In statistics
1. Hierarchical Bayes MCMC
2. Statistical time series analysis (multivariate time series, causality analysis, state change)
3. Causal reasoning (endogenous, difference difference method, propensity score)

There is

I am
Introduction to statistical modeling for data analysis
Time series data analysis ready for use in the field
Statistical science of survey observation data-causal reasoning
I read.

But I'm not sure how to output the above three.

Is there any way to output 1 to 3 statistical skills using publicly available data such as the iris data set?
Also, there is no particular method for output in any form.
If the language is python or R and you know the recommended output method, can you tell me?

  • Answer # 1

    Since reading the book doesn't give you the skills and concepts, I'm very happy that I would like to output it with an actual data set.

    If you don't have any resistance in English, why not join Kaggle's Competitions where you can make use of these technologies?


    I think that it will be possible to use the normal datasets especially for the technologies 1 and 3. Time series data analysis of 2 is natural, but it is impossible unless it is a specialized data set. Personally, I think stock price analysis is a good starting point for time series data analysis. If you do a Google search, you will see various stock price analysis articles in Python such as Qiita articles.

  • Answer # 2

    >There is no particular way to output anything in any way.
    >Language is python or R, can you tell me if you know the recommended output method?

    The easiest way is print ().

    print ("1, Hierarchical Bayes/MCMC")
    print ("2, Statistical time series analysis (multivariate time series, causality analysis, state change)")
    print ("3, causal inference (endogenous, difference difference method, propensity score)")