This chapter introduced three well known nonlinear regression models: the logistic, Poisson, and negative binomial models, and you became familiar with the general logic of modeling. It was also shown how the same concepts, such as effect of predictors, goodness of fit, explanative power, model comparison for nested and non-nested models, and model building are applied in different contexts. Now, having spent some time on mastering the data analysis skills, in the next chapter, we will get back to some hardcore data science problems, such as the cleansing and structuring of data.
Mastering Data analysis with R
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Mastering Data analysis with R
By:
Overview of this book
Table of Contents (19 chapters)
Mastering Data Analysis with R
Credits
www.PacktPub.com
Preface
Free Chapter
Hello, Data!
Getting Data from the Web
Filtering and Summarizing Data
Restructuring Data
Building Models (authored by Renata Nemeth and Gergely Toth)
Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
Unstructured Data
Polishing Data
From Big to Small Data
Classification and Clustering
Social Network Analysis of the R Ecosystem
Analyzing Time-series
Data Around Us
Analyzing the R Community
References
Customer Reviews