Applied Statistics with Data Analytics (Python) Cover Art

Table of Contents

1.1 What is data?
1.2 What is data visualization?
1.3 Python for data visualization
1.4 Data frames
1.5 Bar charts
1.6 Pie charts
1.7 Scatter plots
1.8 Line charts
1.9 Data visualization example

2.1 What is statistics?
2.2 Measures of center
2.3 Measures of variability
2.4 Box plots
2.5 Histograms

3. Probability and Counting
3.1 Introduction to probability
3.2 Addition rule and complements
3.3 Multiplication rule and independence
3.4 Conditional probability
3.5 Bayes’ Theorem
3.6 Combinations and permutations

4.1 Introduction to random variables
4.2 Properties of discrete probability distributions
4.3 Binomial distribution
4.4 Hypergeometric distribution
4.5 Poisson distribution
4.6 Properties of continuous probability distributions
4.7 Normal distribution
4.8 Student’s t-Distribution
4.9 F-distribution
4.10 Chi-square distribution

5.1 Confidence intervals
5.2 Confidence intervals for population means
5.3 Confidence intervals for population proportions
5.4 Hypothesis testing
5.5 Hypothesis test for a population mean
5.6 Hypothesis test for a population proportion
5.7 Hypothesis test for the difference between two population means
5.8 Hypothesis test for the difference between two population proportions
5.9 One-way analysis of variance (one-way ANOVA)

6.1 Introduction to simple linear regression (SLR)
6.2 SLR assumptions
6.3 Correlation and coefficient of determination
6.4 Interpreting SLR models
6.5 Confidence and prediction intervals for SLR models
6.6 Testing SLR parameters
6.7 Multiple regression
6.8 Categorical predictor variables
6.9 Interaction terms
6.10 Linear regression example

7.1 Categorical data
7.2 Fisher’s exact test
7.3 Introduction to chi-square tests
7.4 Chi-square test for homogeneity and independence
7.5 Relative risk and odds ratios

8.1 What is data mining?
8.2 Data formats
8.3 Machine learning methods
8.4 sci-kit learn

9.1 What is data cleansing?
9.2 Handling missing values
9.3 Outliers
9.4 Standardization and normalization
9.5 Dimensionality reduction
9.6 Training, validation, and test sets

10.1 k nearest neighbors
10.2 Logistic regression
10.3 Evaluating classification models
10.4 Supervised learning examples

11.1 Clustering methods
11.2 Association rules
11.3 Evaluating clustering models
11.4 Unsupervised learning examples

12.1 Introduction to decision trees
12.2 Classification and regression trees (CART)
12.3 ID3 and C4.5 algorithms
12.4 Classification tree example
12.5 Regression tree example
12.6 Random forests

13.1 Misleading statistics
13.2 Abuse of the p-value
13.3 Data privacy
13.4 Ethical guidelines

14.1 t-distribution table
14.2 z-distribution table
14.3 Chi-squared distribution table

15.1 Data sets

16.1 Violin plots
16.2 What is a time series?
16.3 Time series patterns and stationarity
16.4 Moving average and exponential smoothing forecasting
16.5 Forecasting using regression
16.6 What is a Monte Carlo simulation?
16.7 Building simulations
16.8 Optimization and forecasting
16.9 What-if analysis
16.10 Advanced simulations

What You’ll Find In This zyBook:

More action with less text.

  • An exceptionally student-focused introduction to applied statistics.
  • Traditionally difficult topics are made easier using animations and learning questions.
  • Several chapters on data analytics and data mining algorithms are included.
  • Python coding environments are provided throughout to allow students to experiment.
  • Auto-graded programming activities are included using a built-in programming environment.
  • Commonly combined with “Applied Regression Analysis” with numerous configurations possible.

Instructors: Interested in evaluating this zyBook for your class? Sign up for a Free Trial and check out the first chapter of any zyBook today!

The zyBooks Approach

Less text doesn’t mean less learning.

This zyBook provides a concise introduction to bivariate and multivariate statistics using an applied approach with real-world data. Equations for common statistical quantities are provided, but most concepts are explained using animations rather than rigorous mathematical proof. This content is recommended for STEM majors who may not have a solid foundation on statistics, but want a friendly introduction to data analytics. Python coding environments are provided that allows students to experiment with datasets that are both interesting and relevant to students’ day-to-day lives.

“The most striking aspect of ZyBooks for me as an instructor has been the ability to introduce a topic and then point my students to specific exercises/activities in ZyBooks that would not only expound on the concept but allow them to practice them with confidence.”

Senior Contributors

Joel Berrier
Assistant Professor, Dept. of Physics and Astronomy, Univ. of Nebraska, Kearny, Ph.D. Physics and Astronomy, UC Irvine

Chris Chan
Content lead: Mathematics, zyBooks, M.A. Mathematics, San Francisco State Univ.

Scott Nestler
Associate Teaching Professor, Mendoza College of Business, Univ. of Notre Dame, Ph.D. Management Science, Univ. of Maryland, College Park

Iain Pardoe
Mathematics and Statistics Instructor, Thompson Rivers Univ., Pennsylvania State Univ., and, PhD Statistics, Univ. of Minnesota

Ron Siu
Content developer, zyBooks, M.S. Biomedical Engineering, UCLA; M.S. Developmental Biology, Stanford

Rodney X. Sturdivant
Professor, Dept. of Mathematics and Physics, Azusa Pacific Univ., Ph.D. Biostatistics, U Mass Amherst

Krista Watts
Assistant Professor, Director—Center for Data Analysis and Statistics, United States Military Academy, West Point, Ph.D. Biostatistics, Harvard