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Extending Excel with Python and R

Extending Excel with Python and R

By : Steven Sanderson, David Kun
5 (5)
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Extending Excel with Python and R

Extending Excel with Python and R

5 (5)
By: Steven Sanderson, David Kun

Overview of this book

– Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics. – This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. – Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. – Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level. – By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed.
Table of Contents (20 chapters)
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1
Part 1:The Basics – Reading and Writing Excel Files from R and Python
6
Part 2: Making It Pretty – Formatting, Graphs, and More
10
Part 3: EDA, Statistical Analysis, and Time Series Analysis
14
Part 4: The Other Way Around – Calling R and Python from Excel
16
Part 5: Data Analysis and Visualization with R and Python for Excel Data – A Case Study

Linear regression

Linear regression is a fundamental statistical method used for modeling the relationship between a dependent variable (usually denoted as “Y”) and one or more independent variables (often denoted as “X”). It aims to find the best-fitting linear equation that describes how changes in the independent variables affect the dependent variable. Many of you may know this as the ordinary least squares (OLS) method.

In simpler terms, linear regression helps us predict a continuous numeric outcome based on one or more input features. For this to work, if you are unaware, many assumptions must be held true. If you would like to understand these more, then a simple search will bring you a lot of good information on them. In this tutorial, we will delve into both simple linear regression (one independent variable) and multiple linear regression (multiple independent variables).

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Extending Excel with Python and R
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