Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data.
The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing.
The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.
About the Author:
Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineeri
2020-01-03 11:34:39
4.84MB
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数据结构课程的程序设计实践大作业实验报告（C语言版），共三个课题（运动会分数统计+停车场管理系统+校园十大优秀青年评比），内附详细的设计流程思路，及完整的源代码，共需要学习或者交大作业的同学参考。
2020-01-03 11:33:14
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本书由统计学领域著名专家所著，从基本的金融数据出发，讨论了这些数据的汇总统计和相关的可视化方法，之后分别介绍了商业、金融和经济领域中的基本时间序列分析和计量经济模型。作者通过实际操作的方法介绍金融数据分析，选择使用免费的R软件和实际案例来展示书中所讨论方法的实现。书中抽象理论和实际应用并重，读者既能从中轻松学习金融计量模型，也能了解它们在现实世界中的丰富应用。 [1]
2020-01-03 11:31:19
116.41MB
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R语言数据挖掘(NBA数据实战)，以NBA篮球数据为示例，教你如何实现对篮球数据的数据挖掘和分析。初学者慎入。
2020-01-03 11:24:02
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void showMenu() //主菜单界面
{
printf("\n\n-------------===================主界面====================--------------");
printf("\n\n");
printf(" 火车票务管理系统 \n");
printf("\n");
printf(" 1 列车信息录入 \n");
printf("\n");
printf(" 2 列车信息删除 \n");
printf("\n");
printf(" 3 列车信息修改 \n");
printf("\n");
printf(" 4 列车信息输出 \n");
printf("\n");
printf(" 5 列车信息查询 \n");
printf("\n");
printf(" 6 列车票价排序 \n");
printf("-------------------------------------------------------------------------\n");
printf("\n请输入你的选择:");
}
2020-01-03 11:21:09
249KB
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迷宫求解的源码
以一个 m*n 的长方阵表示迷宫，0 和 1 分别表示迷宫中的通路和障碍。设计一个程序，对任意设 定的迷宫，求出一条从入口到出口的通路，或得出没有通路的结论。
2020-01-03 11:19:56
3KB
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