Development of a technique for estimating noise covariances using multiple observers / by Bundick, W. Thomas, Langley Research Center

Subjects: Get full text

Excel 2010 for engineering statistics : a guide to solving practical problems / by Quirk, Thomas Joseph, SpringerLink (Online service)

Table of Contents: “…Sample size, mean, standard deviation, standard error of the mean -- Random number generator -- Confidence interval about the mean using the TINV function and hypothesis testing -- One-group t-test for the mean -- Two-group t-test of the difference of the means for independent groups -- Correlation and simple linear regression -- Multiple correlation and multiple regression -- One-way analysis of variance (ANOVA).…”
Get full text

Excel 2019 for engineering statistics a guide to solving practical problems / by Quirk, Thomas J., SpringerLink (Online service)

Table of Contents: “…Intro -- Preface -- Acknowledgements -- Contents -- Chapter 1: Sample Size, Mean, Standard Deviation, and Standard Error of the Mean -- 1.1 Mean -- 1.2 Standard Deviation -- 1.3 Standard Error of the Mean -- 1.4 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean -- 1.4.1 Using the Fill/Series/Columns Commands -- 1.4.2 Changing the Width of a Column -- 1.4.3 Centering Information in a Range of Cells -- 1.4.4 Naming a Range of Cells -- 1.4.5 Finding the Sample Size Using the =COUNT Function -- 1.4.6 Finding the Mean Score Using the =AVERAGE Function…”
Get full text

Machine learning for email / by Conway, Drew, Safari Books Online

Table of Contents: “…-- Inferring the Types of Columns in Your Data -- Inferring Meaning -- Numeric Summaries -- Means, Medians, and Modes -- Quantiles -- Standard Deviations and Variances -- Exploratory Data Visualization -- Modes -- Skewness -- Thin Tails vs. …”
Get full text

Multiple criteria decision making : beyond the information age / by SpringerLink (Online service), International Conference on Multiple Criteria Decision Making

Table of Contents: “…Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function -- Selection of Emergency Assembly Points: A Case Study for the Expected Istanbul Earthquake -- Assessing Smartness and Urban Development of the European Cities: An Integrated Approach of Entropy and VIKOR -- An MCDM-Based Health Technology Assessment (HTA) Study for Evaluating Kidney Stone Treatment Alternatives -- Geographic Distribution of the Efficiency of Childbirth Services in Turkey -- Multicriteria Methods and the Hydropower Plants Planning in Brazil -- Regional Examination of Energy Investments in Turkey Using an Intuitionistic Fuzzy Method -- Small Series Fashion Supplier Selection Using MCDM Methods -- Enhanced Performance Assessment of Airlines with Integrated Balanced Scorecard, Network-Based Superefficiency DEA and PCA Methods -- The Effects of Country Characteristics on Entrepreneurial Activities -- A Geometric Standard Deviation Based Soft Consensus Model in Analytic Hierarchy Process -- Coherency: From Outlier Detection to Reducing Comparisons in the ANP Supermatrix -- Usage of Entropy-Based Objective Weighting in Neutrosophic Multiple Attribute Decision-Making -- Implementation of Cumulative Belief Degree Approach to Group Decision-Making Problems Under Hesitancy -- A Literature Survey on Project Portfolio Selection Problem.…”
Get full text

Principles of GNSS, inertial, and multi-sensor integrated navigation systems / by Groves, Paul D. (Paul David), PALCI EBSCO books

Table of Contents: “…-- Inertial Navigation -- Radio and Satellite Navigation -- Feature Matching -- The Complete Navigation System -- Navigation Mathematics -- Coordinate Frames, Kenematics, and the Earth -- Coordinate Frames -- Kinematics -- Earth Surface and Gravity Models -- Frame Transformations -- The Kalman Filter -- Introduction -- Algorithms and Models -- Implementation Issues -- Extensions to the Kalman Filter -- Navigation Systems -- Inertial Sensors -- Acceleromoters -- Gyroscopes -- Inertial Measurement Units -- Error Characteristics -- Inertial Navigation -- Inertial-Frame Navigation Equations -- Earth-Frame Navigation Equations -- Local-Navigation-Frame Navigation Equations -- Navigations Equations Precision -- initialization and Alignment -- INS Error Propagation -- Platform INS -- Horizontal-Plane Inertial Navigation -- Satellite Navigation Systems -- Fundamentals of Satellite Navigation -- Global Positioning System -- GLONASS -- Galileo -- Regional Navigation Systems -- GNSS Interoperability -- Satellite Navigation Processing, Errors, and Geometry -- Satellite Navigation Geometry -- Receiver Hardware and Antenna -- Ranging Processor -- Range Error Sources -- Navigation Processor -- Advanced Satellite Navigation -- Differential GNSS -- Carrier-Phase Positioning and Attitude -- Poor Signal-to-Noise Environments -- Multipath Mitigation -- Signal Monitoring -- Semi-Codeless Tracking -- Terrestrial Radio Navigation -- Point-Source Systems -- Loran -- Instrument Landing System -- Urban and Indoor Positioning -- Relative Navigation -- Tracking -- Sonar Transponders -- Dead Reckoning, attitude, and Height Measurement -- Height and Depth Measurement -- Odometers -- Pedestrian Dead Reckoning -- Doppler Radar and Sonar -- Other Dead-Reckoning Techniques -- Feature Matching -- Terrain-Referenced NAvigation -- Image Matching -- Map Matching -- Other Feature-Matching Techniques -- Integrated Navigation -- INS/GNSS Integration -- Integration Architectures -- System Model and State Selection -- Measurement Models -- Advanced INS/GNSS Integration -- INS Alignment and Zero Velocity Updates -- Transfer Alignment -- Quasi-Stationary Alignment with Unknown Heading -- Quasi-Stationary Fine Alignment and Zero Velocity Updates -- Multisensor Integrated Navigation -- Integration Architectures -- Terestrial Radio Navigation -- Dead Reckoning, Attitude, and Height Measurement -- Feature Mapping -- Fault Detection and Integrity Monitoring -- Failure Modes -- Range Checks -- Kalman Filter Measurement Innovations -- Direct Consistency Checks -- Certified Integrity Monitoring -- Vectors and Matrices -- Introduction to Vectors -- Introduction to Matrices -- Special Matrix Types -- Matrix Inversion -- Calculus -- Statistical Measures -- Mean, Variance, and Standard Deviation -- Probability Density Function -- Gaussian Distribution -- Chi-Square Distribution.…”
Get full text

Nanofibers : fabrication, performance, and applications / by ProQuest Ebook Subscriptions

Table of Contents: “…""RESPONSE SURFACES FOR MEAN FIBER DIAMETER""""RESPONSE SURFACES FOR STANDARD DEVIATION OF FIBER DIAMETER ""; ""CONCLUSION ""; ""APPENDIX ""; ""REFERENCES ""; ""CARBON NANO-FIBERS AND THEIR APPLICATIONS: DERIVED FROM ELECTROSPINNING AND VAPOR GROWN PROCESSES ""; ""ABBREVIATIONS ""; ""1. …”
Get full text

Digital Signal Processing : a Practical Guide for Engineers and Scientists. by Smith, Steven W., ProQuest Ebook Subscriptions

Table of Contents: “…Statistics, Probability and Noise; Signal and Graph Terminology; Mean and Standard Deviation; Signal vs. Underlying Process; The Histogram, Pmf and Pdf; The Normal Distribution; Digital Noise Generation; Precision and Accuracy; Chapter 3. …”
Get full text

Normalization of multidimensional data for multi-criteria decision making problems : inversion, displacement, asymmetry / by Mukhametzyanov, Irik Z., SpringerLink (Online service)

Table of Contents: “…2.3.3 Objective Weighting Methods: Entropy, CRITIC, SD -- Entropy Weighting Method (EWM) [26, 27, 37] -- CRiteria Importance Through Inter-criteria Correlation (CRITIC) [28] -- Standard Deviation (SD) -- 2.4 Aggregation of the Attributes: An Overview of Some Methods -- 2.4.1 Value Measurement Methods -- Simple Additive Weighting (SAW) or Weighted Sum Method (WSM) [1] -- Weighted Product Method (WPM) [39] -- Weighted Aggregated Sum Product Assessment (WASPAS) [39] -- Multi-Attributive Border Approximation Area Comparison (MABAC) [45] -- Complex Proportional Assessment (COPRAS) Method [46]…”
Get full text

Introduction to probability and statistics for engineers and scientists / by Ross, Sheldon M., PALCI EBSCO books

Table of Contents: “…Cover -- Contents -- Preface -- CHAPTER 1 INTRODUCTION TO STATISTICS -- 1.1 INTRODUCTION -- 1.2 DATA COLLECTION AND DESCRIPTIVE STATISTICS -- 1.3 INFERENTIAL STATISTICS AND PROBABILITY MODELS -- 1.4 POPULATIONS AND SAMPLES -- 1.5 A BRIEF HISTORY OF STATISTICS -- CHAPTER 2 DESCRIPTIVE STATISTICS -- 2.1 INTRODUCTION -- 2.2 DESCRIBING DATA SETS -- 2.2.1 Frequency Tables and Graphs -- 2.2.2 Relative Frequency Tables and Graphs -- 2.2.3 Grouped Data, Histograms, Ogives, and Stem and Leaf Plots -- 2.3 SUMMARIZING DATA SETS -- 2.3.1 Sample Mean, Sample Median, and Sample Mode -- 2.3.2 Sample Variance and Sample Standard Deviation -- 2.3.3 Sample Percentiles and Box Plots -- 2.4 CHEBYSHEV'S INEQUALITY -- 2.5 NORMAL DATA SETS -- 2.6 PAIRED DATA SETS AND THE SAMPLE CORRELATION COEFFICIENT -- CHAPTER 3 ELEMENTS OF PROBABILITY -- 3.1 INTRODUCTION -- 3.2 SAMPLE SPACE AND EVENTS -- 3.3 VENN DIAGRAMS AND THE ALGEBRA OF EVENTS -- 3.4 AXIOMS OF PROBABILITY -- 3.5 SAMPLE SPACES HAVING EQUALLY LIKELY OUTCOMES -- 3.6 CONDITIONAL PROBABILITY -- 3.7 BAYES' FORMULA -- 3.8 INDEPENDENT EVENTS -- CHAPTER 4 RANDOM VARIABLES AND EXPECTATION -- 4.1 RANDOM VARIABLES -- 4.2 TYPES OF RANDOM VARIABLES -- 4.3 JOINTLY DISTRIBUTED RANDOM VARIABLES -- 4.3.1 Independent Random Variables -- *4.3.2 Conditional Distributions -- 4.4 EXPECTATION -- 4.5 PROPERTIES OF THE EXPECTED VALUE -- 4.5.1 Expected Value of Sums of Random Variables -- 4.6 VARIANCE -- 4.7 COVARIANCE AND VARIANCE OF SUMS OF RANDOM VARIABLES -- 4.8 MOMENT GENERATING FUNCTIONS -- 4.9 CHEBYSHEV'S INEQUALITY AND THE WEAK LAW OF LARGE NUMBERS -- CHAPTER 5 SPECIAL RANDOM VARIABLES -- 5.1 THE BERNOULLI AND BINOMIAL RANDOM VARIABLES -- 5.1.1 Computing the Binomial Distribution Function -- 5.2 THE POISSON RANDOM VARIABLE -- 5.2.1 Computing the Poisson Distribution Function -- 5.3 THE HYPERGEOMETRIC RANDOM VARIABLE -- 5.4 THE UNIFORM RANDOM VARIABLE -- 5.5 NORMAL RANDOM VARIABLES -- 5.6 EXPONENTIAL RANDOM VARIABLES -- *5.6.1 The Poisson Process -- *5.7 THE GAMMA DISTRIBUTION -- 5.8 DISTRIBUTIONS ARISING FROM THE NORMAL -- 5.8.1 The Chi-Square Distribution -- 5.8.2 The t-Distribution -- 5.8.3 The F-Distribution -- *5.9 THE LOGISTICS DISTRIBUTION -- CHAPTER 6 DISTRIBUTIONS OF SAMPLING STATISTICS -- 6.1 INTRODUCTION -- 6.2 THE SAMPLE MEAN -- 6.3 THE CENTRAL LIMIT THEOREM -- 6.3.1 Approximate Distribution of the Sample Mean -- 6.3.2 How Large a Sample Is Needed? …”
Get full text

Probabilistic design for optimization and robustness for engineers / by Dodson, Bryan, 1962-, Safari Books Online

Table of Contents: “…5.3 Limitations of first-order Taylor series approximation for variance5.4 Effect of non-normal input distributions; 5.5 Nonconstant input standard deviation; 5.6 Summary; Exercises; 6 Desirability; 6.1 Introduction; 6.2 Requirements and scorecards; 6.2.1 Types of requirements; 6.2.2 Design scorecard; 6.3 Desirability-single requirement; 6.3.1 Desirability-one-sided limit; 6.3.2 Desirability-two-sided limit; 6.3.3 Desirability-nonlinear function; 6.4 Desirability-multiple requirements; 6.4.1 Maxi-min total desirability index; 6.5 Desirability-accounting for variation.…”
Get full text

Probability and random variables for electrical engineering : probability : measurement of uncertainty / by Catak, Muammer, Allahviranloo, Tofigh, Pedrycz, Witold, 1953-, SpringerLink (Online service)

Table of Contents: “…4.5.5 Sum of Two Statistically Independent Continuous Random Variables, Z=X+Y -- 4.5.6 Sum of Two Statistically Independent Discrete Random Variables, Z=X+Y -- 4.5.7 Z=XY -- 4.5.8 Z=XY -- 4.5.9 Central Limit Theorem -- 4.6 Problems -- 5 Statistical Analysis of Random Variables -- 5.1 Statistical Analysis of One Random Variable -- 5.1.1 Expected Value and Mean -- 5.1.2 Variance and Standard Deviation -- 5.2 Moment Generating Functions -- 5.2.1 Maclaurin Series -- 5.2.2 Characteristic Function -- 5.3 Statistical Analysis of Multiple Random Variables -- 5.3.1 Normalized Joint Moments…”
Get full text

Impact Analysis of Total Productive Maintenance : Critical Success Factors and Benefits / by Díaz-Reza, José Roberto, SpringerLink (Online service)

Table of Contents: “…TPM Benefits; 6.4.4 Rating Scale; 6.5 Questionnaire Application; 6.5.1 Sample; 6.5.2 Gathering Data; 6.6 Data Entry and Analysis; 6.7 Data Depuration; 6.7.1 Identification of Missing Values; 6.7.2 Identification of Outliers; 6.7.3 Standard Deviation Analysis; 6.7.4 Normal Distribution; 6.7.5 Homoscedastic Analysis; 6.7.6 Collinearity Analysis; 6.8 Descriptive Analysis.…”
Get full text

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.…”
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

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

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

Failure prevention of plant and machinery / by Hattangadi, A. A., AccessEngineering

Table of Contents: “…Sleeve bearing failures -- Introduction -- Common failure modes -- Failures due to external heating -- Failures due to thefts of lubricants -- Failures due to leakage of oil from reservoirs -- Failures due to excessive stress -- Failures of railway axle boxes -- Failures due to excessive ambient temperatures -- Do's and don'ts for preventing sleeve bearing failures -- Appendix 11.1 calculation of standard deviation -- 12. Failures of white metal bearings of electric locomotives -- Introduction -- Failure modes -- Failure investigation -- Remedial measures -- Conclusion -- 13. …”
Get full text

Non-destructive in situ strength assessment of concrete : practical application of the RILEM TC 249-ISC recommendations / by RILEM Technical Committee 249-ISC, SpringerLink (Online service)

Get full text