Into the Unknown
Statistics with Python University of Michigan Python itself is more general purpose, while there are many other programming languages focus on statistics. However given the fact that Python has emerged as the undisputed champion in artificial intelligence and machine learning, which are so closed to statistics, learning statistics with Python is definitely worth the effort…
Fitting Statistical Models to Data with Python University of Michigan AI is combination of many different fields. Statistics definitely belongs to the group of the most fundamental ones. There are so many online courses, I never saw two courses that are teaching identical things. I can always learn something new. Before this course, I’ve never…
Logistic Regression Logistic regression is an extension to linear regression. When there are only only 2 options for the predicted values, like failed (0) or success (1), linear regression model is not appropriate. Instead, we will predict a transformed version of the probability of success. It is call the logit transformation where the logit function…
The purpose of fitting models to data is to help answer research questions. We specify models based on theory or subject matter knowledge and then we fit these models to the data that we’ve collected, assuming that the variables in our dataset follow distributions or have certain relationships. The models that we fit to the…
Inferential Statistical Analysis with Python University of Michigan Simply put, this course teaches 2 things: confidence intervals and hypothesis testing. Both of them are so fundamental that a solid grasp is critical. Bear in mind that calculating the mean or percentage value from a sample is not the whole story. The range of values you…
Typically we have some question, and that question could be “could the value of the parameter be X?” We could have some idea of what it could be but we’re not sure if that’s correct or not. Then, with that question we use data to try and support that claim or maybe go against that…
We use data to do statistical inference means we either estimate some parameters with confidence, or test some theories or hypothesis about those parameters. Significance Level Consider an example, suppose we have 2 hypothesis: the Null and the Alternative. Also we have come up with a decision rule: after taking an observation of one sample…
Understanding and Visualizing Data with PythonUniversity of Michigan This is a fantastic statistics course taught in the setting of Python programming language. It astonishes me that the professor did a cartwheel at the beginning, very impressive. The course mainly focus on the fundamentals of statistics. After the lecture of concepts, there are hands-on videos tutoring…
In the 1930s, Jerzy Nayman and others made some very important breakthroughs in this area and his work enabled researchers to use random sampling as a technique to measure populations. It means we did not have to measure every single unit in the population to make statements about the population (inference). The first important step…
Statistics: Arts and Sciences Statistics is the subject that encompasses all aspects of learning from data. We are talking about tools and methods to allow us to work with data to understand that data. Statisticians apply and develop data analysis methods seeking to understand their properties. Researchers and workers apply and extend statistical methodology. Statistics…