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Mastering Data analysis with R

You're reading from   Mastering Data analysis with R Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783982028
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Gergely Daróczi Gergely Daróczi
Author Profile Icon Gergely Daróczi
Gergely Daróczi
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Table of Contents (17) Chapters Close

Preface 1. Hello, Data! 2. Getting Data from the Web FREE CHAPTER 3. Filtering and Summarizing Data 4. Restructuring Data 5. Building Models (authored by Renata Nemeth and Gergely Toth) 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) 7. Unstructured Data 8. Polishing Data 9. From Big to Small Data 10. Classification and Clustering 11. Social Network Analysis of the R Ecosystem 12. Analyzing Time-series 13. Data Around Us 14. Analyzing the R Community A. References Index

How well does the line fit in the data?


Although we know that the trend line is the best fitting among the possible linear trend lines, we don't know how well this fits the actual data. The significance of the regression parameters is obtained by testing the null hypothesis, which states that the given parameter equals to zero. The F-test in the output pertains to the hypothesis that each regression parameter is zero. In a nutshell, it tests the significance of the regression in general. A p-value below 0.05 can be interpreted as "the regression line is significant." Otherwise, there is not much point in fitting the regression model at all.

However, even if you have a significant F-value, you cannot say too much about the fit of the regression line. We have seen that residuals characterize the error of the fit. The R-squared coefficient summarizes them into a single measure. R-squared is the proportion of the variance in the response variable explained by the regression. Mathematically,...

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