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Learn the art of regression analysis with Python About This Book Become competent at implementing regression analysis in Python Solve some of the complex data science problems related to predicting outcomes Get to grips with various types of regression for effective data analysis Who This Book Is For The book targets Python developers, with a basic understanding of data science, statistics, and math, who want to learn how to do regression analysis on a dataset. It is beneficial if you have some knowledge of statistics and data science. What You Will Learn Format a dataset for regression and evaluate its performance Apply multiple linear regression to real-world problems Learn to classify training points Create an observation matrix, using different techniques of data analysis and cleaning Apply several techniques to decrease (and eventually fix) any overfitting problem Learn to scale linear models to a big dataset and deal with incremental data In Detail Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer. Style and approach This is a practical tutorial-based book. You will be given an example problem and then supplied with the relevant code and how to walk through it. The details are provided in a step by step manner, followed by a thorough explanation of the math underlying the solution. This approach will help you leverage your own data using the same techniques.

Python (Computer program language) --- Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Scripting languages (Computer science)

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Distribution (Probability theory) --- Regression analysis. --- Mathematical statistics. --- Regression analysis --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistical methods

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Mathematical statistics --- #SBIB:303H522 --- #SBIB:303H523 --- #SBIB:303H10 --- Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Methoden sociale wetenschappen: handboeken statistische analyse --- Methoden sociale wetenschappen: associatie, correlatie --- Methoden en technieken: algemene handboeken en reeksen --- Regression Analysis --- Regression analysis

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This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs. Provides insight into modeling of nonlinear relationships and also justifications for when to use them, thereby providing novel insights about relationships Addresses not only adaptive generation of additive models but also of models based on nonlinear interactions Discusses adaptive modeling of variances/dispersions as well as of means Highlights both univariate and multivariate outcomes, rather than solely univariate outcomes.

Statistics. --- Biostatistics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical Theory and Methods. --- Regression analysis. --- Nonlinear theories. --- Medicine --- Mathematical statistics. --- Research --- Statistical methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Nonlinear problems --- Nonlinearity (Mathematics) --- Analysis, Regression --- Linear regression --- Regression modeling --- Statistical methods --- Statistics --- Probabilities --- Sampling (Statistics) --- Calculus --- Mathematical analysis --- Mathematical physics --- Multivariate analysis --- Structural equation modeling --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Health Workforce --- Statistics . --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics