Changing the PivotChart type -- Adding data labels to your PivotChart -- Sorting the PivotChart -- Adding PivotChart titles -- Moving the PivotChart legend -- Displaying a data table with the PivotChart -- Part 3 Discovering Advanced Data Analysis Tools -- Chapter 10 Tracking Trends and Making Forecasts -- Plotting a Best-Fit Trend line -- Calculating Best-Fit Values -- Plotting Forecasted Values -- Extending a Linear Trend -- Extending a linear trend using the fill handle -- Extending a linear trend using the Series command -- Calculating Forecasted Linear Values -- Plotting an Exponential Trend Line -- Calculating Exponential Trend Values -- Plotting a Logarithmic Trend Line -- Plotting a Power Trend Line -- Plotting a Polynomial Trend Line -- Chapter 11 Analyzing Data with Statistics -- Counting Things -- Counting numbers -- Counting nonempty cells -- Counting empty cells -- Counting cells that match criteria -- Counting cells that match multiple criteria -- Counting permutations -- Counting combinations -- Averaging Things -- Calculating an average -- Calculating a conditional average -- Calculating an average based on multiple conditions -- Calculating the median -- Calculating the mode -- Finding the Rank -- Determining the Nth Largest or Smallest Value -- Calculating the nth highest value -- Calculating the nth smallest value -- Creating a Grouped Frequency Distribution -- Calculating the Variance -- Calculating the Standard Deviation -- Finding the Correlation -- Chapter 12 Analyzing Data with Descriptive Statistics -- Loading the Analysis ToolPak -- Generating Descriptive Statistics…”

Tabla de Contenidos: “…Using Simple CASE Expressions -- Using Searched CASE Expressions -- Hierarchical Queries -- The Example Data -- Using the CONNECT BY and START WITH Clauses -- Using the LEVEL Pseudo Column -- Formatting the Results from a Hierarchical Query -- Starting at a Node Other than the Root -- Using a Subquery in a START WITH Clause -- Traversing Upward Through the Tree -- Eliminating Nodes and Branches from a Hierarchical Query -- Including Other Conditions in a Hierarchical Query -- Using Recursive Subquery Factoring to Query Hierarchical Data -- Using the ROLLUP and CUBE Clauses -- The Example Tables -- Using the ROLLUP Clause -- Using the CUBE Clause -- Using the GROUPING() Function -- Using the GROUPING SETS Clause -- Using the GROUPING_ID() Function -- Using a Column Multiple Times in a GROUP BY Clause -- Using the GROUP_ID() Function -- Using CROSS APPLY and OUTER APPLY -- CROSS APPLY -- OUTER APPLY -- Using LATERAL -- Summary -- Chapter 8: Analyzing Data -- Using Analytic Functions -- The Example Table -- Using the Ranking Functions -- Using the Inverse Percentile Functions -- Using the Window Functions -- Using the Reporting Functions -- Using the LAG() and LEAD() Functions -- Using the FIRST and LAST Functions -- Using the Linear Regression Functions -- Using the Hypothetical Rank and Distribution Functions -- Using the MODEL Clause -- An Example of the MODEL Clause -- Using Positional and Symbolic Notation to Access Cells -- Accessing a Range of Cells Using BETWEEN and AND -- Accessing All Cells Using ANY and IS ANY -- Getting the Current Value of a Dimension Using CURRENTV() -- Accessing Cells Using a FOR Loop -- Handling Null and Missing Values -- Updating Existing Cells -- Using the PIVOT and UNPIVOT Clauses -- A Simple Example of the PIVOT Clause -- Pivoting on Multiple Columns -- Using Multiple Aggregate Functions in a Pivot…”