Data science, also known as data-driven decision, is an interdisciplinery field about scientific methods, process and systems to extract knowledge from data in various forms, and take descision based on this knowledge. A data scientist should not only be evaluated only on his/her knowledge on mahine learning, but he/she should also have good expertise on statistics. I will try to start from very basics of data science and then slowly move to expert level. So let’s get started.
I have a major pet peeve that I need to confess. I go insane when I hear programmers talking about statistics like they know shit when it’s clearly obvious they do not. I’ve been studying it for years and years and still don’t think I know anything. This article is my call for all programmers…
JASP is an open-source statistics program that is free, friendly, and flexible. Armed with an easy-to-use GUI, JASP allows both classical and Bayesian analyses.
The %ITEM macro computes descriptive statistics for analysis of data from a multiple-choice test. Each observation contains the answers from one subject to a set of questions ("items"). The data are compared to an answer key to determine which answers are correct. The score for each subject is computed as the number of correct answers. The output is very similar to that from the ITEM procedure in the SUGI Supplemental library, but several incorrect statistics have been fixed.
NOTE: Beginning in SAS 9.4, this macro is no longer needed. Use the OUTPLC= option in Base SAS PROC CORR to save a matrix of polychoric (or tetrachoric) correlations.
PURPOSE:
The %POLYCHOR macro creates a SAS data set containing a correlation matrix of polychoric correlations or a distance matrix based on polychoric correlations.
This sample combines macro programming with PROC FREQ and DATA Step logic to count the number of missing and non-missing values for every variable in a data set. The results are stored in a data set.
This sample illustrates one method of counting the number of missing and non-missing values for each variable in a data set. Two methods for structuring the resulting data set are shown.
The SELECT macro performs model selection methods for categorical-response models that can be fit in PROC LOGISTIC. These include models using the logit, probit, cloglog, cumulative logit, or generalized logit links. The macro supports binary as well as ordinal and nominal multinomial models.
Standard model selection is done by choosing candidate effects for entry to or removal from the model according to their significance levels. After completion, the set of models selected at each step of this process is sorted on the selected criterion - AUC, R-square, max-rescaled R-square, AIC, or BIC. The requested number of best models on the selected criterion is displayed.
NOTE: Beginning in SAS 9.2, the QIC statistic is produced by PROC GENMOD. Beginning in SAS 9.4 TS1M2, QIC is available in PROC GEE.
PURPOSE:
The %QIC macro computes the QIC and QICu statistics proposed by Pan (2001) for GEE (generalized estimating equations) models. These statistics allow comparisons of GEE models (model selection) and selection of a correlation structure.
This article is divided into three parts: the first part explains the definition of the economically dependent self-employed and proposes ideas for improving this definition of this dependency. The second part of this article is dedicated to the working conditions of the self-employed, while the last part compares the job satisfaction of the self-employed, employees and family workers.
D. Hogg, and S. Villar. (2021)cite arxiv:2101.07256Comment: all code used to make the figures is available at https://github.com/davidwhogg/FlexibleLinearModels.
M. Lindvall, and J. Molin. (2020)cite arxiv:2001.07455Comment: Accepted for presentation in poster format for the ACM CHI'19 Workshop <Emerging Perspectives in Human-Centered Machine Learning>.
T. Junk, and L. Lyons. (2020)cite arxiv:2009.06864Comment: 50 pages, 6 figures. Please see https://hdsr.mitpress.mit.edu/pub/32yz0u49/release/1 for a thoughtful comment by Andrew Fowlie, and https://hdsr.mitpress.mit.edu/pub/57tywz64/release/1 for the authors' response.
S. Dias, and D. Caldwell. Archives of Disease in Childhood - Fetal and Neonatal Edition, 104 (1):
F8-F12(January 2019)Network meta-analysis; Mètodes bayessians; Introductori.
A. Joseph. (2019)cite arxiv:1912.10997Comment: 122 pages, 34 figures, several appendices. These lecture notes are based on the three lectures given at the 2019 Joburg School in Theoretical Physics: Aspects of Machine Learning, Mandelstam Institute for Theoretical Physics, The University of the Witwatersrand, Johannesburg, South Africa (November 11 - 15, 2019).
A. Rothkopf. (2019)cite arxiv:1903.02293Comment: 12 pages, 4 figures, talk given at the XIIIth Quark Confinement and the Hadron Spectrum Conference 2018, Maynooth, Ireland.
A. Koshiyama, and N. Firoozye. (2019)cite arxiv:1905.05023Comment: Large portions of this work appeared previously in a replacement of arXiv:1901.01751 (version 2) which was uploaded there by mistake.