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2017 Directors Report

Research Methods: Regression Analysis with Python

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Description

 

In this course you will learn how to use Python with the most salient libraries to gain data driven insights into typical research problems from various disciplines. This course is not a complete beginners’ course, yet it allows you to enter with just some fairly-basic statistics knowledge. This course is also not meant to comprehensively cover programming and algorithms, yet we will introduce you to some important concepts and show how these can be applied for novel approaches to your data. 
The course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using the freely available, easy-to-learn, yet powerful Python language, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you


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When


1-3 April 2019


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Who Should Attend


Researchers who would like to use Python.
It is not a complete beginners’ course, yet it allows you to enter with just some fairly-basic statistics knowledge. This course is also not meant to comprehensively cover programming and algorithms, yet we will introduce you to some important concepts and show how these can be applied for novel approaches to your data


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Benefits of Attendance


You will learn
• How to apply regression/Python to your data 
• Easy and hands-on approach to Python as programming language for your tasks
• Create valid regression models
• How to interpret the outcome
• Beautiful visualizations of the data 
• How to setup and maintain an easy to work environment on your computer
• Apply your knowledge to real-world problems in afternoon lab sessions
• Tips and tricks and lots of networking…


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Location


Worcester College of the University of Oxford
1 Walton St, Oxford OX1 2HB, UK


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Speakers


Prof Dr Othmar M Lehner
Austria & London
ACRN Director

 

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Provider Information


ACRN Oxford Research Network

1 King's Meadow, Osney Mead
OX2 0DP
Oxford
UK


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Additional Information


Please bring your own laptop


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Contact


ACRN Oxford Research Network
seeds@acrn.eu 


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Event Price

EU academic rate GBP 660,-
standard fee GBP 920,-

Travel, accommodation and food are not included in the course fee! 


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Book Now



The booking deadline of this event is: 20 January 2019

To book your place please go to: http://www.acrn.eu/courses/application/ 

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1st April 2019   to   3rd April 2019
Event Information
Event Provider ACRN Oxford Research Network
Date / Time 1-3 April 2019
Venue Worcester College of the University of Oxford 1 Walton St, Oxford OX1 2HB, UK
Event Type Lecture
Target Audience Doctoral Students, Post Doctoral Fellows, Early/Mid Career Academics, Senior Academics, Directors of Research, Deans/Heads of Departments
Price
Booking Deadline 20th January 2019
Link to Booking Form http://www.acrn.eu/courses/application/
Contact ACRN Oxford Research Network seeds@acrn.eu
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