Descriptive Statistics for Beginners
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Book: Â Descriptive Statistics for Beginners
Authors: Prof. Olusanya Olubusoye, Prof. Osowole Isola and Adeniran Adefemi Tajudeen.
Publisher: The book was published in 2024 by Stirling-Horden Publishers Ltd, Ibadan.
Year of Publication: 2024
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The 178-page book is written by three eminent scholars of the University of Ibadan. Prof. Olusanya Olubusoye, Prof. Osowole Isola and Prof. Adeniran Adefemi Tajudeen , all of the Statistics Department.
Prof. Olubusoye, a University of Ibadan scholar is a chartered Statistician, who has served as the Head of the Department of Minerals, Petroleum, Energy Economics , and Law at the University of Ibadan.
Osowole is a Professor of Statistics , faculty of Science, UI. At present, he is the Head of Department of Statistics.
Adeniran, a first-class graduate, is a lecturer  at the Statistics Department of the premier university.
The book was published in 2024 by Stirling-Horden Publishers Ltd, Ibadan.
The book is divided into 13 chapters.
Starting Introduction to Statistics, which is the chapter one. The chapter lays the foundation for the subject matter. In it, the book defines Statistics as collecting, organizing, analyzing, interpreting, and presenting data. Aspects cover in here include types of statistics [descriptive and inferential], applications of the course in various fields, and an overview of R Software for statistical computation.
Chapter Two is about Understanding Data. Under the chapter, the book states that understanding the types of data is fundamental in statistics as it indicates the appropriate methods of analysis and interpretation. Types of data which are qualitative and quantitative are explained with examples. Sources of obtaining data which are primary and secondary are thoroughly elucidated.
The chapters also highlights Methods of data collection, including Survey and Questionnaires, Interviews, Experiments, Observations and existing data sources.
It points out how data quality can assessed.
The chapters, under Measurement Scales, asserts that scales determine the type of statistical analysis that can be performed on the data. The scales include nominal, Ordinal, Interval and Ratio.
The chapter concludes that in ensuring effective data management and analysis, R provides robust tools for handling various data types under the sub-topic ‘Using R to Handle Different Data Types’.
In chapter three, the focus is on Tabular Data Presentation. It explains the creation of Frequency Tables in R and steps to create Frequency Tables in R with copious examples.
Chapter four focuses on Diagrammatic Data Presentation, using Line Grapics, Bar Charts, Pictograms, Pie Charts, Histograms, Frequency Polygons and Stem-and-Leaf Displays.
In chapter five, Measures of Central Tendency takes centre stage. These are statistical metrics that describe the centre points of a dataset. The commonly used measures of central tendency, which are the Mean, Median, and Mode are mathematically and qualitatively explained and their application.
In describing the variability of a dataset, using Range, Variance, Standard Deviation, and Coefficient of Variation as statistical metrics, chapter six does justice to that under the topic: ‘Measures of Dispersion’. It also explains the application of R with Empirical Rule and Chebyshev’s Rule.
Measures of Relative Position, which chapter seven starts with the calculation in R, using Quartiles, Inter-quartile Range, Deciles, Percentiles and Z-Scores as metrics. It also how outliers can be detected using Z-Scores.
Measures of Shape, which describe the distribution of data, focusing on its symmetry and peakedness is the thrust of Chapter Eight. The chapter states that the two main measures of shape are Skewness and Kurtosis. According to the chapter, Skewness indicates the asymmetry of data, while Kurtosis measures the peakedness of the distribution.
Chapter Nine, with the topic:  ‘Introduction to R Programming for Statistics’, introduces the basic syntax and operations in R, and explores various data types and structures, and provides guidelines for importing and exporting data.
Chapter 10 which entails Identifying Missing Data, Data Transformation and Manipulation in R, and Summarising Data with dplyr and Other R Packages, is captured under the topic: ‘Data Cleaning and Preparation’.
Exploratory Data Analysis [EDA] is the theme of chapter eleven. It explains that EDA is a critical step in the data analysis process aimed at summarizing the main characteristics of the data. It examines the importance of EDA, Graphical Techniques in R, and Descriptive Statistical Techniques in R mathematically.
In providing deeper insights and clearer communication of complex data, Advanced Data Visualisation, which is chapter twelve becomes handy, book posits. It further takes a vivid look at the creation of Advanced Plots with ggplot2, and customisation of Plots for better insights.
Concluding the book in chapter Thirteen with the theme: ‘Summary and Practical Applications’, the authors say: ‘’ From understanding basic statistical concepts to creating advanced visualization, each chapter has built upon the previous to provide a comprehensive guide to descriptive statistics for beginners’’. This succinctly encapsulates the thrust of the book.
One unique feature of the book  is the questions-and-answers session that follows every chapter. While many find calculations difficult to understand, the book with its descriptive nature and lucid explanations, makes mathematics, science of data and numerical expressions simple. It is written in a simple language for beginners to grasp its essentials and appreciate the importance of data and statistics in addressing everyday’s complex problems. It is recommended for students of Statistics, Accounting, Economics, other related courses and policy makers.