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A student's guide to data and error analysis /
Table of Contents: “…Combining uncertainties -- Systematic deviations due to random errors -- Characteristic function -- From binomial to normal distributions -- Central limit theorem -- Estimation of th varience -- Standard deviation of the mean -- Weight factors when variances are not equal -- Least squares fitting -- Python codes -- Scientific data.…”
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2
A panorama of statistics : perspectives, puzzles and paradoxes in statistics /
Table of Contents: “…-- How statistics differs from mathematics -- Statistical literacy- essential in the 21st century -- Statistical inquiry on the web -- Part II Statistical description -- Trustworthy statistics are accurate, meaningful and relevant -- Let's hear it for the standard deviation! -- Index numbers- time travel for averages -- The beguiling ways of bad statistics I -- The beguiling ways of bad statistics II -- Part III Preliminaries to inference -- Puzzles and paradoxes in probability -- Some paradoxes of randomness -- Hidden risks for gamblers -- Models in statistics -- The normal distribution: history, computation and curiosities -- Part IV Statistical inference -- The pillars of applied statistics- estimation -- The pillars of applied statistics- hypothesis testing -- 'Data snooping' and the significance level in multiple testing -- Francis Galton and the birth of regression -- Experimental design- piercing the veil of random variation -- In praise of Bayes -- Part V Some statistical byways -- Quality in statistics -- History of ideas: statistical personalities and the personalities of statisticians -- Statistical eponymy -- Statistical 'laws' -- Statistical artefacts -- Part VI Answers -- Answers to the chapter questions.…”
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3
U Can : statistics for dummies /
Table of Contents: “…Grabbing Some Basic Statistical JargonData; Data set; Variable; Population; Statistic; Parameter; Mean (Average); Median; Standard deviation; Percentile; Standard score; Distribution and normal distribution; Central Limit Theorem; z-values; Margin of error; Confidence interval; Hypothesis testing; p-values; Statistical significance; Correlation, regression, and two-way tables; Drawing Credible Conclusions; Reeling in overstated results; Questioning claims of cause and effect; Becoming a Sleuth, Not a Skeptic; Part II Number-Crunching Basics; Chapter 4 Crunching Categorical Data.…”
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4
Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics.
Table of Contents: “…Reading Data Files; Reading a CSV File; Writing a CSV File; Reading an Excel File; Writing an Excel File; Reading an SPSS File; Writing an SPSS File; Reading a JSON File; Basic Data Processing; Selecting Data; Sorting; Filtering; Removing Missing Values; Removing Duplicates; Some Basic Statistics Terms; Types of Data; Mode, Median, Mean; Mode; Median; Mean; Interquartile Range, Variance, Standard Deviation; Range; Interquartile Range; Variance; Standard Deviation; Normal Distribution; Modality; Skewness; Binomial Distribution; The summary() and str() Functions…”
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Machine learning with R : discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R /
Table of Contents: “…Measuring spread -- variance and standard deviationExploring categorical variables; Measuring the central tendency -- the mode; Exploring relationships between variables; Visualizing relationships -- scatterplots; Examining relationships -- two-way cross-tabulations; Summary; Chapter 3: Lazy Learning -- Classification Using Nearest Neighbors; Understanding nearest neighbor classification; The k-NN algorithm; Measuring similarity with distance; Choosing an appropriate k; Preparing data for use with k-NN; Why is the k-NN algorithm lazy?…”
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6
Robust statistics : theory and methods (with R) /
Table of Contents: “…Cover; Title Page; Copyright; Contents; Preface; Preface to the First Edition; About the Companion Website; Chapter 1 Introduction; 1.1 Classical and robust approaches to statistics; 1.2 Mean and standard deviation; 1.3 The "three sigma edit" rule; 1.4 Linear regression; 1.4.1 Straight-line regression; 1.4.2 Multiple linear regression; 1.5 Correlation coefficients; 1.6 Other parametric models; 1.7 Problems; Chapter 2 Location and Scale; 2.1 The location model; 2.2 Formalizing departures from normality; 2.3 M-estimators of location; 2.3.1 Generalizing maximum likelihood…”
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7
Introduction to probability and statistics for engineers and scientists /
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? …”
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8
Probability and stochastic processes : a friendly introduction for electrical and computer engineers /
Table of Contents: “…1.7 MatlabProblems; Chapter 2: Sequential Experiments; 2.1 Tree Diagrams; 2.2 Counting Methods; 2.3 Independent Trials; 2.4 Reliability Analysis; 2.5 Matlab; Problems; Chapter 3: Discrete Random Variables; 3.1 Definitions; 3.2 Probability Mass Function; 3.3 Families of Discrete Random Variables; 3.4 Cumulative Distribution Function (CDF); 3.5 Averages and Expected Value; 3.6 Functions of a Random Variable; 3.7 Expected Value of a Derived Random Variable; 3.8 Variance and Standard Deviation; 3.9 Matlab; Problems; Chapter 4: Continuous Random Variables; 4.1 Continuous Sample Space.…”
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9
Risk Modelling in General Insurance : From Principles to Practice.
Table of Contents: “…3.6.2 Asymptotic approximations3.7 Statistics for compound distributions; 3.8 The individual risk model; 3.8.1 The mean and variance for the individual risk model; 3.8.2 The distribution function and moment generating function for the individual risk model; 3.8.3 Approximations for the individual risk model; Exercises; 4: Model based pricing -- setting premiums; 4.1 Premium calculation principles; 4.1.1 The expected value principle (EVP); 4.1.2 The standard deviation principle (SDP); 4.1.3 The variance principle (VP); 4.1.4 The quantile principle (QP); 4.1.5 The zero utility principle (ZUP).…”
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10
Understanding least squares estimation and geomatics data analysis /
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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11
Mathematical understanding for secondary teaching : a framework and classroom-based situations /
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. …”
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