Emotion and Information Processing A Practical Approach. by Mohanty, Sachi Nandan, SpringerLink (Online service)

Table of Contents: “…Chapter 5: Effect on the Emotional Self-Esteem of Women with Reference to Make-Up -- 5.1 Introduction -- 5.2 Objectives of the Study -- 5.3 Literature Review -- 5.4 Conceptual Framework -- 5.5 Hypothesis -- 5.6 Methodology -- 5.7 Data Analysis -- 5.7.1 Finding out the Response Rate (Table 5.1) -- 5.7.2 Finding out Descriptive Statistics -- 5.7.2.1 Frequencies (Table 5.2) -- 5.7.3 Reliability -- 5.7.4 Using Mean and Standard Deviation -- 5.7.5 Hypothesis 1 -- 5.7.6 Using Correlation and Regression Analysis -- 5.7.7 Using Regression to Prove Hypothesis -- 5.7.8 Hypothesis 2…”
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Data Reduction and Analysis by Raghavender, U. S., PALCI EBSCO books

Table of Contents: “…3.5 Estimates Of Location3.6 Mean; 3.7 Median And Robust Estimates; 3.8 Estimates Of Variability; 3.9 Standard Deviation And Related Estimates; 3.10 Conclusions; Chapter 4 Data Science With Python And R; 4.1 Dataframe; 4.2 Reading The Files; 4.3 Indexing And Slicing; 4.4 Data Selection; 4.5 Function Mapping And Grouping; 4.6 Aggregate; 4.7 Conclusions; Chapter 5 Error Analysis; 5.1 Uncertainties In Data; 5.2 Propagation of Errors; 5.3 Conclusions; Chapter 6 Principal Component Analysis; 6.1 Preparing Our TB Data; 6.2 Using R For PCA; 6.3 Exploring Data Structure With K-Means Clustering…”
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Understanding and Applying Basic Statistical Methods Using R. by Wilcox, Rand R., ProQuest Ebook Subscriptions

Table of Contents: “…2.2.2 The Median -- 2.2.3 Sample Mean versus Sample Median -- 2.2.4 Trimmed Mean -- 2.2.5 R function mean, tmean, and median -- 2.3 Quartiles -- 2.3.1 R function idealf and summary -- 2.4 Measures of Variation -- 2.4.1 The Range -- 2.4.2 R function Range -- 2.4.3 Deviation Scores, Variance, and Standard Deviation -- 2.4.4 R Functions var and sd -- 2.4.5 The Interquartile Range -- 2.4.6 MAD and the Winsorized Variance -- 2.4.7 R Functions winvar, winsd, idealfIQR, and mad -- 2.5 Detecting Outliers -- 2.5.1 A Classic Outlier Detection Method -- 2.5.2 The Boxplot Rule -- 2.5.3 The MAD-Median Rule…”
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Bayesian Statistics : An Introduction. by Lee, Peter M., ProQuest Ebook Subscriptions

Table of Contents: “…1.3.4 The normal distribution -- 1.3.5 Mixed random variables -- 1.4 Several random variables -- 1.4.1 Two discrete random variables -- 1.4.2 Two continuous random variables -- 1.4.3 Bayes' Theorem for random variables -- 1.4.4 Example -- 1.4.5 One discrete variable and one continuous variable -- 1.4.6 Independent random variables -- 1.5 Means and variances -- 1.5.1 Expectations -- 1.5.2 The expectation of a sum and of a product -- 1.5.3 Variance, precision and standard deviation -- 1.5.4 Examples -- 1.5.5 Variance of a sum -- covariance and correlation…”
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The process of enterprise risk management / by Oh, Kok-Boon, PALCI EBSCO books

Table of Contents: “…Mean-Variance Space (Standard Deviation); 3.4.1. Volatility and Expected Returns; 3.4.1.1. …”
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Data Science The Executive Summary - a Technical Book for Non-Technical Professionals. by Cady, Field, ProQuest Ebook Subscriptions

Table of Contents: “…4.2 Outliers, Visualizations, and the Limits of Summary Statistics: A Picture Is Worth a Thousand Numbers -- 4.3 Experiments, Correlation, and Causality -- 4.4 Summarizing One Number -- 4.5 Key Properties to Assess: Central Tendency, Spread, and Heavy Tails -- 4.5.1 Measuring Central Tendency -- 4.5.1.1 Mean -- 4.5.1.2 Median -- 4.5.1.3 Mode -- 4.5.2 Measuring Spread -- 4.5.2.1 Standard Deviation -- 4.5.2.2 Percentiles -- 4.5.3 Advanced Material: Managing Heavy Tails -- 4.6 Summarizing Two Numbers: Correlations and Scatterplots -- 4.6.1 Correlations -- 4.6.1.1 Pearson Correlation…”
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Learning pandas / by Heydt, Michael, ProQuest Ebook Subscriptions

Table of Contents: “…Categorical values as an index -- CategoricalIndex -- Indexing by date and time using DatetimeIndex -- Indexing periods of time using PeriodIndex -- Working with Indexes -- Creating and using an index with a Series or DataFrame -- Selecting values using an index -- Moving data to and from the index -- Reindexing a pandas object -- Hierarchical indexing -- Summary -- Chapter 7: Categorical Data -- Configuring pandas -- Creating Categoricals -- Renaming categories -- Appending new categories -- Removing categories -- Removing unused categories -- Setting categories -- Descriptive information of a Categorical -- Munging school grades -- Summary -- Chapter 8: Numerical and Statistical Methods -- Configuring pandas -- Performing numerical methods on pandas objects -- Performing arithmetic on a DataFrame or Series -- Getting the counts of values -- Determining unique values (and their counts) -- Finding minimum and maximum values -- Locating the n-smallest and n-largest values -- Calculating accumulated values -- Performing statistical processes on pandas objects -- Retrieving summary descriptive statistics -- Measuring central tendency: mean, median, and mode -- Calculating the mean -- Finding the median -- Determining the mode -- Calculating variance and standard deviation -- Measuring variance -- Finding the standard deviation -- Determining covariance and correlation -- Calculating covariance -- Determining correlation -- Performing discretization and quantiling of data -- Calculating the rank of values -- Calculating the percent change at each sample of a series -- Performing moving-window operations -- Executing random sampling of data -- Summary -- Chapter 9: Accessing Data -- Configuring pandas -- Working with CSV and text/tabular format data -- Examining the sample CSV data set -- Reading a CSV file into a DataFrame.…”
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Understanding least squares estimation and geomatics data analysis / by Ogundare, John Olusegun, ProQuest Ebook Subscriptions

Table of Contents: “…Intro; Title Page; Copyright Page; Contents; Preface; Acknowledgments; About the Author; About the Companion Website; Chapter 1 Introduction; 1.1 Observables and Observations; 1.2 Significant Digits of Observations; 1.3 Concepts of Observation Model; 1.4 Concepts of Stochastic Model; 1.4.1 Random Error Properties of Observations; 1.4.2 Standard Deviation of Observations; 1.4.3 Mean of Weighted Observations; 1.4.4 Precision of Observations; 1.4.5 Accuracy of Observations; 1.5 Needs for Adjustment; 1.6 Introductory Matrices; 1.6.1 Sums and Products of Matrices; 1.6.2 Vector Representation.…”
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Project management analytics : a data-driven approach to making rational and effective project decisions / by Singh, Harjit, Safari Books Online

Table of Contents: “…-- Project versus Program versus Portfolio -- Project Management Office (PMO) -- Project Life Cycle (PLC) -- Project Management Life Cycle (PMLC) -- A Process within the PMLC -- Work Breakdown Structure (WBS) -- Systems Development Life Cycle (SDLC) -- Summary -- Key Terms -- Case Study: Life Cycle of a Construction Project -- Case Study Questions -- Chapter Review and Discussion Questions -- Bibliography -- ch. 4 Chapter Statistical Fundamentals I: Basics and Probability Distributions -- Statistics Basics -- Probability Distribution -- Mean, Variance, and Standard Deviation of a Binomial Distribution -- Poisson Distribution -- Normal Distribution -- Confidence Intervals -- Summary -- Key Terms -- Solutions to Example Problems -- Chapter Review and Discussion Questions -- Bibliography -- ch. 5 Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression -- What Is a Hypothesis?…”
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Intelligent data analysis : from data gathering to data comprehension / by Safari Books Online.

Table of Contents: “…3.1 Introduction -- 3.2 Probability -- 3.2.1 Definitions -- 3.2.1.1 Random Experiments -- 3.2.1.2 Probability -- 3.2.1.3 Probability Axioms -- 3.2.1.4 Conditional Probability -- 3.2.1.5 Independence -- 3.2.1.6 Random Variable -- 3.2.1.7 Probability Distribution -- 3.2.1.8 Expectation -- 3.2.1.9 Variance and Standard Deviation -- 3.2.2 Bayes' Rule -- 3.3 Descriptive Statistics -- 3.3.1 Picture Representation -- 3.3.1.1 Frequency Distribution -- 3.3.1.2 Simple Frequency Distribution -- 3.3.1.3 Grouped Frequency Distribution -- 3.3.1.4 Stem and Leaf Display -- 3.3.1.5 Histogram and Bar Chart…”
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Timing performance of nanometer digital circuits under process variations / by Champac, Victor, Gervacio, Jose Garcia, SpringerLink (Online service)

Table of Contents: “…3.5 Parameter Modeling3.6 Spatial Correlation Modeling; 3.6.1 Exponential Model; 3.6.1.1 Example; 3.6.2 Grid Model; 3.7 Summary; References; 4 Gate Delay Under Process Variations; 4.1 Mathematical Formulation of the Statistical Delay of a Logic Gate; 4.1.1 Mean Delay of a Gate; 4.1.2 Variance of the Delay of a Gate; 4.2 Delay of Logic Gates Under Process Variations; 4.3 Computing Delay Variance of an Inverter; 4.3.1 Analytical Delay Model; 4.3.2 Sensitivity Delay Model; 4.3.3 Example of Computing Delay Standard Deviation of an Inverter; 4.4 Computing Delay Variance of a Nand Gate.…”
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Condition Monitoring with Vibration Signals : Compressive Sampling and Learning Algorithms for Rotating Machines. by Nandi, Asoke K., ProQuest Ebook Subscriptions

Table of Contents: “…3.2.4 Peak-to-Peak Amplitude3.2.5 Crest Factor (CF); 3.2.6 Variance and Standard Deviation; 3.2.7 Standard Error; 3.2.8 Zero Crossing; 3.2.9 Wavelength; 3.2.10 Willison Amplitude; 3.2.11 Slope Sign Change; 3.2.12 Impulse Factor; 3.2.13 Margin Factor; 3.2.14 Shape Factor; 3.2.15 Clearance Factor; 3.2.16 Skewness; 3.2.17 Kurtosis; 3.2.18 Higher-Order Cumulants (HOCs); 3.2.19 Histograms; 3.2.20 Normal/Weibull Negative Log-Likelihood Value; 3.2.21 Entropy; 3.3 Time Synchronous Averaging; 3.3.1 TSA Signals; 3.3.2 Residual Signal (RES); 3.3.2.1 NA4; 3.3.2.2 NA4*; 3.3.3 Difference Signal (DIFS)…”
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Data Wrangling with JavaScript / by Davis, Ashley, Safari, an O'Reilly Media Company, Safari Books Online

Table of Contents: “…8.7.7 Filtering using queries -- 8.7.8 Discarding data with projection -- 8.7.9 Sorting large data sets -- 8.8 Achieving better data throughput -- 8.8.1 Optimize your code -- 8.8.2 Optimize your algorithm -- 8.8.3 Processing data in parallel -- Summary -- Chapter 9: Practical data analysis -- 9.1 Expanding your toolkit -- 9.2 Analyzing the weather data -- 9.3 Getting the code and data -- 9.4 Basic data summarization -- 9.4.1 Sum -- 9.4.2 Average -- 9.4.3 Standard deviation -- 9.5 Group and summarize -- 9.6 The frequency distribution of temperatures -- 9.7 Time series -- 9.7.1 Yearly average temperature -- 9.7.2 Rolling average -- 9.7.3 Rolling standard deviation -- 9.7.4 Linear regression -- 9.7.5 Comparing time series -- 9.7.6 Stacking time series operations -- 9.8 Understanding relationships -- 9.8.1 Detecting correlation with a scatter plot -- 9.8.2 Types of correlation -- 9.8.3 Determining the strength of the correlation -- 9.8.4 Computing the correlation coefficient -- Summary -- Chapter 10: Browser-based visualization -- 10.1 Expanding your toolkit -- 10.2 Getting the code and data -- 10.3 Choosing a chart type -- 10.4 Line chart for New York City temperature -- 10.4.1 The most basic C3 line chart -- 10.4.2 Adding real data -- 10.4.3 Parsing the static CSV file -- 10.4.4 Adding years as the X axis -- 10.4.5 Creating a custom Node.js web server -- 10.4.6 Adding another series to the chart -- 10.4.7 Adding a second Y axis to the chart -- 10.4.8 Rendering a time series chart -- 10.5 Other chart types with C3 -- 10.5.1 Bar chart -- 10.5.2 Horizontal bar chart -- 10.5.3 Pie chart -- 10.5.4 Stacked bar chart -- 10.5.5 Scatter plot chart -- 10.6 Improving the look of our charts -- 10.7 Moving forward with your own projects -- Summary -- Chapter 11: Server-side visualization -- 11.1 Expanding your toolkit -- 11.2 Getting the code and data.…”
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Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks / by Mund, Sumit, Safari Books Online

Table of Contents: “…Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introduction -- Introduction to predictive analytics -- Problem definition and scoping -- Data collection -- Data exploration and preparation -- Model development -- Model deployment -- Machine learning -- Kinds of machine learning problems -- Classification -- Regression -- Clustering -- Common machine learning techniques/algorithms -- Linear regression -- Logistic regression -- Decision tree-based ensemble models -- Neural networks and deep learning -- Introduction to Azure Machine Learning -- ML Studio -- Summary -- Chapter 2: ML Studio Inside Out -- Introduction to ML Studio -- Getting started with Microsoft Azure -- Microsoft account and subscription -- Creating and managing ML workspaces -- Inside ML Studio -- Experiments -- Creating and editing an experiment -- Running an experiment -- Creating and running an experiment -- do it yourself -- Workspace as a collaborative environment -- Summary -- Chapter 3: Data Exploration and Visualization -- The basic concepts -- The mean -- The median -- Standard deviation and variance -- Understanding a histogram -- The box and whiskers plot -- The outliers -- A scatter plot -- Data exploration in ML Studio -- Visualizing an automobile price dataset -- A histogram -- The box and whiskers plot -- Comparing features -- A snapshot -- Do it yourself -- Summary -- Chapter 4: Getting Data in and out of ML Studio -- Getting data in ML Studio -- Uploading data from a PC -- The Enter Data module -- The Data Reader module -- Getting data from the Web -- Getting data from Azure -- Data format conversion -- Getting data from ML Studio -- Saving dataset in a PC -- Saving results in ML Studio -- The Writer module -- Summary -- Chapter 5: Data Preparation.…”
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Measure, integration & real analysis / by Axler, Sheldon Jay

Table of Contents:

Mathematical and statistical methods for actuarial sciences and finance / by SpringerLink (Online service)

Table of Contents: “…Magatti: The estimation of standard deviation of premium risk under solvency 2. -- 15 M. …”
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Railway track engineering / by Mundrey, J. S., AccessEngineering

Table of Contents: “…Track tolerances, track inspection and track recordings -- Track tolerances -- Service tolerances laid down in indian railways -- Track inspections -- Track recordings -- Track recording cars -- Oscillograph car -- Portable oscillations monitoring system oms-2000 -- Correlation between amsler track recording car and oscillograph car results -- Standard deviation as a measure of track irregularity -- Microprocessor based track monitoring system -- Track geometry index (tgi) for standard deviation based assessment of track geometry -- Plasser and theurer's modern track recording cars -- 18. …”
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Hedge fund modelling and analysis using Excel and VBA / by Darbyshire, Paul, Safari Books Online

Table of Contents: “…Moments of a Distribution -- 4.7.1. Mean and Standard Deviation -- 4.7.2. Skewness -- 4.7.3. Excess Kurtosis -- 4.7.4. …”
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Large-scale scientific computing : 13th International Conference, LSSC 2021, Sozopol, Bulgaria, June 7-11, 2021, Revised selected papers / by LSSC (Conference) Sozopol, Bulgaria), SpringerLink (Online service)

Table of Contents: “…Invited Papers -- Random-walk Based Approximate k-Nearest Neighbors Algorithm for Diffusion State Distance -- Model Reduction for Large Scale Systems -- II Fractional Di_usion Problems: Numerical Methods, Algorithms and Applications -- Constructions of Second Order Approximations of the Caputo Fractional Derivative -- Parameter Identification Approach for a Fractional Dynamics Model of Honeybee Population -- A Newton's Method for Best Uniform Polynomial Approximation -- Reduced Sum Implementation of the BURA Method for Spectral Fractional Diffusion Problems -- First-order Reaction-diffusion System with Space-fractional Diffusion in an Unbounded medium -- Performance Study of Hierarchical Semi-Separable Compression Solver for Parabolic Problems with Space-fractional Diffusion -- Numerical Solution of Non-Stationary Problems with a Rational Approximation for Fractional Powers of the Operator -- Large-Scale Models: Numerical Methods, Parallel Computations and Applications -- An Exact Schur Complement Method for Time-harmonic Optimal Control Problems -- On the Consistency Order of Runge-Kutta Methods Combined with Active Richardson Extrapolation -- Study the Recurrence of the Dominant Pollutants in the Formation of AQI Status Over the City of Sofia for the Period 2013-2020 -- One Solution of Task with Internal Flow in Non-uniform Fluid Using CABARET Method -- Behavior and Scalability of the Regional Climate Model RegCM4 on High Performance Computing Platforms -- Quantum Effects on 1/2[111] Edge Dislocation Motion in Hydrogen-Charged Fe from Ring-Polymer Molecular Dynamics -- Degeneracy of Tetrahedral Partitions Produced by Randomly Generated Red Refinements -- Effluent Recirculation for Contaminant Removal in Constructed Wetlands under Uncertainty: A Stochastic Numerical Approach Based on Monte Carlo Methodology -- Sensitivity Study of Large-Scale Air Pollution Model Based on Modifications of the Latin Hypercube Sampling Method -- Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data -- Using the Cauchy Criterion and the Standard Deviation to Evaluate the Sustainability of Climate Simulations -- Multidimensional Sensitivity Analysis of an Air Pollution Model Based on Modifications of the van der Corput Sequence -- Running an Atmospheric Chemistry Scheme from a Large Air Pollution Model by Using Advanced Versions of the Richardson Extrapolation -- Application of Metaheuristics to Large-Scale Problems -- New Clustering Techniques of Node Embeddings Based on Metaheuristic Optimization Algorithms -- A Comparison of Machine Learning Methods for Forecasting Dow Jones Stock Index -- Optimal Knockout Tournaments: Definition and Computation -- Risk Registry Platform for Optimizations in Cases of CBRN and Critical Infrastructure Attacks -- Influence of the ACO Evaporation Parameter for Unstructured Workforce Planning Problem -- binMeta: a New Java Package for Meta-heuristic Searches -- Synergy between Convergence and Divergence - Review of Concepts and Methods -- Advanced Stochastic Approaches Based on Optimization of Lattice Sequences for Large-Scale Finance Problems -- Intuitionistic Fuzzy Approach for Outsourcing Provider Selection in a Refinery -- Quantitative Relationship Between Particulate Matter and Morbidity -- Advanced Discretizations and Solvers for Coupled Systems of Partial Differential Equations -- Decoupling Methods for Systems of Parabolic Equations -- Optimal Control of ODEs, PDEs and Applications -- Random Lifting of Set-valued Maps -- Höolder Regularity in Bang-Bang Type Affine Optimal Control Problems -- Simultaneous Space-time Finite Element Methods for Parabolic Optimal Control Problems -- A New Algorithm for the LQR Problem with Partially Unknown Dynamics -- Tensor and Matrix Factorization for Big-Data Analysis -- Solving Systems of Polynomial Equations - a Tensor Approach -- Nonnegative Tensor-train Low-rank Approximations of the Smoluchowski Coagulation Equation -- Boolean Hierarchical Tucker Networks on Quantum Annealers -- Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization -- Machine Learning and Model Order Reduction for Large Scale Predictive Simulations -- Deep Neural Networks and Adaptive Quadrature for Solving Variational Problems -- A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows -- A Multiscale Fatigue Model for the Degradation of Fiber-reinforced Materials -- A Classification Algorithm for Anomaly Detection in Terahertz Tomography -- Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue -- Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows -- HPC and Big Data: Algorithms and Applications -- On the Use of Low-discrepancy Sequences in the Training of Neural Networks -- A PGAS-based Implementation for the Parallel Minimum Spanning Tree Algorithm -- Comparison of Di_erent Methods for Multiple Imputation by Chain Equation -- Monte Carlo Method for Estimating Eigenvalues Using Error Balancing -- Multi-Lingual Emotion Classification Using Convolutional Neural Networks -- On Parallel MLMC for Stationary Single Phase Flow Problem -- Numerical Parameter Estimates of Beta-uniform Mixture Models -- Large-Scale Computer Simulation of the Performance of the Generalized Nets Model of the LPF-algorithm -- Contributed Papers -- A New Error Estimate for a Primal-Dual Crank-Nicolson Mixed Finite Element Using Lowest Degree Raviart-Thomas Spaces for Parabolic Equations -- A Finite Volume Scheme for a Wave Equation with Several Time Independent Delays -- Recovering the Time-Dependent Volatility in Jump-Diffusion Models from Nonlocal Price Observations -- On the Solution of Contact Problems with Tresca Friction by the Semismooth* Newton Method -- Fitted Finite Volume Method for Unsaturated Flow Parabolic Problems with Space Degeneration -- Minimization of p-Laplacian via the Finite Element Method in MATLAB -- Quality Optimization of Seismic-derived Surface Meshes of Geological Bodies.…”
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Medical statistics from scratch : an introduction for health professionals / by Bowers, David, 1938-, ProQuest Ebook Subscriptions

Table of Contents: “…Measures of spread -- Numbers R us -- (again) -- Preamble -- The range -- The interquartile range (IQR) -- Estimating the median and interquartile range from the cumulative frequency curve -- The boxplot (also known as the box and whisker plot) -- Standard deviation -- Standard deviation and the Normal distribution -- Testing for Normality -- Using SPSS -- Using Minitab -- Transforming data -- 7. …”
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