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Travel behavior characteristics analysis technology based on mobile phone location data : methodology and empirical research / / Fei Yang and Zhenxing Yao
| Travel behavior characteristics analysis technology based on mobile phone location data : methodology and empirical research / / Fei Yang and Zhenxing Yao |
| Autore | Yang Fei <1980-> |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (235 pages) |
| Disciplina | 388.4 |
| Soggetto topico |
Choice of transportation - Mathematical models
Planificació del transport Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
981-16-8007-8
981-16-8008-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- List of Figures -- Contents -- List of Tables -- Foreword -- 1 Introduction -- 1.1 General -- 1.1.1 Drawbacks of Individual Travel Survey Methods -- 1.1.2 Advantages of Mobile Phone Sensor Survey Methods -- 1.1.3 Traffic Demand Analysis Model Development Challenges -- 1.1.4 New Opportunities in the Era of Traffic Big Data -- 1.2 Target and Values of Mobile Phone Data Based Travel Survey Method -- 1.3 From Mobile Phone Location Data to Travel Information -- 1.3.1 Mobile Phone Location Data Collection and Analysis -- 1.3.2 Refined Travel Information Extraction and Collection -- 1.3.3 'Man-Vehicle-Communication' Simulation Platform Construction and Simulation -- 1.3.4 Empirical Study and Performance Evaluation -- 1.3.5 Challenges Faced by Travel Information Detection and Analysis -- 1.4 Summary -- 2 Literature Review -- 2.1 Types and Characteristics of Mobile Data Based Travel Survey Method -- 2.1.1 Mobile Phone Sensor Data Based Travel Survey Method -- 2.1.2 Mobile Phone Signaling Data Based Travel Survey Method -- 2.1.3 Mobile Phone Social Network Data Based Travel Survey Method -- 2.2 Overview and Summary of Existing Researches and Applications -- 2.3 Individual Travel Behavior Analysis Based on Mobile Phone Signaling Data -- 2.3.1 Dynamic Monitoring of Residents' Travel Activities -- 2.3.2 Regional and Cross-Section Passenger Flow Analysis -- 2.4 Individual Travel Behavior Analysis Based on Mobile Phone Sensor Data -- 2.4.1 Trip Chain Information Extraction -- 2.4.2 Resident Travel Survey Application -- 2.5 Activity Hotspots Analysis Based on Wi-Fi Data -- 2.6 Individual Travel Behavior Analysis Based on Mobile Phone Social Network Data -- 2.6.1 Resident Travel Characteristics Detection -- 2.6.2 Trip OD Estimation -- 2.6.3 Characteristics of Job and Residence Distribution -- 2.7 Research Summary and Trend -- References.
3 Methodology for Mobile Phone Location Data Mining -- 3.1 Technology Structure for Individual Travel Chain Information Extraction -- 3.2 Trip End Recognition Based on Spatial Clustering Algorithm -- 3.3 Mode Transfer Point Recognition Based on Wavelet Analysis Algorithm -- 3.4 Travel Mode Recognition Based on Machine Learning Algorithm -- 3.4.1 Neural Network Algorithm -- 3.4.2 Support Vector Machine Algorithm -- 3.4.3 Bayesian Network Algorithm -- 3.4.4 Random Forest Algorithm -- 3.5 Trip Chain Information Optimization Based on GIS Map Matching -- 3.6 Summary -- References -- 4 Mobile Phone Sensor Data Collection and Analysis -- 4.1 Data Collection App Development -- 4.1.1 Function Description -- 4.1.2 Operation Interface -- 4.2 Database Construction and Management -- 4.3 Privacy and Data Security -- 4.4 Characteristics Analysis of Mobile Phone Sensor Data -- 4.4.1 GPS Data Accuracy and Quality -- 4.4.2 Spatial-temporal Travel Characteristics -- 4.4.3 Travel Trajectory Point Density -- 4.4.4 Travel Speed Characteristics -- 4.4.5 Travel Acceleration Characteristics -- 4.5 Summary -- 5 'Pedestrian-Traffic Flow-Communication' Integrated Simulation Platform Construction -- 5.1 Framework of the Simulation Platform -- 5.2 Traffic Environment and Individual Travel Simulation -- 5.2.1 Traffic Environment Design -- 5.2.2 Individual Travel Module Construction and Simulation -- 5.3 Wireless Communication Simulation -- 5.3.1 Wireless Communication Events Description and Simulation -- 5.3.2 Mobile Communication Signal Propagation Simulation -- 5.3.3 A Case Study of Wireless Communication Simulation -- 5.4 Mobile Phone Sensor Data Simulation -- 5.4.1 Data Disturbance Loading Method and Simulation -- 5.4.2 A Case Study of Mobile Phone Sensor Data Simulation -- 5.5 Summary -- References. 6 Empirical Study on Trip Information Extraction Based on Mobile Phone Sensor Data -- 6.1 Experiment Design and Data Collection -- 6.1.1 Travel Plan for Different Travel Purposes -- 6.1.2 Travel Plan for Multiple Modes -- 6.1.3 Travel Plan for Different Traffic Conditions -- 6.1.4 Travel Log Collection -- 6.2 Empirical Study of Trip End Recognition Based on Spatial Clustering Algorithm -- 6.2.1 Model Parameter Configuration -- 6.2.2 A Case Study of Trip End Recognition and Travel Trajectory Cutting -- 6.2.3 Results and Error Analysis -- 6.3 Empirical Study of Mode Transfer Point Recognition Based on Wavelet Transform Modulus Maximum Algorithm -- 6.3.1 Model Parameter Configuration -- 6.3.2 A Case Study of Mode Transfer Point Recognition -- 6.3.3 Results and Error Analysis -- 6.4 Empirical Study of Travel Mode Recognition Based on Neural Network Algorithm -- 6.4.1 Model Parameter Configuration -- 6.4.2 A Case Study of Traffic Mode Recognition -- 6.4.3 Results and Error Analysis -- 6.5 Empirical Study of Travel Chain Recognition Optimization Based on GIS Map Matching -- 6.5.1 Model Parameter Configuration -- 6.5.2 A Case Study of Travel Mode Recognition Optimization -- 6.5.3 Results and Error Analysis -- 6.6 Summary -- 7 Influence Parameters and Sensitivity Analysis -- 7.1 Influencing Factors and Mechanism -- 7.2 Data Characteristics Under Different Experiment Conditions -- 7.2.1 Data Collection -- 7.2.2 Data Analysis -- 7.3 Sensitivity Analysis of Travel Mode Recognition -- 7.3.1 Influence of Algorithms -- 7.3.2 Influence of Data Sampling Frequency -- 7.3.3 Influence of Traffic Condition -- 7.4 Sensitivity Analysis of Mode Transfer Time Recognition -- 7.4.1 Influence of Algorithm -- 7.4.2 Influence of Data Sampling Frequency -- 7.4.3 Influence of Traffic Condition. 7.5 Sensitivity Analysis of Trip Chain Information Recognition Based on Simulation Data -- 7.5.1 Sensitivity Analysis of Travel Mode -- 7.5.2 Sensitivity Analysis of Mode Transfer Time -- 7.6 Summary -- 8 Thinking About Application of Refined Travel Data in Traffic Planning -- 8.1 Optimizing the Traditional Four-Step Method -- 8.2 Optimizing the Layout of Bus Stations and Network -- 8.3 Constructing Activity Based Traffic Demand Model -- 8.4 Other Applications -- 9 Outlook -- 9.1 Technical Efficiency and Universal Upgrading -- 9.2 Multiple Heterogeneous Data Integrating -- 9.3 Traffic Planning Theories and Models Upgrading. |
| Record Nr. | UNINA-9910743211203321 |
Yang Fei <1980->
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Trends in teaching experimentation in the life sciences : putting research into practice to drive institutional change / / Nancy J. Pelaez, Stephanie M. Gardner, and Trevor R. Anderson
| Trends in teaching experimentation in the life sciences : putting research into practice to drive institutional change / / Nancy J. Pelaez, Stephanie M. Gardner, and Trevor R. Anderson |
| Autore | Pelaez Nancy J. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (572 pages) |
| Disciplina | 570.78 |
| Collana | Contributions from Biology Education Research |
| Soggetto topico |
Biology - Experiments
Biology - Experiments - Data processing Biologia Experiments Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-98592-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910568278503321 |
|
Pelaez Nancy J.
|
||
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Univariate, bivariate, and multivariate statistics using R : quantitative tools for data analysis and data science / / Daniel J. Denis
| Univariate, bivariate, and multivariate statistics using R : quantitative tools for data analysis and data science / / Daniel J. Denis |
| Autore | Denis Daniel J. <1974-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2020 |
| Descrizione fisica | 1 online resource (287 pages) |
| Disciplina | 519.53 |
| Soggetto topico |
Analysis of variance
Multivariate analysis Mathematical statistics - Data processing R (Computer program language) Anàlisi de variància Anàlisi multivariable Estadística matemàtica Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-119-54991-4
1-119-54996-5 1-119-54995-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to applied statistics -- Introduction to R and computational statistics -- Exploring data with R : essential graphics and visualization -- Means, correlations, counts : drawing inferences using easy-to-implement statistical tests -- Power analysis and sample size estimation using R -- Analysis of variance : fixed effects, random effects, mixed models and repeated measures -- Simple and multiple linear regression -- Logistic regression and the generalized linear model -- Multivariate analysis of variance (MANOVA) and discriminant analysis -- Principal components analysis -- Exploratory factor analysis -- Cluster analysis -- Nonparametric tests. |
| Record Nr. | UNINA-9910555025503321 |
|
Denis Daniel J. <1974->
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| Hoboken, New Jersey : , : Wiley, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Using R for biostatistics / / Thomas W. MacFarland, Jan M. Yates
| Using R for biostatistics / / Thomas W. MacFarland, Jan M. Yates |
| Autore | MacFarland Thomas W. |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XXXV, 902 p. 300 illus., 265 illus. in color.) |
| Disciplina | 570.15195 |
| Soggetto topico |
Biometry - Data processing
R (Computer program language) Biometria Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-62404-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction: Biostatistics and R -- 1.1 Purpose of this Text -- 1.2 Development of Biostatistics -- 1.3 Development of R -- 1.4 How R is Used in this Text -- 1.5 Import Data into R -- 1.6 Addendum1: Efficient Programming with R, Project Workflow, and Good Programming Practices (gpp) -- 1.7 Addendum2: Preview of Descriptive Statistics and Graphics Using R -- 1.8 Addendum3: R and Beautiful Graphics -- 1.9 Addendum4: Research Designs Used in Biostatistics -- 1.10 Prepare to Exit, Save, and Later Retrieve this R Session -- 1.11 External Data and/or Data Resources Used in this Lesson -- 2 Data Exploration, Descriptive Statistics, and Measures of Central Tendency -- 2.1 Background -- 2.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 2.3 Organize the Data and Display the Code Book -- 2.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 2.5 Descriptive Statistics for Initial Analysis of the Data -- 2.6 Quality Assurance, Data Distribution, and Tests for Normality -- 2.7 Statistical Test(s) -- 2.8 Summary -- 2.9 Addendum1: Specialized External Packages and Functions -- 2.10 Addendum2: Parametric v Nonparametric -- 2.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 2.12 Prepare to Exit, Save, and Later Retrieve this R Session -- 2.13 External Data and/or Data Resources Used in this Lesson -- 3 Student's t-Test for Independent Samples -- 3.1 Background -- 3.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 3.3 Organize the Data and Display the Code Book -- 3.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 3.5 Descriptive Statistics for Initial Analysis of the Data -- 3.6 Quality Assurance, Data Distribution, and Tests for Normality -- 3.7 Statistical Test(s) -- 3.8 Summary of Outcomes -- 3.9 Addendum1: t-Statistic v z-Statistic -- 3.10 Addendum2: Parametric v Nonparametric -- 3.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 3.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 3.13 External Data and/or Data Resources Used in this Lesson -- 4 Student's t-Test for Matched Pairs -- 4.1 Background -- 4.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 4.3 Organize the Data and Display the Code Book -- 4.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 4.5 Descriptive Statistics for Initial Analysis of the Data -- 4.6 Quality Assurance, Data Distribution, and Tests for Normality -- 4.7 Statistical Test(s) -- 4.8 Summary of Outcomes -- 4.9 Addendum1: R-Based Tools for Unstacked (e.g. Wide) Data -- 4.10 Addendum2: Stacked Data and Student's t-Test for Matched Pairs -- 4.11 Addendum 3: The Impact of N on Student's t-Test -- 4.12 Addendum 4: Parametric v Nonparametric -- 4.13 Addendum5: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 4.14 Prepare to Exit, Save, and Later Retrieve This R Session -- 4.15 External Data and/or Data Resources Used in this Lesson -- 5 Oneway Analysis of Variance (ANOVA) -- 5.1 Background -- 5.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 5.3 Organize the Data and Display the Code Book -- 5.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 5.5 Descriptive Statistics for Initial Analysis of the Data -- 5.6 Quality Assurance, Data Distribution, and Tests for Normality -- 5.7 Statistical Test(s) -- 5.8 Summary of Outcomes -- 5.9 Addendum1: Other Packages for Display of Oneway ANOVA -- 5.10 Addendum2: Parametric v Nonparametric -- 5.11 Addendum3: Additional Practice Data Sets -- 5.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 5.13 External Data and/or Data Resources Used in this Lesson -- 6 Twoway Analysis of Variance (ANOVA) -- 6.1 Background -- 6.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 6.3 Organize the Data and Display the Code Book -- 6.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 6.5 Descriptive Statistics for Initial Analysis of the Data -- 6.6 Quality Assurance, Data Distribution, and Tests for Normality -- 6.7 Statistical Test(s) -- 6.8 Summary of Outcomes -- 6.9 Addendum 1: Other Packages for Display of Twoway ANOVA -- 6.10 Addendum 2: Parametric v Nonparametric -- 6.11 Addendum 3: Additional Practice Data Sets -- 6.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 6.13 External Data and/or Data Resources Used in this Lesson -- 7 Correlation, Association, Regression, Likelihood, and Prediction -- 7.1 Background -- 7.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 7.3 Organize the Data and Display the Code Book -- 7.4 Quality Assurance, Data Distribution, and Tests for Normality -- 7.5 Statistical Test(s) -- 7.6 Summary of Outcomes -- 7.7 Addendum 1: Multiple Regression -- 7.8 Addendum 2: Likelihood and Odds Ratio -- 7.9 Addendum 3:Parametric v Nonparametric -- 7.10 Addendum 4: Additional Practice Data Sets -- 7.11 Prepare to Exit, Save, and Later Retrieve This R Session -- 7.12 External Data and/or Data Resources Used in this Lesson -- 8 Working with Large and Complex Datasets -- 8.1 Background -- 8.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 8.3 Organize the Data and Display the Code Book -- 8.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 8.5 Descriptive Statistics for Initial Analysis of the Data -- 8.6 Quality Assurance, Data Distribution, and Tests for Normality -- 8.7 Statistical Test(s) -- 8.8 Summary of Outcomes -- 8.9 Addendum1: Additional Graphics, to Show Relationships Between and Among Data -- 8.10 Addendum2: Graphics Using the lattice Package -- 8.11 Addendum3: Graphics Using the ggplot2 Package -- 8.12 Addendum 4: Beyond an Introduction to R - Use the tidyverse to Create Subsets of Original Datasets -- 8.13 Prepare to Exit, Save, and Later Retrieve This R Session -- 8.14 External Data and/or Data Resources Used in this Lesson -- 9 Future Actions and Next Steps -- 9.1 Use of This Text -- 9.2 R and Beautiful Reporting with R Markdown -- 9.3 Future Use of R for Biostatistics -- 9.4 Big Data and Bio Informatics -- 9.5 External Resources -- 9.6 Contact the Authors. . |
| Record Nr. | UNINA-9910484142103321 |
|
MacFarland Thomas W.
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Using R for biostatistics / / Thomas W. MacFarland, Jan M. Yates
| Using R for biostatistics / / Thomas W. MacFarland, Jan M. Yates |
| Autore | MacFarland Thomas W. |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XXXV, 902 p. 300 illus., 265 illus. in color.) |
| Disciplina | 570.15195 |
| Soggetto topico |
Biometry - Data processing
R (Computer program language) Biometria Processament de dades R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-62404-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction: Biostatistics and R -- 1.1 Purpose of this Text -- 1.2 Development of Biostatistics -- 1.3 Development of R -- 1.4 How R is Used in this Text -- 1.5 Import Data into R -- 1.6 Addendum1: Efficient Programming with R, Project Workflow, and Good Programming Practices (gpp) -- 1.7 Addendum2: Preview of Descriptive Statistics and Graphics Using R -- 1.8 Addendum3: R and Beautiful Graphics -- 1.9 Addendum4: Research Designs Used in Biostatistics -- 1.10 Prepare to Exit, Save, and Later Retrieve this R Session -- 1.11 External Data and/or Data Resources Used in this Lesson -- 2 Data Exploration, Descriptive Statistics, and Measures of Central Tendency -- 2.1 Background -- 2.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 2.3 Organize the Data and Display the Code Book -- 2.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 2.5 Descriptive Statistics for Initial Analysis of the Data -- 2.6 Quality Assurance, Data Distribution, and Tests for Normality -- 2.7 Statistical Test(s) -- 2.8 Summary -- 2.9 Addendum1: Specialized External Packages and Functions -- 2.10 Addendum2: Parametric v Nonparametric -- 2.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 2.12 Prepare to Exit, Save, and Later Retrieve this R Session -- 2.13 External Data and/or Data Resources Used in this Lesson -- 3 Student's t-Test for Independent Samples -- 3.1 Background -- 3.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 3.3 Organize the Data and Display the Code Book -- 3.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 3.5 Descriptive Statistics for Initial Analysis of the Data -- 3.6 Quality Assurance, Data Distribution, and Tests for Normality -- 3.7 Statistical Test(s) -- 3.8 Summary of Outcomes -- 3.9 Addendum1: t-Statistic v z-Statistic -- 3.10 Addendum2: Parametric v Nonparametric -- 3.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 3.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 3.13 External Data and/or Data Resources Used in this Lesson -- 4 Student's t-Test for Matched Pairs -- 4.1 Background -- 4.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 4.3 Organize the Data and Display the Code Book -- 4.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 4.5 Descriptive Statistics for Initial Analysis of the Data -- 4.6 Quality Assurance, Data Distribution, and Tests for Normality -- 4.7 Statistical Test(s) -- 4.8 Summary of Outcomes -- 4.9 Addendum1: R-Based Tools for Unstacked (e.g. Wide) Data -- 4.10 Addendum2: Stacked Data and Student's t-Test for Matched Pairs -- 4.11 Addendum 3: The Impact of N on Student's t-Test -- 4.12 Addendum 4: Parametric v Nonparametric -- 4.13 Addendum5: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 4.14 Prepare to Exit, Save, and Later Retrieve This R Session -- 4.15 External Data and/or Data Resources Used in this Lesson -- 5 Oneway Analysis of Variance (ANOVA) -- 5.1 Background -- 5.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 5.3 Organize the Data and Display the Code Book -- 5.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 5.5 Descriptive Statistics for Initial Analysis of the Data -- 5.6 Quality Assurance, Data Distribution, and Tests for Normality -- 5.7 Statistical Test(s) -- 5.8 Summary of Outcomes -- 5.9 Addendum1: Other Packages for Display of Oneway ANOVA -- 5.10 Addendum2: Parametric v Nonparametric -- 5.11 Addendum3: Additional Practice Data Sets -- 5.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 5.13 External Data and/or Data Resources Used in this Lesson -- 6 Twoway Analysis of Variance (ANOVA) -- 6.1 Background -- 6.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 6.3 Organize the Data and Display the Code Book -- 6.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 6.5 Descriptive Statistics for Initial Analysis of the Data -- 6.6 Quality Assurance, Data Distribution, and Tests for Normality -- 6.7 Statistical Test(s) -- 6.8 Summary of Outcomes -- 6.9 Addendum 1: Other Packages for Display of Twoway ANOVA -- 6.10 Addendum 2: Parametric v Nonparametric -- 6.11 Addendum 3: Additional Practice Data Sets -- 6.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 6.13 External Data and/or Data Resources Used in this Lesson -- 7 Correlation, Association, Regression, Likelihood, and Prediction -- 7.1 Background -- 7.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 7.3 Organize the Data and Display the Code Book -- 7.4 Quality Assurance, Data Distribution, and Tests for Normality -- 7.5 Statistical Test(s) -- 7.6 Summary of Outcomes -- 7.7 Addendum 1: Multiple Regression -- 7.8 Addendum 2: Likelihood and Odds Ratio -- 7.9 Addendum 3:Parametric v Nonparametric -- 7.10 Addendum 4: Additional Practice Data Sets -- 7.11 Prepare to Exit, Save, and Later Retrieve This R Session -- 7.12 External Data and/or Data Resources Used in this Lesson -- 8 Working with Large and Complex Datasets -- 8.1 Background -- 8.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 8.3 Organize the Data and Display the Code Book -- 8.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 8.5 Descriptive Statistics for Initial Analysis of the Data -- 8.6 Quality Assurance, Data Distribution, and Tests for Normality -- 8.7 Statistical Test(s) -- 8.8 Summary of Outcomes -- 8.9 Addendum1: Additional Graphics, to Show Relationships Between and Among Data -- 8.10 Addendum2: Graphics Using the lattice Package -- 8.11 Addendum3: Graphics Using the ggplot2 Package -- 8.12 Addendum 4: Beyond an Introduction to R - Use the tidyverse to Create Subsets of Original Datasets -- 8.13 Prepare to Exit, Save, and Later Retrieve This R Session -- 8.14 External Data and/or Data Resources Used in this Lesson -- 9 Future Actions and Next Steps -- 9.1 Use of This Text -- 9.2 R and Beautiful Reporting with R Markdown -- 9.3 Future Use of R for Biostatistics -- 9.4 Big Data and Bio Informatics -- 9.5 External Resources -- 9.6 Contact the Authors. . |
| Record Nr. | UNISA-996466547503316 |
|
MacFarland Thomas W.
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Soggetto topico
-
Processament de dades
(155)
- Statistics (19)
- Machine learning (17)
- Aprenentatge automàtic (16)
- Estadística matemàtica (16)