Building Statistical Models in Python : Develop Useful Models for Regression, Classification, Time Series, and Survival Analysis / Huy Hoang Nguyen, Paul N. Adams, and Stuart J. Miller.

Format:

Book

Author/Creator:

Nguyễn, Huy Hoàng, TS., author.

Adams, Paul N., author.

Miller, Stuart J., 1938- author.

Language:

English

Subjects (All):

Mathematical statistics.

Mathematical models.

Python (Computer program language).

Statistics.

Physical Description:

1 online resource (420 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2023]

Biography/History:

Nguyen Huy Hoang: Huy Hoang Nguyen is a Mathematician and a Data Scientist with far-ranging experience, championing advanced mathematics and strategic leadership, and applied machine learning research. He holds a Master's in Data Science and a PhD in Mathematics. His previous work was related to Partial Differential Equations, Functional Analysis and their applications in Fluid Mechanics. He transitioned from academia to the healthcare industry and has performed different Data Science projects from traditional Machine Learning to Deep Learning. Adams Paul N: Paul Adams is a Data Scientist with a background primarily in the healthcare industry. Paul applies statistics and machine learning in multiple areas of industry, focusing on projects in process engineering, process improvement, metrics and business rules development, anomaly detection, forecasting, clustering and classification. Paul holds a Master of Science in Data Science from Southern Methodist University. Miller Stuart J: Stuart Miller is a Machine Learning Engineer with degrees in Data Science, Electrical Engineering, and Engineering Physics. Stuart has worked at several Fortune 500 companies, including Texas Instruments and StateFarm, where he built software that utilized statistical and machine learning techniques. Stuart is currently an engineer at Toyota Connected helping to build a more modern cockpit experience for drivers using machine learning.

Summary:

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.

Contents:

Cover

Copyright

Contributors

Table of Contents

Preface

Part 1: Introduction to Statistics

Chapter 1: Sampling and Generalization

Software and environment setup

Population versus sample

Population inference from samples

Randomized experiments

Observational study

Sampling strategies - random, systematic, stratified, and clustering

Probability sampling

Non-probability sampling

Summary

Chapter 2: Distributions of Data

Technical requirements

Understanding data types

Nominal data

Ordinal data

Interval data

Ratio data

Visualizing data types

Measuring and describing distributions

Measuring central tendency

Measuring variability

Measuring shape

The normal distribution and central limit theorem

The Central Limit Theorem

Bootstrapping

Confidence intervals

Standard error

Correlation coefficients (Pearson's correlation)

Permutations

Permutations and combinations

Permutation testing

Transformations

References

Chapter 3: Hypothesis Testing

The goal of hypothesis testing

Overview of a hypothesis test for the mean

Scope of inference

Hypothesis test steps

Type I and Type II errors

Type I errors

Type II errors

Basics of the z-test - the z-score, z-statistic, critical values, and p-values

The z-score and z-statistic

A z-test for means

z-test for proportions

Power analysis for a two-population pooled z-test

Chapter 4: Parametric Tests

Assumptions of parametric tests

Normally distributed population data

Equal population variance

T-test - a parametric hypothesis test

T-test for means

Two-sample t-test - pooled t-test

Two-sample t-test - Welch's t-test

Paired t-test

Tests with more than two groups and ANOVA

Multiple tests for significance

ANOVA.

Pearson's correlation coefficient

Power analysis examples

Chapter 5: Non-Parametric Tests

When parametric test assumptions are violated

Permutation tests

The Rank-Sum test

The test statistic procedure

Normal approximation

Rank-Sum example

The Signed-Rank test

The Kruskal-Wallis test

Chi-square distribution

Chi-square goodness-of-fit

Chi-square test of independence

Chi-square goodness-of-fit test power analysis

Spearman's rank correlation coefficient

Part 2: Regression Models

Chapter 6: Simple Linear Regression

Simple linear regression using OLS

Coefficients of correlation and determination

Coefficients of correlation

Coefficients of determination

Required model assumptions

A linear relationship between the variables

Normality of the residuals

Homoscedasticity of the residuals

Sample independence

Testing for significance and validating models

Model validation

Chapter 7: Multiple Linear Regression

Multiple linear regression

Adding categorical variables

Evaluating model fit

Interpreting the results

Feature selection

Statistical methods for feature selection

Performance-based methods for feature selection

Recursive feature elimination

Shrinkage methods

Ridge regression

LASSO regression

Elastic Net

Dimension reduction

PCA - a hands-on introduction

PCR - a hands-on salary prediction study

Part 3: Classification Models

Chapter 8: Discrete Models

Probit and logit models

Multinomial logit model

Poisson model

The Poisson distribution

Modeling count data

The negative binomial regression model

Negative binomial distribution

Chapter 9: Discriminant Analysis

Bayes' theorem

Probability

Conditional probability.

Discussing Bayes' Theorem

Linear Discriminant Analysis

Supervised dimension reduction

Quadratic Discriminant Analysis

Part 4: Time Series Models

Chapter 10: Introduction to Time Series

What is a time series?

Goals of time series analysis

Statistical measurements

Mean

Variance

Autocorrelation

Cross-correlation

The white-noise model

Stationarity

Chapter 11: ARIMA Models

Models for stationary time series

Autoregressive (AR) models

Moving average (MA) models

Autoregressive moving average (ARMA) models

Models for non-stationary time series

ARIMA models

Seasonal ARIMA models

More on model evaluation

Chapter 12: Multivariate Time Series

Multivariate time series

Time-series cross-correlation

ARIMAX

Preprocessing the exogenous variables

Fitting the model

Assessing model performance

VAR modeling

Step 1 - visual inspection

Step 2 - selecting the order of AR(p)

Step 3 - assessing cross-correlation

Step 4 - building the VAR(p,q) model

Step 5 - testing the forecast

Step 6 - building the forecast

Part 5: Survival Analysis

Chapter 13: Time-to-Event Variables - An Introduction

What is censoring?

Left censoring

Right censoring

Interval censoring

Type I and Type II censoring

Survival data

Survival Function, Hazard and Hazard Ratio

Chapter 14: Survival Models

Kaplan-Meier model

Model definition

Model example

Exponential model

Cox Proportional Hazards regression model

Step 1

Step 2

Step 3

Step 4

Step 5

Index

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Notes:

Includes index.

Description based on print version record.

ISBN:

1-80461-215-4

OCLC:

1396227320