Building statistical models in Python develop useful models for regression, classification, time series, and survival analysis
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 dat...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing Ltd.
2023
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| Edition: | 1st edition |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Includes bibliographical references and index
- 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
- 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
- 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
- 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
- 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