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231010 ||| eng |
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4 |
|a QA276
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| 100 |
1 |
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|a Nguyen, Huy Hoang
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| 245 |
0 |
0 |
|a Building statistical models in Python
|b develop useful models for regression, classification, time series, and survival analysis
|c Huy Hoang Nguyen, Paul N. Adams, Stuart J. Miller
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| 250 |
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|a 1st edition
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| 260 |
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|a Birmingham, UK
|b Packt Publishing Ltd.
|c 2023
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| 300 |
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|a 420 pages
|b illustrations
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| 505 |
0 |
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|a Includes bibliographical references and index
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| 505 |
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|a 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 -- Summary -- References -- Chapter 3: Hypothesis Testing -- The goal of hypothesis testing -- Overview of a hypothesis test for the mean -- Scope of inference
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| 505 |
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|a Spearman's rank correlation coefficient -- Summary -- 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 -- Summary -- Chapter 7: Multiple Linear Regression -- Multiple linear regression
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| 505 |
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|a 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
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| 505 |
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|a Tests with more than two groups and ANOVA -- Multiple tests for significance -- ANOVA -- Pearson's correlation coefficient -- Power analysis examples -- Summary -- References -- 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
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| 505 |
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|a 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 -- Summary -- 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
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| 653 |
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|a statistics / aat
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| 653 |
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|a Mathematical models / http://id.loc.gov/authorities/subjects/sh85082124
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| 653 |
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|a Statistics / http://id.loc.gov/authorities/subjects/sh85127580
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Mathematical statistics / fast / (OCoLC)fst01012127
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| 653 |
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|a Statistique
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| 653 |
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|a Python (Computer program language) / fast / (OCoLC)fst01084736
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| 653 |
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|a Statistics / fast / (OCoLC)fst01132103
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| 653 |
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|a Mathematical models / fast / (OCoLC)fst01012085
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| 653 |
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|a Mathematical statistics / http://id.loc.gov/authorities/subjects/sh85082133
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| 700 |
1 |
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|a Adams, Paul N.
|e author
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| 700 |
1 |
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|a Miller, Stuart J.
|e author
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9781804614280
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781804614280/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.5
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| 082 |
0 |
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|a 519.5
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| 520 |
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|a 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
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