Linear & Logistic Regression

Description

This superb-value online course is the ideal training solution for businesses and individuals who need to learn how to build robust linear models and carry out logistic regressions in Excel, R and Python that will stand up to scrutiny when applied to the demands of real world situations.
Who Is The Course For?
You don’t need a statistics background to sign up for the training as all the easy-to-understand lessons use basic maths as their starting point. However, the course will appeal particularly to data analysts who want to progress their skillset from summarising data to explaining and predicting data. The course is also a great training platform for those who wish to pursue a career in data science and other professionals who are required to apply linear regression to solve relevant problems.
How Do I Study?
All learning takes place online, making it the perfect flexible study experience for busy professionals who need to fit the development of their skillset around the demands of their already busy working lives. With no deadlines to meet or weekly class to attend, it’s easy and achievable to stay on track with your learning targets.
KEY LEARNING POINTS
Work through the five hours of clear and concise course content to learn all you need to know to get confident with linear and logistic regression.
Get confident understanding random variables, cause-effect relationships, maximum likelihood estimation and more.
Learn about method of least squares, explaining variance and forecasting an outcome in terms of simple regression.
Find out about residuals and assumptions about residuals when it comes to simple regression.
Explore how to implement simple regression in Excel, R and Python.
Understand how to interpret simple regression results and avoid common pitfalls.
Gain insight into how to implement multiple regression in Excel, R and Python.
Learn how to introduce a categorical variable.
Find out about applications of logistic regression and the link to linear regression and machine learning.
Learn about solving logistic regression using maximum likelihood estimation and linear regression.
Get to grips with extending binominal logistic regression to multinomial logistic regression.
Find out how to implement logistic regression to build a model of stock price movements in Excel, R and Python.
ADVANTAGES OF THIS COURSE
Technical support is available, should it be required.
The models are implemented in Excel, R and Python.
No statistics background is needed to embark on the course – all you need is a knowledge of basic maths.
Not only is the course great value, it’s also a time-effective solution for busy professionals and businesses.
Taking the course is a career-enhancing move that will get you noticed in the competitive and ever-expanding world of data analysis.