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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Preparing the Data 2. Exploring the Data FREE CHAPTER 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

Using OLS to forecast how much electricity can be produced


Ordinary Least Squares (OLS) is also a linear model. In fact, a linear regression is estimated using the least squares method as well. The OLS is, however, capable of estimating a model where the relationship between the dependent and independent variables is nonlinear as long as this relationship is linear in parameters.

Getting ready

To execute this recipe, you will need pandas and Statsmodels. No other prerequisites are required.

How to do it…

We will, as always, wrap our model estimation efforts in a function (the regression_ols.py file):

import statsmodels.api as sm

@hlp.timeit
def regression_ols(x,y):
    '''
        Estimate a linear regression
    '''
    # add a constant to the data
    x = sm.add_constant(x)

    # create the model object
    model = sm.OLS(y, x)

    # and return the fit model
    return model.fit()

# the file name of the dataset
r_filename = '../../Data/Chapter6/power_plant_dataset_pc.csv'

# read the data...
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