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Ctrl+shift+enter [[electronic resource] ] : mastering Excel array formulas / / Mike Girvin
Ctrl+shift+enter [[electronic resource] ] : mastering Excel array formulas / / Mike Girvin
Autore Girvin Mike
Pubbl/distr/stampa Uniontown, OH, : Holy Macro! Books, 2013
Descrizione fisica 1 online resource (353 p.)
Disciplina 005.54
Soggetto topico Electronic spreadsheets - Computer programs
ISBN 1-61547-209-6
1-61547-109-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover -- Title page -- Copyright page -- Contents -- Dedications -- About the Author -- Acknowledgements -- Introduction -- Chapter 1: Formula Basics -- Chapter 2: Introduction to Array Formulas -- Chapter 3: Math Array Operations -- Chapter 4: Comparative Array Operations and Aggregate Calculations with One or More Conditions -- Chapter 5: Join Array Operations -- Chapter 6: Function Argument Array Operations -- Chapter 7: Array Constants -- Chapter 8: Array Formulas That Deliver More Than One Value -- Chapter 9: A First Look at Array Functions: TRANSPOSE, MODE.MULT, and TREND -- Chapter 10: The Amazing SUMPRODUCT Function (and SUMIFS, Too) -- Chapter 11: Boolean Logic: AND Criteria and OR Criteria -- Chapter 12: When Is an Array Formula Really Needed? -- Chapter 13: Dynamic Ranges with the INDEX and OFFSET Functions -- Chapter 14: Array Formula Efficiency Rules -- Chapter 15: Extracting Data, Based on Criteria -- Chapter 16: The FREQUENCY Array Function -- Chapter 17: Unique Counting Formulas and the Power of the FREQUENCY Function -- Chapter 18: The MMULT Array Function -- Chapter 19: Extracting Unique Lists and Sorting Formulas -- Chapter 20: Conditional Formatting with Array Formulas -- Chapter 21: Data Tables -- Chapter 22: The LINEST Array Function -- Chapter 23: Can You Figure Out How the Huge Array Formula Works? -- Conclusion -- Index.
Record Nr. UNINA-9910814971103321
Girvin Mike  
Uniontown, OH, : Holy Macro! Books, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Mining for Business Analytics : Concepts, Techniques, and Applications with XLMiner
Data Mining for Business Analytics : Concepts, Techniques, and Applications with XLMiner
Autore Bruce Peter C
Edizione [3rd ed.]
Pubbl/distr/stampa Hoboken : , : John Wiley & Sons, Incorporated, , 2016
Descrizione fisica 1 online resource (549 pages)
Disciplina 005.54
Altri autori (Persone) ShmueliGalit
PatelNitin R
Soggetto topico Business--Data processing
Soggetto genere / forma Electronic books.
ISBN 9781118729243
9781118729472
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Dedication -- Contents -- Foreword -- Preface to the Third Edition -- Preface to the First Edition -- Acknowledgments -- Part I: Preliminaries -- Chapter 1: Introduction -- 1.1 What Is Business Analytics? -- 1.2 What Is Data Mining? -- 1.3 Data Mining and Related Terms -- 1.4 Big Data -- 1.5 Data Science -- 1.6 Why Are There So Many Different Methods? -- 1.7 Terminology and Notation -- 1.8 Road Maps to This Book -- Order of Topics -- Chapter 2: Overview of the Data Mining Process -- 2.1 Introduction -- 2.2 Core Ideas in Data Mining -- Classification -- Prediction -- Association Rules and Recommendation Systems -- Predictive Analytics -- Data Reduction and Dimension Reduction -- Data Exploration and Visualization -- Supervised and Unsupervised Learning -- 2.3 The Steps in Data Mining -- 2.4 Preliminary Steps -- Organization of Datasets -- Sampling from a Database -- Oversampling Rare Events in Classification Tasks -- Preprocessing and Cleaning the Data -- 2.5 Predictive Power and Overfitting -- Creation and Use of Data Partitions -- Overfitting -- 2.6 Building a Predictive Model with XLMiner -- Predicting Home Values in the West Roxbury Neighborhood -- Modeling Process -- 2.7 Using Excel for Data Mining -- 2.8 Automating Data Mining Solutions -- Data Mining Software Tools: the State of the Market -- Problems -- Part II: Data Exploration and Dimension Reduction -- Chapter 3: Data Visualization -- 3.1 Uses of Data Visualization -- 3.2 Data Examples -- Example 1: Boston Housing Data -- Example 2: Ridership on Amtrak Trains -- 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots -- Distribution Plots: Boxplots and Histograms -- Heatmaps: Visualizing Correlations and Missing Values -- 3.4 Multidimensional Visualization -- Adding Variables: Color, Size, Shape, Multiple Panels, and Animation.
Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering -- Reference: Trend Line and Labels -- Scaling up to Large Datasets -- Multivariate Plot: Parallel Coordinates Plot -- Interactive Visualization -- 3.5 Specialized Visualizations -- Visualizing Networked Data -- Visualizing Hierarchical Data: Treemaps -- Visualizing Geographical Data: Map Charts -- 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal -- Prediction -- Classification -- Time Series Forecasting -- Unsupervised Learning -- Problems -- Chapter 4: Dimension Reduction -- 4.1 Introduction -- 4.2 Curse of Dimensionality -- 4.3 Practical Considerations -- Example 1: House Prices in Boston -- 4.4 Data Summaries -- Summary Statistics -- Pivot Tables -- 4.5 Correlation Analysis -- 4.6 Reducing the Number of Categories in Categorical Variables -- 4.7 Converting a Categorical Variable to a Numerical Variable -- 4.8 Principal Components Analysis -- Example 2: Breakfast Cereals -- Principal Components -- Normalizing the Data -- Using Principal Components for Classification and Prediction -- 4.9 Dimension Reduction Using Regression Models -- 4.10 Dimension Reduction Using Classification and Regression Trees -- Problems -- Part III: Performance Evaluation -- Chapter 5: Evaluating Predictive Performance -- 5.1 Introduction -- 5.2 Evaluating Predictive Performance -- Benchmark: The Average -- Prediction Accuracy Measures -- Comparing Training and Validation Performance -- Lift Chart -- 5.3 Judging Classifier Performance -- Benchmark: The Naive Rule -- Class Separation -- The Classification Matrix -- Using the Validation Data -- Accuracy Measures -- Propensities and Cutoff for Classification -- Performance in Unequal Importance of Classes -- Asymmetric Misclassification Costs -- Generalization to More Than Two Classes -- 5.4 Judging Ranking Performance.
Lift Charts for Binary Data -- Decile Lift Charts -- Beyond Two Classes -- Lift Charts Incorporating Costs and Benefits -- Lift as Function of Cutoff -- 5.5 Oversampling -- Oversampling the Training Set -- Evaluating Model Performance Using a Non-oversampled Validation Set -- Evaluating Model Performance If Only Oversampled Validation Set Exists -- Problems -- Part IV: Prediction and Classification Methods -- Chapter 6: Multiple Linear Regression -- 6.1 Introduction -- 6.2 Explanatory vs. Predictive Modeling -- 6.3 Estimating the Regression Equation and Prediction -- Example: Predicting the Price of Used Toyota Corolla Cars -- 6.4 Variable Selection in Linear Regression -- Reducing the Number of Predictors -- How to Reduce the Number of Predictors -- Problems -- Chapter 7: κ-Nearest-Neighbors (κ-NN) -- 7.1 The -NN Classifier (categorical outcome) -- Determining Neighbors -- Classification Rule -- Example: Riding Mowers -- Choosing -- Setting the Cutoff Value -- -NN with More Than Two Classes -- Converting Categorical Variables to Binary Dummies -- 7.2 -NN for a Numerical Response -- 7.3 Advantages and Shortcomings of -NN Algorithms -- Problems -- Chapter 8: The Naive Bayes Classifier -- 8.1 Introduction -- Cutoff Probability Method -- Conditional Probability -- Example 1: Predicting Fraudulent Financial Reporting -- 8.2 Applying the Full (Exact) Bayesian Classifier -- Using the "Assign to the Most Probable Class" Method -- Using the Cutoff Probability Method -- Practical Difficulty with the Complete (Exact) Bayes Procedure -- Solution: Naive Bayes -- Example 2: Predicting Fraudulent Financial Reports, Two Predictors -- Example 3: Predicting Delayed Flights -- 8.3 Advantages and Shortcomings of the Naive Bayes Classifier -- Problems -- Chapter 9: Classification and Regression Trees -- 9.1 Introduction -- 9.2 Classification Trees.
Recursive Partitioning -- Example 1: Riding Mowers -- Measures of Impurity -- Tree Structure -- Classifying a New Observation -- 9.3 Evaluating the Performance of a Classification Tree -- Example 2: Acceptance of Personal Loan -- 9.4 Avoiding Overfitting -- Stopping Tree Growth: CHAID -- Pruning the Tree -- 9.5 Classification Rules from Trees -- 9.6 Classification Trees for More Than two Classes -- 9.7 Regression Trees -- Prediction -- Measuring Impurity -- Evaluating Performance -- 9.8 Advantages, Weaknesses, and Extensions -- 9.9 Improving Prediction: Multiple Trees -- Problems -- Chapter 10: Logistic Regression -- 10.1 Introduction -- 10.2 The Logistic Regression Model -- Example: Acceptance of Personal Loan -- Model with a Single Predictor -- Estimating the Logistic Model from Data: Computing Parameter Estimates -- Interpreting Results in Terms of Odds (for a Profiling Goal) -- 10.3 Evaluating Classification Performance -- Variable Selection -- 10.4 Example of Complete Analysis: Predicting Delayed Flights -- Data Preprocessing -- Model Fitting and Estimation -- Model Interpretation -- Model Performance -- Variable Selection -- 10.5 Appendix: Logistic Regression for Profiling -- Appendix A: Why Linear Regression Is Problematic for a Categorical Response -- Appendix B: Evaluating Explanatory Power -- Appendix C: Logistic Regression for More Than Two Classes -- Problems -- Chapter 11: Neural Nets -- 11.1 Introduction -- 11.2 Concept and Structure of a Neural Network -- 11.3 Fitting a Network to Data -- Example 1: Tiny Dataset -- Computing Output of Nodes -- Preprocessing the Data -- Training the Model -- Example 2: Classifying Accident Severity -- Avoiding Overfitting -- Using the Output for Prediction and Classification -- 11.4 Required User Input -- 11.5 Exploring the Relationship Between Predictors and Response.
11.6 Advantages and Weaknesses of Neural Networks -- Unsupervised Feature Extraction and Deep Learning -- Problems -- Chapter 12: Discriminant Analysis -- 12.1 Introduction -- Example 1: Riding Mowers -- Example 2: Personal Loan Acceptance -- 12.2 Distance of an Observation from a Class -- 12.3 Fisher's Linear Classification Functions -- 12.4 Classification Performance of Discriminant Analysis -- 12.5 Prior Probabilities -- 12.6 Unequal Misclassification Costs -- 12.7 Classifying More Than Two Classes -- Example 3: Medical Dispatch to Accident Scenes -- 12.8 Advantages and Weaknesses -- Problems -- Chapter 13: Combining Methods: Ensembles and Uplift Modeling -- 13.1 Ensembles -- Why Ensembles Can Improve Predictive Power -- Simple Averaging -- Bagging -- Boosting -- Advantages and Weaknesses of Ensembles -- 13.2 Uplift (Persuasion) Modeling -- A-B Testing -- Uplift -- Gathering the Data -- A Simple Model -- Modeling Individual Uplift -- Using the Results of an Uplift Model -- 13.3 Summary -- Problems -- Part V: Mining Relationships among Records -- Chapter 14: Association Rules and Collaborative Filtering -- 14.1 Association Rules -- Discovering Association Rules in Transaction Databases -- Example 1: Synthetic Data on Purchases of Phone Faceplates -- Generating Candidate Rules -- The Apriori Algorithm -- Selecting Strong Rules -- Data Format -- The Process of Rule Selection -- Interpreting the Results -- Rules and Chance -- Example 2: Rules for Similar Book Purchases -- 14.2 Collaborative Filtering -- Data Type and Format -- Example 3: Netflix Prize Contest -- User-Based Collaborative Filtering: "People Like You" -- Item-Based Collaborative Filtering -- Advantages and Weaknesses of Collaborative Filtering -- Collaborative Filtering vs. Association Rules -- 14.3 Summary -- Problems -- Chapter 15: Cluster Analysis -- 15.1 Introduction.
Example: Public Utilities.
Record Nr. UNINA-9910795808803321
Bruce Peter C  
Hoboken : , : John Wiley & Sons, Incorporated, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Mining for Business Analytics : Concepts, Techniques, and Applications with XLMiner
Data Mining for Business Analytics : Concepts, Techniques, and Applications with XLMiner
Autore Bruce Peter C
Edizione [3rd ed.]
Pubbl/distr/stampa Hoboken : , : John Wiley & Sons, Incorporated, , 2016
Descrizione fisica 1 online resource (549 pages)
Disciplina 005.54
Altri autori (Persone) ShmueliGalit
PatelNitin R
Soggetto topico Business--Data processing
Soggetto genere / forma Electronic books.
ISBN 9781118729243
9781118729472
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Dedication -- Contents -- Foreword -- Preface to the Third Edition -- Preface to the First Edition -- Acknowledgments -- Part I: Preliminaries -- Chapter 1: Introduction -- 1.1 What Is Business Analytics? -- 1.2 What Is Data Mining? -- 1.3 Data Mining and Related Terms -- 1.4 Big Data -- 1.5 Data Science -- 1.6 Why Are There So Many Different Methods? -- 1.7 Terminology and Notation -- 1.8 Road Maps to This Book -- Order of Topics -- Chapter 2: Overview of the Data Mining Process -- 2.1 Introduction -- 2.2 Core Ideas in Data Mining -- Classification -- Prediction -- Association Rules and Recommendation Systems -- Predictive Analytics -- Data Reduction and Dimension Reduction -- Data Exploration and Visualization -- Supervised and Unsupervised Learning -- 2.3 The Steps in Data Mining -- 2.4 Preliminary Steps -- Organization of Datasets -- Sampling from a Database -- Oversampling Rare Events in Classification Tasks -- Preprocessing and Cleaning the Data -- 2.5 Predictive Power and Overfitting -- Creation and Use of Data Partitions -- Overfitting -- 2.6 Building a Predictive Model with XLMiner -- Predicting Home Values in the West Roxbury Neighborhood -- Modeling Process -- 2.7 Using Excel for Data Mining -- 2.8 Automating Data Mining Solutions -- Data Mining Software Tools: the State of the Market -- Problems -- Part II: Data Exploration and Dimension Reduction -- Chapter 3: Data Visualization -- 3.1 Uses of Data Visualization -- 3.2 Data Examples -- Example 1: Boston Housing Data -- Example 2: Ridership on Amtrak Trains -- 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots -- Distribution Plots: Boxplots and Histograms -- Heatmaps: Visualizing Correlations and Missing Values -- 3.4 Multidimensional Visualization -- Adding Variables: Color, Size, Shape, Multiple Panels, and Animation.
Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering -- Reference: Trend Line and Labels -- Scaling up to Large Datasets -- Multivariate Plot: Parallel Coordinates Plot -- Interactive Visualization -- 3.5 Specialized Visualizations -- Visualizing Networked Data -- Visualizing Hierarchical Data: Treemaps -- Visualizing Geographical Data: Map Charts -- 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal -- Prediction -- Classification -- Time Series Forecasting -- Unsupervised Learning -- Problems -- Chapter 4: Dimension Reduction -- 4.1 Introduction -- 4.2 Curse of Dimensionality -- 4.3 Practical Considerations -- Example 1: House Prices in Boston -- 4.4 Data Summaries -- Summary Statistics -- Pivot Tables -- 4.5 Correlation Analysis -- 4.6 Reducing the Number of Categories in Categorical Variables -- 4.7 Converting a Categorical Variable to a Numerical Variable -- 4.8 Principal Components Analysis -- Example 2: Breakfast Cereals -- Principal Components -- Normalizing the Data -- Using Principal Components for Classification and Prediction -- 4.9 Dimension Reduction Using Regression Models -- 4.10 Dimension Reduction Using Classification and Regression Trees -- Problems -- Part III: Performance Evaluation -- Chapter 5: Evaluating Predictive Performance -- 5.1 Introduction -- 5.2 Evaluating Predictive Performance -- Benchmark: The Average -- Prediction Accuracy Measures -- Comparing Training and Validation Performance -- Lift Chart -- 5.3 Judging Classifier Performance -- Benchmark: The Naive Rule -- Class Separation -- The Classification Matrix -- Using the Validation Data -- Accuracy Measures -- Propensities and Cutoff for Classification -- Performance in Unequal Importance of Classes -- Asymmetric Misclassification Costs -- Generalization to More Than Two Classes -- 5.4 Judging Ranking Performance.
Lift Charts for Binary Data -- Decile Lift Charts -- Beyond Two Classes -- Lift Charts Incorporating Costs and Benefits -- Lift as Function of Cutoff -- 5.5 Oversampling -- Oversampling the Training Set -- Evaluating Model Performance Using a Non-oversampled Validation Set -- Evaluating Model Performance If Only Oversampled Validation Set Exists -- Problems -- Part IV: Prediction and Classification Methods -- Chapter 6: Multiple Linear Regression -- 6.1 Introduction -- 6.2 Explanatory vs. Predictive Modeling -- 6.3 Estimating the Regression Equation and Prediction -- Example: Predicting the Price of Used Toyota Corolla Cars -- 6.4 Variable Selection in Linear Regression -- Reducing the Number of Predictors -- How to Reduce the Number of Predictors -- Problems -- Chapter 7: κ-Nearest-Neighbors (κ-NN) -- 7.1 The -NN Classifier (categorical outcome) -- Determining Neighbors -- Classification Rule -- Example: Riding Mowers -- Choosing -- Setting the Cutoff Value -- -NN with More Than Two Classes -- Converting Categorical Variables to Binary Dummies -- 7.2 -NN for a Numerical Response -- 7.3 Advantages and Shortcomings of -NN Algorithms -- Problems -- Chapter 8: The Naive Bayes Classifier -- 8.1 Introduction -- Cutoff Probability Method -- Conditional Probability -- Example 1: Predicting Fraudulent Financial Reporting -- 8.2 Applying the Full (Exact) Bayesian Classifier -- Using the "Assign to the Most Probable Class" Method -- Using the Cutoff Probability Method -- Practical Difficulty with the Complete (Exact) Bayes Procedure -- Solution: Naive Bayes -- Example 2: Predicting Fraudulent Financial Reports, Two Predictors -- Example 3: Predicting Delayed Flights -- 8.3 Advantages and Shortcomings of the Naive Bayes Classifier -- Problems -- Chapter 9: Classification and Regression Trees -- 9.1 Introduction -- 9.2 Classification Trees.
Recursive Partitioning -- Example 1: Riding Mowers -- Measures of Impurity -- Tree Structure -- Classifying a New Observation -- 9.3 Evaluating the Performance of a Classification Tree -- Example 2: Acceptance of Personal Loan -- 9.4 Avoiding Overfitting -- Stopping Tree Growth: CHAID -- Pruning the Tree -- 9.5 Classification Rules from Trees -- 9.6 Classification Trees for More Than two Classes -- 9.7 Regression Trees -- Prediction -- Measuring Impurity -- Evaluating Performance -- 9.8 Advantages, Weaknesses, and Extensions -- 9.9 Improving Prediction: Multiple Trees -- Problems -- Chapter 10: Logistic Regression -- 10.1 Introduction -- 10.2 The Logistic Regression Model -- Example: Acceptance of Personal Loan -- Model with a Single Predictor -- Estimating the Logistic Model from Data: Computing Parameter Estimates -- Interpreting Results in Terms of Odds (for a Profiling Goal) -- 10.3 Evaluating Classification Performance -- Variable Selection -- 10.4 Example of Complete Analysis: Predicting Delayed Flights -- Data Preprocessing -- Model Fitting and Estimation -- Model Interpretation -- Model Performance -- Variable Selection -- 10.5 Appendix: Logistic Regression for Profiling -- Appendix A: Why Linear Regression Is Problematic for a Categorical Response -- Appendix B: Evaluating Explanatory Power -- Appendix C: Logistic Regression for More Than Two Classes -- Problems -- Chapter 11: Neural Nets -- 11.1 Introduction -- 11.2 Concept and Structure of a Neural Network -- 11.3 Fitting a Network to Data -- Example 1: Tiny Dataset -- Computing Output of Nodes -- Preprocessing the Data -- Training the Model -- Example 2: Classifying Accident Severity -- Avoiding Overfitting -- Using the Output for Prediction and Classification -- 11.4 Required User Input -- 11.5 Exploring the Relationship Between Predictors and Response.
11.6 Advantages and Weaknesses of Neural Networks -- Unsupervised Feature Extraction and Deep Learning -- Problems -- Chapter 12: Discriminant Analysis -- 12.1 Introduction -- Example 1: Riding Mowers -- Example 2: Personal Loan Acceptance -- 12.2 Distance of an Observation from a Class -- 12.3 Fisher's Linear Classification Functions -- 12.4 Classification Performance of Discriminant Analysis -- 12.5 Prior Probabilities -- 12.6 Unequal Misclassification Costs -- 12.7 Classifying More Than Two Classes -- Example 3: Medical Dispatch to Accident Scenes -- 12.8 Advantages and Weaknesses -- Problems -- Chapter 13: Combining Methods: Ensembles and Uplift Modeling -- 13.1 Ensembles -- Why Ensembles Can Improve Predictive Power -- Simple Averaging -- Bagging -- Boosting -- Advantages and Weaknesses of Ensembles -- 13.2 Uplift (Persuasion) Modeling -- A-B Testing -- Uplift -- Gathering the Data -- A Simple Model -- Modeling Individual Uplift -- Using the Results of an Uplift Model -- 13.3 Summary -- Problems -- Part V: Mining Relationships among Records -- Chapter 14: Association Rules and Collaborative Filtering -- 14.1 Association Rules -- Discovering Association Rules in Transaction Databases -- Example 1: Synthetic Data on Purchases of Phone Faceplates -- Generating Candidate Rules -- The Apriori Algorithm -- Selecting Strong Rules -- Data Format -- The Process of Rule Selection -- Interpreting the Results -- Rules and Chance -- Example 2: Rules for Similar Book Purchases -- 14.2 Collaborative Filtering -- Data Type and Format -- Example 3: Netflix Prize Contest -- User-Based Collaborative Filtering: "People Like You" -- Item-Based Collaborative Filtering -- Advantages and Weaknesses of Collaborative Filtering -- Collaborative Filtering vs. Association Rules -- 14.3 Summary -- Problems -- Chapter 15: Cluster Analysis -- 15.1 Introduction.
Example: Public Utilities.
Record Nr. UNINA-9910823158603321
Bruce Peter C  
Hoboken : , : John Wiley & Sons, Incorporated, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining for business analytics : concepts, techniques, and applications in Microsoft Office Excel with XLMiner / / Galit Shmueli, Nitin R. Patel, Peter C. Bruce
Data mining for business analytics : concepts, techniques, and applications in Microsoft Office Excel with XLMiner / / Galit Shmueli, Nitin R. Patel, Peter C. Bruce
Autore Shmueli Galit <1971->
Edizione [Third edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2016
Descrizione fisica 1 online resource (583 p.)
Disciplina 005.54
Soggetto topico Business - Data processing
Data mining
Soggetto genere / forma Electronic books.
ISBN 1-118-72924-2
1-118-72913-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- k-Nearest Neighbors (kNN) -- The Naive Bayes Classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Association rules and collaborative filtering -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Social network analytics -- Text mining.
Record Nr. UNINA-9910465753503321
Shmueli Galit <1971->  
Hoboken, New Jersey : , : Wiley, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The deductive spreadsheet / / Iliano Cervesato
The deductive spreadsheet / / Iliano Cervesato
Autore Cervesato Iliano
Edizione [1st ed. 2013.]
Pubbl/distr/stampa Heidelberg [Germany] : , : Springer, , 2013
Descrizione fisica 1 online resource (xxi, 406 pages) : illustrations (some color)
Disciplina 005.54
Collana Cognitive Technologies
Soggetto topico Electronic spreadsheets
Deductive databases
Logic programming
ISBN 3-642-37747-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chap. 1 Introduction -- Chap. 2  Requirements -- Part I. Deductive Engine -- Chap. 3 The Traditional Spreadsheet -- Chap. 4 The Relational Spreadsheet -- Chap. 5 The Logical Spreadsheet -- Chap. 6 The Deductive Spreadsheet -- Part II. User Interface 184 -- Chap. 7 The Traditional Interface -- Chap. 8 Cognitive Interface Design -- Chap. 9 Toward a Deductive Interface -- Chap. 10 Preliminary Experiments -- Chap. 11 Future Developments.
Record Nr. UNINA-9910437583903321
Cervesato Iliano  
Heidelberg [Germany] : , : Springer, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Eserciziario di Excel : 160 esercizi risolti e commentati / Massimo Ballerini ... [et al.]
Eserciziario di Excel : 160 esercizi risolti e commentati / Massimo Ballerini ... [et al.]
Edizione [3. ed]
Pubbl/distr/stampa Milano, : EGEA, 2021
Descrizione fisica XIX, 447 p. ; 24 cm
Disciplina 005.54
Collana Tools
Soggetto topico Microsoft Excel - Esercizi
ISBN 978-88-7534-209-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Titolo uniforme
Record Nr. UNISA-996482072403316
Milano, : EGEA, 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Essentials of Excel VBA, Python, and R . Volume I Financial Statistics and Portfolio Analysis / / John Lee and Cheng-Few Lee
Essentials of Excel VBA, Python, and R . Volume I Financial Statistics and Portfolio Analysis / / John Lee and Cheng-Few Lee
Autore Lee John
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (XVI, 696 p. 1113 illus., 1005 illus. in color.)
Disciplina 005.54
Soggetto topico Electronic spreadsheets - Computer programs
Finance - Data processing
Finance - Statistical methods
Python (Computer program language)
Finances
Estadística matemàtica
Processament de dades
Python (Llenguatge de programació)
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 3-031-14236-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Introduction -- Chapter 2. Data Collection, Presentation, and Yahoo Finance -- Chapter 3. Histograms and the Rate of Returns of JPM and JNJ -- Chapter 4. Numerical Summary Measures on Stock Rates of Return and Market Rates of Return -- Chapter 5. Probability Concepts and their Analysis -- Chapter 6. Discrete Random Variables and Probability Distributions -- Chapter 7. The Normal and Lognormal Distributions -- Chapter 8. Sampling Distributions and Central Limit Theorem -- Chapter 9. Other Continuous Distributions -- Chapter 10. Estimation -- Chapter 11. Hypothesis Testing -- Chapter 12. Analysis of Variance and Chi-Square Tests -- Chapter 13. Simple Linear Regression and the Correlation Coefficient -- Chapter 14. Simple Linear Regression and Correlation: Analyses and Applications -- Chapter 15. Multiple Linear Regression -- Chapter 16. Residual and Regression Assumption Analysis -- Chapter 17. Nonparametric Statistics -- Chapter 18. Time Series: Analysis, Model, and Forecasting -- Chapter 19. Index Numbers and Stock Market Indexes -- Chapter 20. Sampling Surveys: Methods and Applications -- Chapter 21. Statistical Decision Theory -- Chapter 22. Sources of Risks and their Determination -- Chapter 23. Risk-Aversion, Capital Asset Allocation, and Markowitz Portfolio Selection Model -- Chapter 24. Capital Asset Pricing Model and Beta Forecasting -- Chapter 25. Single-Index Models for Portfolio Selection -- Chapter 26. Sharpe Performance Measure and Treynor Performance Measure Approach to Portfolio Analysis.
Record Nr. UNINA-9910637731803321
Lee John  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Essentials of Excel VBA, Python, and R . Volume I Financial Statistics and Portfolio Analysis / / John Lee and Cheng-Few Lee
Essentials of Excel VBA, Python, and R . Volume I Financial Statistics and Portfolio Analysis / / John Lee and Cheng-Few Lee
Autore Lee John
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (XVI, 696 p. 1113 illus., 1005 illus. in color.)
Disciplina 005.54
Soggetto topico Electronic spreadsheets - Computer programs
Finance - Data processing
Finance - Statistical methods
Python (Computer program language)
Finances
Estadística matemàtica
Processament de dades
Python (Llenguatge de programació)
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 3-031-14236-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Introduction -- Chapter 2. Data Collection, Presentation, and Yahoo Finance -- Chapter 3. Histograms and the Rate of Returns of JPM and JNJ -- Chapter 4. Numerical Summary Measures on Stock Rates of Return and Market Rates of Return -- Chapter 5. Probability Concepts and their Analysis -- Chapter 6. Discrete Random Variables and Probability Distributions -- Chapter 7. The Normal and Lognormal Distributions -- Chapter 8. Sampling Distributions and Central Limit Theorem -- Chapter 9. Other Continuous Distributions -- Chapter 10. Estimation -- Chapter 11. Hypothesis Testing -- Chapter 12. Analysis of Variance and Chi-Square Tests -- Chapter 13. Simple Linear Regression and the Correlation Coefficient -- Chapter 14. Simple Linear Regression and Correlation: Analyses and Applications -- Chapter 15. Multiple Linear Regression -- Chapter 16. Residual and Regression Assumption Analysis -- Chapter 17. Nonparametric Statistics -- Chapter 18. Time Series: Analysis, Model, and Forecasting -- Chapter 19. Index Numbers and Stock Market Indexes -- Chapter 20. Sampling Surveys: Methods and Applications -- Chapter 21. Statistical Decision Theory -- Chapter 22. Sources of Risks and their Determination -- Chapter 23. Risk-Aversion, Capital Asset Allocation, and Markowitz Portfolio Selection Model -- Chapter 24. Capital Asset Pricing Model and Beta Forecasting -- Chapter 25. Single-Index Models for Portfolio Selection -- Chapter 26. Sharpe Performance Measure and Treynor Performance Measure Approach to Portfolio Analysis.
Record Nr. UNISA-996508570103316
Lee John  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Excel 2003 VBA programmer's reference [[electronic resource] /] / Paul Kimmel ... [et al.]
Excel 2003 VBA programmer's reference [[electronic resource] /] / Paul Kimmel ... [et al.]
Edizione [1st edition]
Pubbl/distr/stampa Indianapolis, IN., : Wiley Pub., c2004
Descrizione fisica 1 online resource (1176 p.)
Disciplina 005.54
Altri autori (Persone) KimmelPaul
Collana Programmer to programmer
Soggetto topico Business - Computer programs
Electronic spreadsheets
Computer software - Development
Soggetto genere / forma Electronic books.
ISBN 1-280-26598-1
9786610265985
0-7645-7898-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto About the Authors; Acknowledgments; Introduction; Chapter 1: Primer in Excel VBA; Using the Macro Recorder; Recording Macros; Running Macros; The Visual Basic Editor; Other Ways to Run Macros; User Defined Functions; Creating a UDF; What UDFs Cannot Do; The Excel Object Model; Objects; Getting Help; Experimenting in the Immediate Window; The VBA Language; Basic Input and Output; Calling Functions and Subroutines; Variable Declaration; Scope and Lifetime of Variables; Variable Type; Object Variables; Making Decisions; Looping; Arrays; Runtime Error Handling; Summary
Chapter 2: Programming in the VBEWriting Code; Programming for People; Writing Code; Where Does My Code Go?; Managing a Project; Adding Classes; Modifying Properties; Importing and Exporting Visual Basic Code; Editing; Managing Editor Options; Running and Debugging Code; Using Watches; Using the Object Browser; Summary; Chapter 3: The Application Object; Globals; The Active Properties; Display Alerts; Screen Updating; Evaluate; InputBox; StatusBar; SendKeys; OnTime; OnKey; Worksheet Functions; Caller; Summary; Chapter 4: Object-Oriented Theory and VBA; Comparing Classes and Interfaces
Defining an InterfaceImplementing an Interface; Defining Methods; Parameters; Implementing Recursive Methods; Eliminating Recursion with Loops; Defining Fields; Defining Properties; Defining Events; Defining Events in Classes; Raising Events; Handling Events; Information Hiding and Access Modifiers; Encapsulation, Aggregation, and References; Summary; Chapter 5: Event Procedures; Worksheet Events; Enable Events; Worksheet Calculate; Chart Events; Before Double Click; Workbook Events; Save Changes; Headers and Footers; Summary; Chapter 6: Class Modules; Creating Your Own Objects
Using CollectionsClass Module Collection; Trapping Application Events; Embedded Chart Events; A Collection of UserForm Controls; Referencing Classes Across Projects; Summary; Chapter 7: Writing Bulletproof Code; Using Debug.Print; Using Debug.Assert; A Brief Exemplar of PC Debugging; Creating Reusable Tools with the Debug Object; Tracing Code Execution; Trapping Code Execution Paths; Asserting Application Invariants; Raising Errors; Writing Error Handlers; On Error Goto Line Number; On Error Resume Next; On Error GoTo 0; Using the Err Object; Scaffolding; Writing to the EventLog; Summary
Chapter 8: Debugging and TestingStepping Through Code; Running Your Code; Stepping into Your Code; Step Over; Step Out; Run to Cursor; Set Next Statement; Show Next Statement; Using Breakpoints; Using Watches; Add Watch; Edit Watch; Quick Watch; Locals Windows; Testing an Expression in the Immediate Window; Resources for Finding Definitions; Edit => Quick Info; Edit => Parameter Info; Edit => Complete Word; Edit => List Properties/Methods; Edit => List Constants; Edit => Bookmarks; View => Definition; View => Object Browser; Viewing the Call Stack; Asserting Application Invariants; Summary
Chapter 9: UserForms
Record Nr. UNINA-9910449940303321
Indianapolis, IN., : Wiley Pub., c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Excel 2003 VBA programmer's reference [[electronic resource] /] / Paul Kimmel ... [et al.]
Excel 2003 VBA programmer's reference [[electronic resource] /] / Paul Kimmel ... [et al.]
Edizione [1st edition]
Pubbl/distr/stampa Indianapolis, IN., : Wiley Pub., c2004
Descrizione fisica 1 online resource (1176 p.)
Disciplina 005.54
Altri autori (Persone) KimmelPaul
Collana Programmer to programmer
Soggetto topico Business - Computer programs
Electronic spreadsheets
Computer software - Development
ISBN 1-280-26598-1
9786610265985
0-7645-7898-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto About the Authors; Acknowledgments; Introduction; Chapter 1: Primer in Excel VBA; Using the Macro Recorder; Recording Macros; Running Macros; The Visual Basic Editor; Other Ways to Run Macros; User Defined Functions; Creating a UDF; What UDFs Cannot Do; The Excel Object Model; Objects; Getting Help; Experimenting in the Immediate Window; The VBA Language; Basic Input and Output; Calling Functions and Subroutines; Variable Declaration; Scope and Lifetime of Variables; Variable Type; Object Variables; Making Decisions; Looping; Arrays; Runtime Error Handling; Summary
Chapter 2: Programming in the VBEWriting Code; Programming for People; Writing Code; Where Does My Code Go?; Managing a Project; Adding Classes; Modifying Properties; Importing and Exporting Visual Basic Code; Editing; Managing Editor Options; Running and Debugging Code; Using Watches; Using the Object Browser; Summary; Chapter 3: The Application Object; Globals; The Active Properties; Display Alerts; Screen Updating; Evaluate; InputBox; StatusBar; SendKeys; OnTime; OnKey; Worksheet Functions; Caller; Summary; Chapter 4: Object-Oriented Theory and VBA; Comparing Classes and Interfaces
Defining an InterfaceImplementing an Interface; Defining Methods; Parameters; Implementing Recursive Methods; Eliminating Recursion with Loops; Defining Fields; Defining Properties; Defining Events; Defining Events in Classes; Raising Events; Handling Events; Information Hiding and Access Modifiers; Encapsulation, Aggregation, and References; Summary; Chapter 5: Event Procedures; Worksheet Events; Enable Events; Worksheet Calculate; Chart Events; Before Double Click; Workbook Events; Save Changes; Headers and Footers; Summary; Chapter 6: Class Modules; Creating Your Own Objects
Using CollectionsClass Module Collection; Trapping Application Events; Embedded Chart Events; A Collection of UserForm Controls; Referencing Classes Across Projects; Summary; Chapter 7: Writing Bulletproof Code; Using Debug.Print; Using Debug.Assert; A Brief Exemplar of PC Debugging; Creating Reusable Tools with the Debug Object; Tracing Code Execution; Trapping Code Execution Paths; Asserting Application Invariants; Raising Errors; Writing Error Handlers; On Error Goto Line Number; On Error Resume Next; On Error GoTo 0; Using the Err Object; Scaffolding; Writing to the EventLog; Summary
Chapter 8: Debugging and TestingStepping Through Code; Running Your Code; Stepping into Your Code; Step Over; Step Out; Run to Cursor; Set Next Statement; Show Next Statement; Using Breakpoints; Using Watches; Add Watch; Edit Watch; Quick Watch; Locals Windows; Testing an Expression in the Immediate Window; Resources for Finding Definitions; Edit => Quick Info; Edit => Parameter Info; Edit => Complete Word; Edit => List Properties/Methods; Edit => List Constants; Edit => Bookmarks; View => Definition; View => Object Browser; Viewing the Call Stack; Asserting Application Invariants; Summary
Chapter 9: UserForms
Record Nr. UNINA-9910783278403321
Indianapolis, IN., : Wiley Pub., c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui