# Welcome to Data Scince

Department of Statistics
Faculty of STEM
Tangerang, Banten
Info: siregarbakti@gmail.com

## Preface

In this book, we will begin to the next level of data science (Beginners part 2). Here, we will focus on computational statistics. The first section, focus on the fundamental of elementary statistics. These topics have to be done before we begin to learn inferential statistics (Regression & ANOVA):

• Probability Distributions
• Confidence Intervals
• Hypothesis Testing
• A/B Testing
• Goodness of Fit
• Non-parametric Methods

The second section, we will learn one of the most important models in Regression Analysis:

• Simple Linear Regression
• Inference Linear Regression
• Multiple Linear Regression
• Logistic Regression

In the third section, we learn how to compare multiple means in R using the ANOVA (Analysis of Variance) method and variants, including:

• ANOVA test for comparing independent measures.
• Repeated-measures ANOVA, which is used for analyzing data where the same subjects are measured more than once.
• Mixed-ANOVA, which is used to compare the means of groups cross-classified by at least two factors, where one factor is a “within-subjects” factor (repeated measures) and the other factor is a “between-subjects” factor.
• ANCOVA (Analyse of Covariance), an extension of the one-way ANOVA that incorporates a covariate variable.
• MANOVA (Multivariate Analysis of Variance), an ANOVA with two or more continuous outcome variables.