Book Image

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
Book Image

Building Statistical Models in Python

By: Huy Hoang Nguyen, Paul N Adams, Stuart J Miller

Overview of this book

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.
Table of Contents (22 chapters)
1
Part 1:Introduction to Statistics
7
Part 2:Regression Models
10
Part 3:Classification Models
13
Part 4:Time Series Models
17
Part 5:Survival Analysis

Chi-square goodness-of-fit

The chi-square goodness-of-fit test compares the count of occurrences of multiple factor levels for a single variable (factor) to determine whether the levels are statistically equal. For example, a vendor offers three models of phones – three levels (brands) of the single factor (phone) – to customers, who purchase in total an average of 90 phones per week. We can say the expected frequency is 1/3 – so, 30 phones of each model are sold per week, on average. Pearson’s chi-square test statistic, which is calculated by measuring the observed frequencies against expected frequencies, is the test statistic used for the chi-square goodness-of-fit test. The linear equation for this test statistic is as follows:

χ 2 = (O i E i) 2 _ E i , degrees of freedom = k-1

Where O i is the observed frequency, E i, is the expected frequency, and k is the number of factor...