Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS), a subsampling method based on energy distance, is employed for efficient double machine learning (DML) in causal inference. The support points are selected and split as optimal representative points of the full raw data in a random sample, in contrast to the traditional random splitting, and providing an optimal sub-representation of the underlying data generating distribution. They offer the best representation of a full big dataset, whereas the unit structural information of the underlying distribution via the traditional random data splitting is most likely not preserved. Three machine learning estimators were adopted for causal inference, support vector machine (SVM), deep learning (DL), and a hybrid super learner (SL) with deep learning (SDL), using SPSS. A comparative study is conducted between the proposed SVM, DL, and SDL representations using SPSS, and the benchmark results from Chernozhukov et al. (2018), which employed random forest, neural network, and regression trees with a random k-fold cross-fitting technique on the 401(k)-pension plan real data. The simulations show that DL with SPSS and the hybrid methods of DL and SL with SPSS outperform SVM with SPSS in terms of computational efficiency and the estimation quality, respectively.

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Logistic Regression in SPSS for Social Science Research Complete step by step guide on logistic regression in SPSS including interpretation and visualization New What you'll learn Social research with Logistic Regression in SPSS: A Complete Guide for the Social Sciences The only course on Udemy that shows you how to perform, interpret and visualize logistic regression in SPSS, using a real world example, using the quantitative research process. Follow along with me as I talk you through everything you need to know to become confident in using regression analysis in your quantitative research report, dissertation or thesis. Perfect for those studying social science subjects or want to increase their statistical confidence and literacy. Don't fall for other courses that are over-technical, math's based and heavy on statistics! This course cuts all that out and explains in a way that is easy to understand! Course outcomes On completion of the course you will fully understand: Logistics regression is a statistical model that is used to predict the probability of a certain outcome or event occurring, when that outcome or event is binary (such as pass/fail, true/false, healthy/sick).

Getting Started Udemy SPSS For Research SPSS is short for Statistical Package for the Social Sciences, and it's used by various kinds of researchers for complex statistical data analysis by Bogdan Anastasiei What you'll learn perform simple operations with data: define variables, recode variables, create dummy variables, select and weight cases, split files built the most useful charts in SPSS: column charts, line charts, scatterplot charts, boxplot diagrams perform the basic data analysis procedures: Frequencies, Descriptives, Explore, Means, Crosstabs test the hypothesis of normality (with numeric and graphic methods) detect the outliers in a data series (with numeric and graphic methods) perform the main one-sample analyses: one-sample t test, binomial test, chi square for goodness of fit perform the tests of association: Pearson and Spearman correlation, partial correlation, chi square test for association, loglinear analysis Description Become an expert in statistical analysis with the most extended SPSS course at Udemy: 146 video lectures covering about 15 hours of video! Within a very short time you will master all the essential skills of an SPSS data analyst, from the simplest operations with data to the advanced multivariate techniques like logistic regression, multidimensional scaling or principal component analysis. The good news – you don't need any previous experience with SPSS. If you know the very basic statistical concepts, that will do. And you don't need to be a mathematician or a statistician to take this course (neither am I).

Linear regression is one of the essential tools in statistical analysis. In this course, we'll walk through step-by-step how to conduct many important analyses using SPSS. Although you will learn the basics of what these statistics are, we'll avoid complicated mathematical discussions and go right to what you need to know to conduct these analyses. Linear regression is basically a tool that allows you to test relationships between many variables at the same time, control for variables' effects, and create simple statistical models that allow you to make predictions. In this course, we'll cover the following key topics: You'll not only learn how to conduct these analyses, we'll also go over how to interpret the statistical results and how to graph the results using SPSS and a special Excel template I've created for you.

This is meant to be a practical book. The author's "objective is to provide a thorough discussion of the most useful data-mining tools that goes beyond the typical'black box' description, and to show why these tools work". I think the result of reading and doing the exercises in this book is: 1. I will have acquired some familiarity with regression techniques and a few of the problems they can help with 2. I will have performed the regression techniques in R Over half the text focuses on various kinds of regression. Then there is a little bit on classification, decision trees, clustering, principal components analysis.

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