複数の診断値の比較と組み合わせ

Learn why Propensity Score Matching analysis is so popular and what its advantages are. Also, use it to easily create tables and graphs that can be used immediately when submitting a paper.

Visualize and present the overflowing public data skillfully using the most common and familiar Excel and PowerPoint.

We will talk about the contents covered in general statistics lectures in a storytelling manner. You can use R, a free program, as easily as Excel, and easier than SPSS.

If you follow the pivot table, you will soon become an expert. Once you know it, you will regret the times when you struggled because you didn't know. Why didn't you know something this easy (actual reaction)

This is the content that has always been well-received while lecturing to civil servants for many years. This lecture was prepared with a sense of mission to increase the work efficiency of excellent and outstanding civil servants.

R shiny allows you to implement R's infinite functions on a web app in the simplest way possible, making it easy to learn and use.

As you follow along with fun, you will naturally learn the basic concepts and techniques. You will be able to easily create visualization tools that are accurate, clear, pretty, and attractive. You will naturally understand the features and advantages of each from Excel 2007 to 2016, and learn what advantages and disadvantages they have. You will learn how to make the impossible in Excel indirectly.

Learn why Propensity Score Matching analysis is so popular and what its advantages are. Also, use it to easily create tables and graphs that can be used immediately when submitting a paper.

We will talk about the contents covered in general statistics lectures in a storytelling manner. You can use R, a free program, as easily as Excel, and easier than SPSS.

Were you a math-phobe during your school days? It's said that math is important in AI, but do you feel lost on how to approach it? The reason math is important in AI is because math is used in the computational processes of AI (Machine Learning and Deep Learning). For easy explanations of differentiation, linear algebra, and probability and statistics to understand AI, it is structured with simple explanations using graphs (coordinates) rather than mathematical derivations.

This course is for those who want to study machine learning but lack statistical knowledge.

For those of you who are having a hard time with college statistics, a senior who was in the same position will share his know-how. Conquering basic statistics using R programming