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.…”
Get full text

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?…”
Get full text

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…”
Get full text

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.…”
Get full text

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)…”
Get full text

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.…”
Get full text

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.…”
Get full text

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. …”
Get full text
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

Understanding research methods and statistics in psychology / by Gavin, Helen, Sage Knowledge

Table of Contents: “…-- Dispersion -- The sample variance and the standard deviation -- Standard error of the mean -- The z-score -- Disruptions to normality -- skew and kurtosis -- Tabular presentation -- Summary -- 5. …”
Get full text

Pejoration / by PALCI EBSCO books

Table of Contents: “…Acoustic analyses -- 6.1 Distribution of fundamental frequency -- 6.2 Mean fundamental frequency, standard deviation and range -- 6.3 Characteristics of the fundamental frequency contours -- 6.4 Graphical representation of the fundamental frequency contours -- 6.5 Steepness of contours -- 6.6 Realization of stress -- 6.7 Analysis of duration and speech rate -- 7. …”
Get full text

Road traffic : safety, modeling & impacts / by PALCI EBSCO books

Table of Contents: “…Driving Behavior -- Steering Wheel Angle Behavior -- Standard Deviation of Lateral Acceleration -- 3.3. Arithmetic Task Performance -- 3.4. …”
Get full text

Laboratory experiments in physics for modern astronomy with comprehensive development of the physical principles / by Golden, Leslie, SpringerLink (Online service)

Table of Contents: Get full text

Spectrum and network measurements / by Witte, Robert A., PALCI EBSCO books

Table of Contents: “…Mean, Variance, and Standard Deviation -- 8.3. Power Spectral Density -- 8.4. …”
Get full text

Mathematical understanding for secondary teaching : a framework and classroom-based situations / by PALCI EBSCO books

Table of Contents: “…Kathleen Heid, Laura Singletary, and Sarah Donaldson -- Representing Standard Deviation: Situation 39 From the MACMTL-CPTM Situations Project / Rose Mary Zbiek, M. …”
Get full text

What is Variation? by JOVE Films

Get full text

The mathematical biology of diatoms / by Safari Books Online

Table of Contents: “…8.4 Modeling Interacting Oscillators of a Bacillaria Colony -- 8.4.1 Observation of the Movement Activity at Uncovered Rhaphes -- 8.4.2 Interaction of Neighboring Diatoms -- 8.4.3 Coupled Oscillators -- 8.5 Kuramoto Model -- 8.5.1 Adaptation of the Kuramoto Model for a Bacillaria Colony -- 8.5.2 Analyses and Approximations -- 8.5.3 Critical Coupling -- 8.5.3.1 Uncoupled Oscillators -- 8.5.3.2 Two Oscillators -- 8.5.3.3 Chains without Retardation -- 8.5.3.4 Identical Oscillator Frequencies and Sufficiently Small Delay -- 8.5.3.5 Remarks on the General Case -- 8.5.4 Statistical Considerations and Monte Carlo Simulations -- 8.5.4.1 Expected Value and Standard Deviation -- 8.5.4.2 Distribution of Critical Coupling -- 8.5.5 Simulation of Non-Synchronous States -- 8.5.5.1 Numerical Integration -- 8.5.5.2 Visualization of the Transient -- 8.5.5.3 Discrete Fourier Transform -- 8.5.6 Coupling to a Periodic Light Source -- 8.6 Discussion -- Acknowledgment -- References -- Chapter 9 The Psychophysical World of the Motile Diatom Bacillaria paradoxa -- Abbreviations -- 9.1 Introduction -- 9.1.1 Aneural Architecture of Bacillaria -- 9.1.2 Aneural Cognition in a Broader Context -- 9.1.3 Psychophysics as Diatom Information Processing -- 9.1.4 Information Processing and Aneural Cognition -- 9.1.5 Hebbian Intelligence and Predictive Processing -- 9.2 Measurement Techniques -- 9.2.1 Weber-Fechner Law -- 9.2.2 Connectionist Network -- 9.2.3 Algorithmic Information -- 9.2.4 Collective Pattern Generator -- 9.2.5 Dynamical States of the CoPG -- 9.3 CPGs vs. …”
Get full text