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Advanced R statistical programming and data models : analysis, machine learning, and visualization / / by Matt Wiley, Joshua F. Wiley
| Advanced R statistical programming and data models : analysis, machine learning, and visualization / / by Matt Wiley, Joshua F. Wiley |
| Autore | Wiley Matt |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 |
| Descrizione fisica | 1 online resource (XX, 638 p. 207 illus., 127 illus. in color.) |
| Disciplina | 005.13 |
| Soggetto topico |
R (Llenguatge de programació)
Estadística matemàtica Programming languages (Electronic computers) Computer programming Mathematical statistics R (Computer program language) Programming Languages, Compilers, Interpreters Programming Techniques Probability and Statistics in Computer Science |
| ISBN |
9781523150311
1523150319 9781484228722 1484228723 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Univariate Data Visualization -- 2 Multivariate Data Visualization -- 3 Generalized Linear Models 1 -- 4 Generalized Linear Models 2 -- 5 Generalized Additive Models -- 6 Machine Learning: Introduction -- 7 Machine Learning: Unsupervised -- 8 Machine Learning: Supervised -- 9 Missing Data -- 10 Generalized Linear Mixed Models: Introduction -- 11 Generalized Linear Mixed Models: Linear -- 12 Generalized Linear Mixed Models: Advanced -- 13 Modeling IIV -- Bibliography. |
| Record Nr. | UNINA-9910338002703321 |
Wiley Matt
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied Calculus with R / / by Thomas J. Pfaff
| Applied Calculus with R / / by Thomas J. Pfaff |
| Autore | Pfaff Thomas J. |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (520 pages) |
| Disciplina | 515.0285 |
| Soggetto topico |
Mathematical statistics
Computer science - Mathematics Stochastic processes Calculus Mathematical Statistics Mathematical Applications in Computer Science Stochastic Calculus Càlcul Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-28571-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Brief Introduction to R -- Describing a Graph -- The Function Gallery -- I: Change and the Derivative -- How Fast is CO2 Increasing? -- The Idea of the Derivative -- Formulas Quantifying Change.-The Microscope Equation -- Successive Approximations to Estimate Derivatives -- The Derivative Graphically -- The Formal Derivative as a Limit -- Basic Derivative Rules -- Produce Rule -- Quotient Rule -- Chain Rule -- Derivatives with R -- End Behavior of a Function - L'Hospital's Rule -- II: Applications of the Derivative -- How Do We Know the Shape of a Function? -- Finding Extremes -- Optimization -- Derivatives of Functions of Two Variables -- Related Rates -- Surge Function -- Differential Equations - Preliminaries -- Differential Equations - Population Growth Models -- Differential Equations - Predator Prey -- Differential equations - SIR Model -- Project: The Gini Coefficient - Prelude to Section III -- III: Accumulation and the Integral -- Area Under Curves -- The Accumulation Function -- The Fundamental Theorem of Calculus -- Techniques of Integration - The u Substitution -- Techniques of Integration - Integration by Parts -- IV: Appendices - Algebra Review -- Algebra Review - Functions and Graphs -- Algebra Review - Adding and Multiplying Fractions -- Algebra Review - Exponents -- Algebra Review - Lines -- Algebra Review - Expanding, Factoring, and Roots -- Algebra Review - Function Composition -- Glossary -- Answers to Odd Problems -- R Code for Figures. |
| Record Nr. | UNINA-9910728948003321 |
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Pfaff Thomas J.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied Multivariate Statistics with R [[electronic resource] /] / by Daniel Zelterman
| Applied Multivariate Statistics with R [[electronic resource] /] / by Daniel Zelterman |
| Autore | Zelterman Daniel |
| Edizione | [2nd ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (469 pages) |
| Disciplina | 570.285 |
| Collana | Statistics for Biology and Health |
| Soggetto topico |
Biometry
Bioinformatics Epidemiology Biostatistics Anàlisi multivariable Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031130052
9783031130045 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. Elements of R -- Chapter 3. Graphical Displays -- Chapter 4. Basic Linear Algebra -- Chapter 5. The Univariate Normal Distribution -- Chapter 6. Bivariate Normal Distribution -- Chapter 7. Multivariate Normal Distribution -- Chapter 8. Factor Methods -- Chapter 9. Multivariate Linear Regression -- Chapter 10. Discrimination and Classification -- Chapter 11. Clustering Methods -- Chapter 12. Basic Models for Longitudinal Data -- Chapter 13. Time Series Models -- Chapter 14. Other Useful Methods. |
| Record Nr. | UNISA-996508571303316 |
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Zelterman Daniel
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Applied Multivariate Statistics with R / / by Daniel Zelterman
| Applied Multivariate Statistics with R / / by Daniel Zelterman |
| Autore | Zelterman Daniel |
| Edizione | [2nd ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (469 pages) |
| Disciplina |
570.285
519.53502855133 |
| Collana | Statistics for Biology and Health |
| Soggetto topico |
Biometry
Bioinformatics Epidemiology Biostatistics Anàlisi multivariable Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031130052
9783031130045 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. Elements of R -- Chapter 3. Graphical Displays -- Chapter 4. Basic Linear Algebra -- Chapter 5. The Univariate Normal Distribution -- Chapter 6. Bivariate Normal Distribution -- Chapter 7. Multivariate Normal Distribution -- Chapter 8. Factor Methods -- Chapter 9. Multivariate Linear Regression -- Chapter 10. Discrimination and Classification -- Chapter 11. Clustering Methods -- Chapter 12. Basic Models for Longitudinal Data -- Chapter 13. Time Series Models -- Chapter 14. Other Useful Methods. |
| Record Nr. | UNINA-9910645887003321 |
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Zelterman Daniel
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied Statistics : Methods Using R / / by Jürgen Hedderich, Lothar Sachs
| Applied Statistics : Methods Using R / / by Jürgen Hedderich, Lothar Sachs |
| Autore | Hedderich Jürgen |
| Edizione | [3rd ed. 2024.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (1056 pages) |
| Disciplina | 519.5 |
| Soggetto topico |
Sampling (Statistics)
Statistics Biometry R (Llenguatge de programació) Mostreig (Estadística) Biometria Estadística matemàtica Dades de recerca Methodology of Data Collection and Processing Applied Statistics Biostatistics Statistical Theory and Methods |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783662700747
3662700743 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Fundamentals of Planning Scientific Work -- Fundamentals of Mathematics -- Descriptive Statistics -- Probabilities -- Random Variables, Distributions -- Estimation -- Hypothesis Testing -- Statistical Model Building -- Introduction to R -- References -- Author Index -- Subject Index -- Examples Index -- R Functions Index. |
| Record Nr. | UNINA-9910917188803321 |
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Hedderich Jürgen
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Basic data analysis for time series with R / / DeWayne R. Derryberry
| Basic data analysis for time series with R / / DeWayne R. Derryberry |
| Autore | Derryberry DeWayne R. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (320 p.) |
| Disciplina | 001.4/2202855133 |
| Soggetto topico |
Time-series analysis - Data processing
R (Computer program language) Anàlisi de sèries temporals Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-118-59337-5
1-118-59323-5 1-118-59336-7 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Part I - Basic correlation structures Chapter 0 - R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 - Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis - What can go wrong... 1.6 Matrix manipulation in R Chapter 2 - The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 - Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 - The autocorrelation function and AR(1), AR(2) models 4.1 Standard models - What are the alternatives to white noise? 4.2 Autocovariance and autocorrelation 4.3 The acf() function in R 4.4 The first alternative to white noise: Autoregressive errors - AR(1), AR(2) Chapter 5 - The moving average models MA(1) and MA(2) 5.1 The moving average model 5.2 The autocorrelation for MA(1) models 5.3 A duality between MA(l) and AR(m) models 5.4 The autocorrelation for MA(2) models 5.5 Simulated examples of the MA(1) model 5.5 Simulated examples of the MA(2) model 5.6 AR(m) and MA(l) model acf() plots Part II - Analysis of periodic data and model selection Chapter 6 - Review of transcendental functions and complex numbers 6.1 Background 6.2 Complex arithmetic 6.3 Some important series 6.4 Useful facts about periodic transcendental functions Chapter 7 - The power spectrum and the periodogram 7.1 Introduction 7.2 A definition and a simplified form for p(f) 7.3 Inverting p(f) to recover the Ck values 7.4 The power spectrum for some familiar models 7.5 The periodogram, a closer look 7.6 The function spec.pgram() in R Chapter 8 - Smoothers, the bias-variance tradeoff, and the smoothed periodogram 8.1 Why is smoothing required? 8.2 Smoothing, bias, and variance 8.3 Smoothers used in R 8.4 Smoothing the periodogram for a series with a known period or unknown period. 8.5 Summary Chapter 9 - A regression model for periodic data. 9.1 The model 9.2 An example: the NYC temperature data 9.2 Complications 1: CO2 data 9.3 Complications 2: Sunspots 9.4 Complications 3: Accidental Deaths 9.5 Summary Chapter 10 - Basic model selection and cross validation. 10.1 Background 10.2 Hypothesis tests in simple regression 10.3 A more general setting for likelihood ratio tests 10.4 A subtlety different situation 10.5 Information criteria 10.6 Cross validation (Data splitting): NYC temperatures 10.7 Summary Chapter 11 - Fitting some Fourier series 11.1 Introduction: more complex periodic models 11.2 More complex periodic behavior: Accidental deaths 11.3 The Boise river flow data 11.4 Where do we go from here? Chapter 12 - Adjusting for AR(1) correlation in complex models 12.1 Introduction 12.2 The two sample t-test - Uncut and patch cut forest 12.3 The second Sleuth case - Global warming, a simple regression 12.4 The Semmelweis intervention 12.5 The NYC temperatures (adjusted) 12.6 The Boise river flow data: model selection with filtering 12.7 Implications of AR(1) adjustments and the "skip" method 12.8 Summary Part III - Complex temporal structures Chapter 13 - The backshift operator, the impulse response function, and general ARMA models 13.1 The general ARMA model 13.2 The backshift (shift, lag) operator 13.3 The impulse response operator - intuition 13.4 Impulse response operator, g(B) - computation 13.5 Interpretation and utility of the impulse response function Chapter 14 - The Yule-Walker equations and the partial autocorrelation function. 14.1 Background 14.2 Autocovariance of an ARMA(m,l) model 14.3 AR(m) and the Yule-Walker equations 14.4 The partial autocorrelation plot 14.5 The spectrum for ARMA processes 14.6 Summary Chapter 15 - Modeling philosophy and complete examples 15.1 Modeling overview 15.2 A complex periodic model - Monthly river flows, Furnas 1931-1978 15.3 A modeling example - trend and periodicity: CO2 levels at Mauna Lau 15.4 Modeling periodicity with a possible intervention - two examples 15.5 Periodic models: monthly, weekly, and daily averages 15.6 Summary Part IV - Some detailed and complete examples Chapter 16 - the Wolf sunspot number data 16.1 Background 16.2 Unknown period => nonlinear model 16.3 The function nls() in R 16.4 Determining the period 16.5 Instability in the mean, amplitude, and period 16.6 Data splitting for prediction 16.7 Summary Chapter 17 - Analysis of prostate and breast cancer data 17.1 Background 17.2 The first data set 17.3 The second data set Chapter 18 - Christopher Tennant/Ben Crosby watershed data 18.1 Background and question 18.2 Looking at the data and fitting Fourier series 18.3 Averaging data 18.4 Results Chapter 19 - Vostok ice core data 19.1 Source of the data 19.2 Background 19.3 Alignment 19.4 A naïve analysis 19.5 A related simulation 19.6 An AR(1) model for irregular spacing 19.7 Summary Appendices Appendix 1 - Using Data Market A1.1 Overview A1.2 Loading a time series in DataMarket A1.3 Respecting DataMarket licensing agreements Appendix 2 - AIC is PRESS A2.1 Introduction A2.2 PRESS A2.3 Connection to Akaike's result A2.4 Normalization and R2 A2.5 An example A2.6 Conclusion and further comments Appendix 3 - A 15 minute tutorial on optimization and nonlinear regression A3.1 Introduction A3.2 Newton's method for one dimensional nonlinear optimization A3.3 A direction, a step size, and a stopping rule A3.4 What could go wrong? A3.5 Generalizing the optimization problem A3.6 What could go wrong revisited A3.7 What can be done? . |
| Record Nr. | UNINA-9910132187103321 |
|
Derryberry DeWayne R.
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Basic data analysis for time series with R / / DeWayne R. Derryberry
| Basic data analysis for time series with R / / DeWayne R. Derryberry |
| Autore | Derryberry DeWayne R. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (320 p.) |
| Disciplina | 001.4/2202855133 |
| Soggetto topico |
Time-series analysis - Data processing
R (Computer program language) Anàlisi de sèries temporals Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-118-59337-5
1-118-59323-5 1-118-59336-7 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Part I - Basic correlation structures Chapter 0 - R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 - Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis - What can go wrong... 1.6 Matrix manipulation in R Chapter 2 - The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 - Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 - The autocorrelation function and AR(1), AR(2) models 4.1 Standard models - What are the alternatives to white noise? 4.2 Autocovariance and autocorrelation 4.3 The acf() function in R 4.4 The first alternative to white noise: Autoregressive errors - AR(1), AR(2) Chapter 5 - The moving average models MA(1) and MA(2) 5.1 The moving average model 5.2 The autocorrelation for MA(1) models 5.3 A duality between MA(l) and AR(m) models 5.4 The autocorrelation for MA(2) models 5.5 Simulated examples of the MA(1) model 5.5 Simulated examples of the MA(2) model 5.6 AR(m) and MA(l) model acf() plots Part II - Analysis of periodic data and model selection Chapter 6 - Review of transcendental functions and complex numbers 6.1 Background 6.2 Complex arithmetic 6.3 Some important series 6.4 Useful facts about periodic transcendental functions Chapter 7 - The power spectrum and the periodogram 7.1 Introduction 7.2 A definition and a simplified form for p(f) 7.3 Inverting p(f) to recover the Ck values 7.4 The power spectrum for some familiar models 7.5 The periodogram, a closer look 7.6 The function spec.pgram() in R Chapter 8 - Smoothers, the bias-variance tradeoff, and the smoothed periodogram 8.1 Why is smoothing required? 8.2 Smoothing, bias, and variance 8.3 Smoothers used in R 8.4 Smoothing the periodogram for a series with a known period or unknown period. 8.5 Summary Chapter 9 - A regression model for periodic data. 9.1 The model 9.2 An example: the NYC temperature data 9.2 Complications 1: CO2 data 9.3 Complications 2: Sunspots 9.4 Complications 3: Accidental Deaths 9.5 Summary Chapter 10 - Basic model selection and cross validation. 10.1 Background 10.2 Hypothesis tests in simple regression 10.3 A more general setting for likelihood ratio tests 10.4 A subtlety different situation 10.5 Information criteria 10.6 Cross validation (Data splitting): NYC temperatures 10.7 Summary Chapter 11 - Fitting some Fourier series 11.1 Introduction: more complex periodic models 11.2 More complex periodic behavior: Accidental deaths 11.3 The Boise river flow data 11.4 Where do we go from here? Chapter 12 - Adjusting for AR(1) correlation in complex models 12.1 Introduction 12.2 The two sample t-test - Uncut and patch cut forest 12.3 The second Sleuth case - Global warming, a simple regression 12.4 The Semmelweis intervention 12.5 The NYC temperatures (adjusted) 12.6 The Boise river flow data: model selection with filtering 12.7 Implications of AR(1) adjustments and the "skip" method 12.8 Summary Part III - Complex temporal structures Chapter 13 - The backshift operator, the impulse response function, and general ARMA models 13.1 The general ARMA model 13.2 The backshift (shift, lag) operator 13.3 The impulse response operator - intuition 13.4 Impulse response operator, g(B) - computation 13.5 Interpretation and utility of the impulse response function Chapter 14 - The Yule-Walker equations and the partial autocorrelation function. 14.1 Background 14.2 Autocovariance of an ARMA(m,l) model 14.3 AR(m) and the Yule-Walker equations 14.4 The partial autocorrelation plot 14.5 The spectrum for ARMA processes 14.6 Summary Chapter 15 - Modeling philosophy and complete examples 15.1 Modeling overview 15.2 A complex periodic model - Monthly river flows, Furnas 1931-1978 15.3 A modeling example - trend and periodicity: CO2 levels at Mauna Lau 15.4 Modeling periodicity with a possible intervention - two examples 15.5 Periodic models: monthly, weekly, and daily averages 15.6 Summary Part IV - Some detailed and complete examples Chapter 16 - the Wolf sunspot number data 16.1 Background 16.2 Unknown period => nonlinear model 16.3 The function nls() in R 16.4 Determining the period 16.5 Instability in the mean, amplitude, and period 16.6 Data splitting for prediction 16.7 Summary Chapter 17 - Analysis of prostate and breast cancer data 17.1 Background 17.2 The first data set 17.3 The second data set Chapter 18 - Christopher Tennant/Ben Crosby watershed data 18.1 Background and question 18.2 Looking at the data and fitting Fourier series 18.3 Averaging data 18.4 Results Chapter 19 - Vostok ice core data 19.1 Source of the data 19.2 Background 19.3 Alignment 19.4 A naïve analysis 19.5 A related simulation 19.6 An AR(1) model for irregular spacing 19.7 Summary Appendices Appendix 1 - Using Data Market A1.1 Overview A1.2 Loading a time series in DataMarket A1.3 Respecting DataMarket licensing agreements Appendix 2 - AIC is PRESS A2.1 Introduction A2.2 PRESS A2.3 Connection to Akaike's result A2.4 Normalization and R2 A2.5 An example A2.6 Conclusion and further comments Appendix 3 - A 15 minute tutorial on optimization and nonlinear regression A3.1 Introduction A3.2 Newton's method for one dimensional nonlinear optimization A3.3 A direction, a step size, and a stopping rule A3.4 What could go wrong? A3.5 Generalizing the optimization problem A3.6 What could go wrong revisited A3.7 What can be done? . |
| Record Nr. | UNINA-9910822358103321 |
|
Derryberry DeWayne R.
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| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann
| Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann |
| Autore | Bozza Silvia |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (XII, 187 p. 22 illus., 5 illus. in color.) |
| Disciplina | 519.5 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Statistics
Mathematical statistics—Data processing Forensic sciences Medical jurisprudence Forensic psychology Social sciences—Statistical methods Statistical Theory and Methods Statistics and Computing Forensic Science Forensic Medicine Forensic Psychology Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy Estadística bayesiana Processament de dades Criminalística R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Bayes factor
scientific evidence decision making forensic science uncertainty management probability theory forensic decision analysis Bayesian modeling R Bayesian statistics probabilistic inference |
| ISBN | 3-031-09839-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to the Bayes factor and decision analysis -- Chapter 2: Bayes factor for model choice -- Chapter 3: Bayes factor for evaluative purposes -- Chapter 4: Bayes factor for investigative purposes. |
| Record Nr. | UNISA-996495166503316 |
|
Bozza Silvia
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Bayes Factors for Forensic Decision Analyses with R / / by Silvia Bozza, Franco Taroni, Alex Biedermann
| Bayes Factors for Forensic Decision Analyses with R / / by Silvia Bozza, Franco Taroni, Alex Biedermann |
| Autore | Bozza Silvia |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (XII, 187 p. 22 illus., 5 illus. in color.) |
| Disciplina | 519.5 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Statistics
Mathematical statistics - Data processing Forensic sciences Medical jurisprudence Forensic psychology Social sciences - Statistical methods Statistical Theory and Methods Statistics and Computing Forensic Science Forensic Medicine Forensic Psychology Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy Estadística bayesiana Processament de dades Criminalística R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031098390
3031098390 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to the Bayes factor and decision analysis -- Chapter 2: Bayes factor for model choice -- Chapter 3: Bayes factor for evaluative purposes -- Chapter 4: Bayes factor for investigative purposes. |
| Record Nr. | UNINA-9910623993803321 |
|
Bozza Silvia
|
||
| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Beginning R : an introduction to statistical programming / / Dr. Joshua F. Wiley, Larry A. Pace
| Beginning R : an introduction to statistical programming / / Dr. Joshua F. Wiley, Larry A. Pace |
| Autore | Wiley Joshua |
| Edizione | [Second edition] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress, , [2015] |
| Descrizione fisica | 1 online resource (337 pages) : illustrations |
| Disciplina | 004 |
| Collana | The expert's voice in programming |
| Soggetto topico |
Programming languages (Electronic computers)
Computer software R (Computer program language) R (Llenguatge de programació) Programming Languages, Compilers, Interpreters Mathematical Software |
| ISBN |
9781484203736
1484203739 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents at a Glance; Contents; About the Author; In Memoriam; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Getting Star ted; 1.1 What is R, Anyway?; 1.2 A First R Session; 1.3 Your Second R Session; 1.3.1 Working with Indexes; 1.3.2 Representing Missing Data in R; 1.3.3 Vectors and Vectorization in R; 1.3.4 A Brief Introduction to Matrices; 1.3.5 More on Lists; 1.3.6 A Quick Introduction to Data Frames; Chapter 2: Dealing with Dates, Strings, and Data Frames; 2.1 Working with Dates and Times; 2.2 Working with Strings
Chapter 5: Functional Programming 5.1 Scoping Rules; 5.2 Reserved Names and Syntactically Correct Names; 5.3 Functions and Arguments; 5.4 Some Example Functions; 5.4.1 Guess the Number; 5.4.2 A Function with Arguments; 5.5 Classes and Methods; 5.5.1 S3 Class and Method Example; 5.5.2 S3 Methods for Existing Classes; Chapter 6: Probability Distributions; 6.1 Discrete Probability Distributions; 6.2 The Binomial Distribution; 6.2.1 The Poisson Distribution; 6.2.2 Some Other Discrete Distributions; 6.3 Continuous Probability Distributions; 6.3.1 The Normal Distribution 6.3.2 The t Distribution 6.3.3 The F distribution; 6.3.4 The Chi-Square Distribution; References; Chapter 7: Working with Tables; 7.1 Working with One-Way Tables; 7.2 Working with Two-Way Tables; Chapter 8: Descriptive Statistics and Exploratory Data Analysis; 8.1 Central Tendency ; 8.1.1 The Mean; 8.1.2 The Median; 8.1.3 The Mode; 8.2 Variability ; 8.2.1 The Range; 8.2.2 The Variance and Standard Deviation ; 8.3 Boxplots and Stem-and-Leaf Displays ; 8.4 Using the fBasics Package for Summary Statistics; References; Chapter 9: Working with Graphics 9.1 Creating Effective Graphics 9.2 Graphing Nominal and Ordinal Data; 9.3 Graphing Scale Data; 9.3.1 Boxplots Revisited ; 9.3.2 Histograms and Dotplots; 9.3.3 Frequency Polygons and Smoothed Density Plots; 9.3.4 Graphing Bivariate Data; References; Chapter 10: Traditional Statistical Methods; 10.1 Estimation and Confidence Intervals; 10.1.1 Confidence Intervals for Means; 10.1.2 Confidence Intervals for Proportions; 10.1.3 Confidence Intervals for the Variance; 10.2 Hypothesis Tests with One Sample; 10.3 Hypothesis Tests with Two Samples; References Chapter 11: Modern Statistical Methods |
| Record Nr. | UNINA-9910300650103321 |
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Wiley Joshua
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| Berkeley, CA : , : Apress, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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