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UNDERSTAND RELATIONSHIPS BETWEEN VARIABLES

Overview

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Looking to learn how to build data analytics models or discover the relationships between variables?

 

In the Statistics for Data Analytics zyBook, you will learn statistics with a focus on regression analysis using R and develop an understanding of the relationships between data, regression model building, and model selection.

Research Proven Excellence

Preferred Choice

80% of surveyed students preferred zyBooks over regular textbooks.

Better Learning

Studies show that students learn 118% more with the minimal text model in only one lesson.

Proven Results

On average, zyBook users improved grades up to ⅔ and read 74% more than users of regular textbooks.

What is a zyBook?

In short, zyBooks are interactive, digital textbooks.

 

By incorporating available technology, zyBooks make learning fun, interactive and engaging – all while drastically reducing the time spent on learning theoretical concepts.

Course Details

The Statistics for Data Analytics zyBook covers statistical concepts needed for data analytics (specifically, regression analysis) with minimal text, maximum interactivity.

Module 1: How to use a zyBook


1.1 Basics 1.2 Account and platform basics 1.3 Feedback




Module 2: Exploratory Data Analysis


2.1 What is data? 2.2 What is data visualisation? 2.3 R for data visualisation 2.4 Data frames 2.5 Scatter plots 2.6 Box plots 2.7 Histograms




Module 3: Parametric Analysis


3.1 Normal distribution 3.2 Student's t-Distribution 3.3 F-distribution 3.4 Chi-square distribution 3.5 Confidence intervals 3.6 Confidence intervals for population means 3.7 Hypothesis testing 3.8 Hypothesis test for a population mean 3.9 Hypothesis test for the difference between two population means 3.10 Chi-square tests for categorical variables 3.11 One-way analysis of variance (one-way ANOVA)




Module 4: Non-parametric Analysis


4.1 Resampling: Randomisation and bootstrapping 4.2 Wilcoxon rank-sum test 4.3 Kruskal-Wallis test 4.4 Multiple tests




Module 7: Logistic Regression


7.1 Introduction to logistic regression (LR) 7.2 Estimating LR parameters 7.3 LR models with multiple predictors 7.4 LR assumptions and diagnostics 7.5 Testing LR parameters 7.6 Interpreting LR models 7.7 Comparing nested models: Likelihood ratio tests and AIC 7.8 Classification using LR models




Module 6: Multiple Linear Regression


6.1 Introduction to multiple regression 6.2 Multiple regression assumptions and diagnostics 6.3 Coefficient of multiple determination 6.4 Multicollinearity 6.5 Interpreting multiple regression models 6.6 Confidence and prediction intervals for MLR models 6.7 Testing multiple regression parameters 6.8 Multiple regression example




Module 5: Linear Regression


5.1 Introduction to simple linear regression (SLR) 5.2 SLR assumptions 5.3 Correlation and coefficient of determination 5.4 Interpreting SLR models 5.5 Confidence and prediction intervals for SLR models 5.6 Testing SLR parameters




Module 11: Principal Component Analysis


11.1 Introduction to principal component analysis (PCA) 11.2 Calculating principal components for two variables 11.3 Extending PCA to more variables 11.4 Determining the number of components 11.5 Interpreting principal components




Module 10: Stepwise Regression


10.1 Introduction to stepwise regression 10.2 Forward selection 10.3 Backward selection 10.4 Stepwise selection




Module 9: Higher Order Regression


9.1 Interaction terms 9.2 Categorical predictor variables 9.3 Quadratic models 9.4 Complete second order models 9.5 Comparing nested models: F-test 9.6 Higher order models




Module 8: Transformations


8.1 Logarithmic transformations 8.2 Ladder of powers 8.3 Box-Cox transformation




Module 12: Appendix A: Distribution Tables


12.1 t-distribution table 12.2 z-distribution table 12.3 Chi-squared distribution table




Module 13: Appendix B: CSV Files


13.1 Data sets





Pricing

Each purchase comes with...

Highly effective reading materials

Interactive figures & tables

Practice questions

1 year access

What Our Students Say

“I really enjoyed zyBooks for use in my Python class. It has surely aided my success in class and helped me build some confidence in my first year at university.”
 

Isaac C.

Cal State University, Long Beach

Our Other zyBooks & Courses

The Statistics of Data Analytics zyBook will pair well with the following:

Frequently Asked Questions

PAYMENT

Will my course fee be subsidised?


No. If you wish to join a course with course fee support*, please check out our Wiley Certified Data Analyst and Unity Certified Associate courses. *Terms & Conditions apply.









CURRICULUM

I have no prior experience. Will I be able to understand the content taught in the zyBooks?


Yes. Unless otherwise specified, all zyBooks are beginner-friendly.




How much time do I have to spend to complete the course?


It depends on your learning style and speed. We estimate taking about 48 hours to read through the material and complete the interactive quizzes at regular speed. Each zyBook comes with 1 year access, and you should have more than enough time to complete and revise the material with regular reading.




Is there a certification examination?


No.





ZYBOOKS

What is a zyBook?


zyBooks are like interactive, digital textbooks. By incorporating available technology, zyBooks make learning fun, interactive and engaging – all while drastically reducing the time spent on reading long texts.




Why should I buy a zyBook instead of a regular textbook?


zyBooks comprise the same content as a textbook but with minimal text and maximum interactivity. Interactive charts and quizzes are strategically placed, allowing students to cement newly learnt concepts.




Do all the zyBooks you offer come with zyLabs (the interactive lab environment)?


No. If zyLabs are included, it will be specified in the course page.