05323nam 2200649 450 991082786600332120200520144314.01-118-42200-71-118-42201-5(CKB)3710000000167931(EBL)1729064(SSID)ssj0001262153(PQKBManifestationID)11729144(PQKBTitleCode)TC0001262153(PQKBWorkID)11229813(PQKB)10192819(OCoLC)875056210(MiAaPQ)EBC1729064(Au-PeEL)EBL1729064(CaPaEBR)ebr10891177(PPN)192309064(EXLCZ)99371000000016793120140717h20142014 uy 0engur|n|---|||||txtccrMaking sense of data I a practical guide to exploratory data analysis and data mining /Glenn J. Myatt, Wayne P. JohnsonSecond edition.Hoboken, New Jersey :Wiley,2014.©20141 online resource (250 p.)Description based upon print version of record.1-118-40741-5 Includes bibliographical references and index.Making Sense of Data I; Contents; Preface; 1 Introduction; 1.1 Overview; 1.2 Sources of Data; 1.3 Process for Making Sense of Data; 1.3.1 Overview; 1.3.2 Problem Definition and Planning; 1.3.3 Data Preparation; 1.3.4 Analysis; 1.3.5 Deployment; 1.4 OVERVIEW OF BOOK; 1.4.1 Describing Data; 1.4.2 Preparing Data Tables; 1.4.3 Understanding Relationships; 1.4.4 Understanding Groups; 1.4.5 Building Models; 1.4.6 Exercises; 1.4.7 Tutorials; 1.5 Summary; Further Reading; Exercises; Exercises; Exercises; Exercises; 2 Describing Data; 2.1 Overview; 2.2 Observations and Variables2.3 Types of Variables2.4 Central Tendency; 2.4.1 Overview; 2.4.2 Mode; 2.4.3 Median; 2.4.4 Mean; 2.5 Distribution of the Data; 2.5.1 Overview; 2.5.2 Bar Charts and Frequency Histograms; 2.5.3 Range; 2.5.4 Quartiles; 2.5.5 Box Plots; 2.5.6 Variance; 2.5.7 Standard Deviation; 2.5.8 Shape; 2.6 Confidence Intervals; 2.7 Hypothesis Tests; Further Reading; Further Reading; Further Reading; Further Reading; 3 Preparing Data Tables; 3.1 Overview; 3.2 Cleaning the Data; 3.3 Removing Observations and Variables; 3.4 Generating Consistent Scales Across Variables; 3.5 New Frequency Distribution3.6 Converting Text to Numbers3.7 Converting Continuous Data to Categories; 3.8 Combining Variables; 3.9 Generating Groups; 3.10 Preparing Unstructured Data; 4 Understanding Relationships; 4.1 Overview; 4.2 Visualizing Relationships Between Variables; 4.2.1 Scatterplots; 4.2.2 Summary Tables and Charts; 4.2.3 Cross-Classification Tables; 4.3 Calculating Metrics About Relationships; 4.3.1 Overview; 4.3.2 Correlation Coefficients; 4.3.3 Kendall Tau; 4.3.4 t-Tests Comparing Two Groups; 4.3.5 ANOVA; 4.3.6 Chi-Square; 5 Identifying and Understanding Groups; 5.1 Overview; 5.2 Clustering5.2.1 Overview5.2.2 Distances; 5.2.3 Agglomerative Hierarchical Clustering; 5.2.4 k-Means Clustering; 5.3 Association Rules; 5.3.1 Overview; 5.3.2 Grouping by Combinations of Values; 5.3.3 Extracting and Assessing Rules; 5.3.4 Example; 5.4 Learning Decision Trees from Data; 5.4.1 Overview; 5.4.2 Splitting; 5.4.3 Splitting Criteria; 5.4.4 Example; Exercises; Further Reading; 6 Building Models from Data; 6.1 Overview; 6.2 Linear Regression; 6.2.1 Overview; 6.2.2 Fitting a Simple Linear Regression Model; 6.2.3 Fitting a Multiple Linear Regression Model; 6.2.4 Assessing the Model Fit6.2.5 Testing Assumptions6.2.6 Selecting and Assessing Independent Variables; 6.3 Logistic Regression; 6.3.1 Overview; 6.3.2 Fitting a Simple Logistic Regression Model; 6.3.3 Fitting and Interpreting Multiple Logistic Regression Models; 6.3.4 Significance of Model and Coefficients; 6.3.5 Classification; 6.4 k-Nearest Neighbors; 6.4.1 Overview; 6.4.2 Training; 6.4.3 Predicting; 6.5 Classification and Regression Trees; 6.5.1 Overview; 6.5.2 Predicting; 6.5.3 Example; 6.6 Other Approaches; 6.6.1 Neural Networks; 6.6.2 Support Vector Machines; 6.6.3 Discriminant Analysis; 6.6.4 Naïve Bayes6.6.5 Random ForestsWith a focus on the needs of educators and students, Making Sense of Data presents the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. This Second Edition focuses on basic data analysis approaches that are necessary to complete a diverse range of projects. New examples have been added to illustrate the different approaches, and there is considerably more emphasis on hands-on software tutorials to provide real-world exercises. Via the related Web site, the book is accompanied by Traceis software, data sets, aData miningMathematical statisticsData mining.Mathematical statistics.006.3/12Myatt Glenn J.1969-695403Johnson Wayne P.MiAaPQMiAaPQMiAaPQBOOK9910827866003321Making sense of data I3922441UNINA04536nam 22006135 450 991102114460332120250818130251.0981-9683-39-410.1007/978-981-96-8339-0(CKB)40378255100041(MiAaPQ)EBC32265650(Au-PeEL)EBL32265650(DE-He213)978-981-96-8339-0(OCoLC)1534195739(EXLCZ)994037825510004120250818d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierNanotechnology in Air Quality Management For Sustainable Environment with high interest /edited by Nabarun Ghosh, Debajyoti Ghosh, Shaily Goyal1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (420 pages)Sustainable Development and Biodiversity,2352-4758 ;39981-9683-38-6 -- Chapter 1: Nanotechnology:an emerging field for air quality sustainability -- Chapter 2: Nanotechnology for Clean Air and Sustainable Futures -- Chapter 3: Nanomaterials in Air Pollution Remediation: A Sustainable Approach for Cleaner Air -- Chapter 4: Promising applications of different nanomaterials in air pollution remediation -- Chapter 5: Cutting-Edge Nanomaterials for Comprehensive Air Pollution Mitigation and Environmental Restoration -- Chapter 6: Application of different nanomaterials in air pollution remediation -- Chapter 7: Discovery and application of nanotechnology towards air purification in reducing airborne pathogens, canine allergies, microflora in ice-machines and application of silver nanoparticles in facial mask -- Chapter 8: Engineered carbon nanoparticles: exposure risks, respiratory toxicity, and underlying mechanisms -- Chapter 9: Characterizing nanoparticle aerosols for better air quality management -- Chapter 10:Biosensors for detecting air pollutants and hazardous gases -- Chapter 11:Nanoscale concepts for air pollution sensors -- Chapter 12:Nano-fibrous membranes in air purification.This book explores the role of nanotechnology in improving air quality and environmental sustainability. It highlights the use of advanced nanomaterials such as nano-sensors, nano-catalysts, nanomembranes, and nano-biomaterials in pollution detection, monitoring, prevention, and remediation. Air quality is a critical component of public health and ecological balance. Rapid industrialization, urban expansion, and natural calamities have significantly increased the levels of air pollutants, posing serious risks to humans and ecosystems. Traditional methods of air purification and monitoring often fall short in managing the scale and complexity of modern pollutants. Nanotechnology provides a transformative approach, offering materials and devices with unique properties such as high reactivity, sensitivity, and durability. This book presents a comprehensive overview of current nanotechnological interventions in air quality management. It discusses the scientific basis, material properties, applications, and limitations, supported by real-world case studies and experimental data. This book benefits researchers, environmental scientists, nanotechnologists, engineers, and policymakers engaged in air quality, sustainability, and environmental technology. It serves as a valuable resource for academic professionals and industry practitioners seeking to understand or apply nanotechnology in environmental monitoring and pollution control.Sustainable Development and Biodiversity,2352-4758 ;39BioremediationEnvironmental chemistryNanobiotechnologyEnvironmental BiotechnologyEnvironmental ChemistryNanobiotechnologyBioremediation.Environmental chemistry.Nanobiotechnology.Environmental Biotechnology.Environmental Chemistry.Nanobiotechnology.628.5660.6Ghosh Nabarun1844469Ghosh Debajyoti1844470Goyal Shaily1844471MiAaPQMiAaPQMiAaPQBOOK9911021144603321Nanotechnology in Air Quality Management4427128UNINA