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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Summarizing linear model fits

The summary function can be used to obtain the formatted coefficient, standard errors, degree of freedom, and other summarized information of a fitted model. This recipe introduces how to obtain overall model information by using the summary function.

Getting ready

You need to have completed the previous recipe by fitting the house rental data into a regression model, and have the fitted model assigned to the variable lmfit.

How to do it…

Perform the following steps to summarize the linear regression model:

  1. Compute a detailed summary of the fitted model, lmfit:
    > summary(lmfit)
    Call:
    lm(formula = Price ~ Sqft, data = house)
    
    Residuals:
       Min     1Q Median     3Q    Max
    -76819 -12388  -3093  10024 112227
    
    Coefficients:
                Estimate Std. Error t value Pr(>|t|)
    (Intercept) 3425.133   1766.646   1.939    0.053 .
    Sqft          38.334      1.034  37.090   <2e-16 ***
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05...
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