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SPSS Statistics for Data Analysis and Visualization.pdf

SPSS Statistics for Data Analysis and Visualization
2018-04-16 上传大小:94.05MB
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Statistics and Data Analysis for Financial Engineering

Publication Date: November 17, 2010 | ISBN-10: 1441977864 | ISBN-13: 978-1441977861 | Edition: 2011 Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful.

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Statistics, Data Analysis, and Decision Modeling Edition(5th) 无水印原版pdf

Statistics, Data Analysis, and Decision Modeling International Edition(5th) 英文无水印原版pdf 第5版 pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

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Mathematical Statistics and Data Analysis,3ed

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SPSS Statistics 17.0安装步骤

SPSS Statistics 17.0安装步骤,帮您迅速安装。

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Mathematical Statistics and Data Analysis 3rd

Mathematical Statistics and Data Analysis 3rd Mathematical Statistics and Data Analysis 3rd

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An Introduction to Secondary Data Analysis with IBM SPSS Statistics

An Introduction to Secondary Data Analysis with IBM SPSS Statistics English | 19 Dec. 2016 | ISBN: 1446285766, 1446285774 | 336 Pages | Many professional, high-quality surveys collect data on people’s behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to: Create a robust research question and design that suits secondary analysis Locate, access and explore data online Understand data documentation Check and ‘clean’ secondary data Manage and analyse your data to produce meaningful results Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you’ll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book’s companion website give you an opportunity to practice, check your understanding and work hands on with real data as you’re learning.

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1986_Silverman_Density Estimation for Statistics and Data Analysis

Density Estimation for Statistics and Data Analysis, Silverman著, 1986年版,核密度估计教材

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Statistics for High-Dimensional Data

Statistics for High-Dimensional Data Methods, Theory and Applications

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Business Analytics: Data Analysis & Decision Making

Business Analytics: Data Analysis & Decision Making (英语) 精装 – 2017年第六版S. Christian Albright (作者), Wayne L. Winston (作者)

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An Introduction to Statistics with Python [2016]

An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing) by Thomas Haslwanter English | 2016 | ISBN: 3319283154 | 298 Pages | PDF (True) | 4.58 MB This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.

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Python for Data Analysis, 2nd Edition 英文高清完整.pdf版下载

Book Description Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? The second edition of this hands-on guide—updated for Python 3.5 and Pandas 1.0—is packed with practical cases studies that show you how to effectively solve a broad set of data analysis problems, using Python libraries such as NumPy, pandas, matplotlib, and IPython. Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive applications. It’s ideal for analysts new to Python and for Python programmers new to scientific computing. Table of Contents Chapter 1 Preliminaries Chapter 2 Python Language Basics, IPython, and Jupyter Notebooks Chapter 3 Built-in Data Structures, Functions, and Files Chapter 4 NumPy Basics: Arrays and Vectorized Computation Chapter 5 Getting Started with pandas Chapter 6 Data Loading, Storage, and File Formats Chapter 7 Data Cleaning and Preparation Chapter 8 Data Wrangling: Join, Combine, and Reshape Chapter 9 Plotting and Visualization Chapter 10 Data Aggregation and Group Operations Chapter 11 Interlude: Data Analysis Examples Chapter 12 Time Series Chapter 13 Advanced NumPy Chapter 14 Using Modeling Libraries with pandas Chapter 15 Examples Data Sets Appendix Advanced IPython and Jupyter

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Naked Statistics, Charles Wheelan.mobi

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SPSS Statistics for Data Analysis and Visualization azw3

SPSS Statistics for Data Analysis and Visualization 英文azw3 本资源转载自网络,如有侵权,请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

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SPSS Statistics for Data Analysis and Visualization epub

SPSS Statistics for Data Analysis and Visualization 英文epub 本资源转载自网络,如有侵权,请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

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Python for Data Analysis-pdf-2017-第二版(完全版)

根据其他朋友azw3格式转化的pdf格式,或者你可以先下载calibre,再用calibre打开azw3格式的文档。

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Network Security Through Data Analysis: From Data to Action 2nd Edition pdf

Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In the updated second edition of this practical guide, security researcher Michael Collins shows InfoSec personnel the latest techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what actions are necessary to harden and defend the systems within it. In three sections, this book examines the process of collecting and organizing data, various tools for analysis, and several different analytic scenarios and techniques. New chapters focus on active monitoring and traffic manipulation, insider threat detection, data mining, regression and machine learning, and other topics. You’ll learn how to: Use sensors to collect network, service, host, and active domain data Work with the SiLK toolset, Python, and other tools and techniques for manipulating data you collect Detect unusual phenomena through exploratory data analysis (EDA), using visualization and mathematical techniques Analyze text data, traffic behavior, and communications mistakes Identify significant structures in your network with graph analysis Examine insider threat data and acquire threat intelligence Map your network and identify significant hosts within it Work with operations to develop defenses and analysis techniques

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Bayesian Data Analysis, Third Edition

This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book's website includes solutions to the problems, data sets, software advice, and other ancillary material.

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Pandas for Everyone Python Data Analysis 无水印pdf

Pandas for Everyone Python Data Analysis 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除

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Bayesian Data Analysis, Third Edition 【百度云】

This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book's website includes solutions to the problems, data sets, software advice, and other ancillary material.

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