This document provides an overview of data analysis and statistics concepts for a training session. It begins with an agenda outlining topics like descriptive statistics, inferential statistics, and independent vs dependent samples. Descriptive statistics concepts covered include measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), and charts. Inferential statistics discusses estimating population parameters, hypothesis testing, and statistical tests like t-tests, ANOVA, and chi-squared. The document provides examples and online simulation tools. It concludes with some practical tips for data analysis like checking for errors, reviewing findings early, and consulting a statistician on analysis plans.
This document provides an introduction to R, including what R is, how it compares to other statistical software packages, its advantages and disadvantages, how to install R, and options for R editors and graphical user interfaces (GUIs). It discusses R as a language for statistical computing and graphics, compares it to packages like SAS, Stata, and SPSS in terms of cost, usage mode, and prevalence. It outlines some of R's advantages like being free and open-source software with an active user community contributing packages, and some disadvantages like the learning curve and lack of a standard GUI.
This document defines key concepts in statistics:
1. Statistics is the study of collecting, organizing, analyzing, and interpreting numerical data through methods like descriptive and inferential statistics.
2. Descriptive statistics deals with presenting and collecting data through tables, graphs and charts, while inferential statistics draws conclusions from the analysis.
3. Other important concepts include populations, which are all elements being studied; samples, which are subsets of populations; parameters, which are measures of populations; and statistics, which are measures of samples.
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
This document provides an overview of time series analysis and properties of time series data. It discusses key concepts such as:
- Time series data consisting of successive observations made over time at regular intervals.
- Examples of time series include stock prices, interest rates, GDP, and other economic indicators measured over time.
- Properties of time series data including non-stationarity, autocorrelation, and seasonal patterns.
- Components of time series including trends, seasonal variations, cyclical patterns, and irregular fluctuations.
- The importance of testing for and addressing non-stationarity through differencing or other transformations before modeling and forecasting time series data.
This document provides an introduction to statistics. It discusses why statistics is important and required for many programs. Reasons include the prevalence of numerical data in daily life, the use of statistical techniques to make decisions that affect people, and the need to understand how data is used to make informed decisions. The document also defines key statistical concepts such as population, parameter, sample, statistic, descriptive statistics, inferential statistics, variables, and different types of variables.
This document provides an overview of exploratory data analysis (EDA). It discusses how EDA is used to generate and refine questions from data by visualizing, transforming, and modeling the data. Questions can come from hypotheses, problems, or the data itself. EDA plays a role in developing, testing, and refining theories, solving problems, and asking interesting questions about the data. The document emphasizes being skeptical of assumptions and open to multiple interpretations during EDA to maximize learning from the data. It introduces the dplyr and ggplot2 packages for selecting, filtering, summarizing, and visualizing data during the EDA process.
Discrete vs continuous data - comparison chartIntellspot
This document compares and contrasts discrete and continuous data. Discrete data has clear separations between values and can be counted in whole numbers, while continuous data falls along a continuous sequence and can be measured to finer degrees. Examples of discrete data include the number of students in a class or home runs in a game. Examples of continuous data include time, height, weight, and rainfall amounts. Discrete data is represented graphically with bar charts while continuous data uses histograms.
Correlation analysis measures the strength and direction of association between two or more variables. It is represented by the coefficient of correlation (r), which ranges from -1 to 1. A value of 0 indicates no association, 1 indicates perfect positive association, and -1 indicates perfect negative association. The scatter diagram is a graphical method to visualize the association between variables by plotting their values. Karl Pearson's coefficient is a commonly used algebraic method to calculate the coefficient of correlation from sample data.
Introductory Statistics discusses the definition and history of statistics. Statistics deals with quantitative or numerical data and is the scientific method of collecting, organizing, analyzing, and making decisions with quantitative data. Historically, Indian texts from the Mauryan period and Mughal period contained early forms of statistical analysis of topics like agriculture. The typical process of a statistical study involves defining objectives, identifying the population and characteristics, planning data collection, collecting and organizing data, performing statistical analysis, and drawing conclusions. Statistics is useful for simplifying complex data, quantifying uncertainty, discovering patterns to enable forecasting, and testing assumptions. Statistical techniques have various applications in fields like marketing, economics, finance, operations, human resources, information technology,
Regression Analysis presentation by Al Arizmendez and Cathryn LottierAl Arizmendez
We present an overview of regression analysis, theoretical construct, then provide a graphic representation before performing multiple regression analysis step by step using SPSS (audio files accompany the tutorial).
This document provides an introduction to key concepts in statistics. It discusses various statistical measures such as measures of central tendency (mean, median, mode), measures of dispersion (range, standard deviation), correlation, and different types of correlation (simple, partial, multiple). It also outlines common statistical methods like scatter diagrams, Karl Pearson's method, and rank correlation method. The role of computer technology in statistics is mentioned.
This document discusses different types of data and methods for presenting data. It describes qualitative and quantitative data, discrete and continuous data, and primary and secondary data. It also covers nominal and ordinal data. Common methods for presenting data include tabulation and various charts or diagrams. Tabulation involves organizing data into tables, following specific rules. Charts allow visualization of data and include bar charts, histograms, frequency polygons, cumulative frequency diagrams, scatter diagrams, line diagrams, and pie charts. Each chart has specific purposes and guidelines for effective presentation of data.
The document discusses exploratory data analysis (EDA) techniques in R. It explains that EDA involves analyzing data using visual methods to discover patterns. Common EDA techniques in R include descriptive statistics, histograms, bar plots, scatter plots, and line graphs. Tools like R and Python are useful for EDA due to their data visualization capabilities. The document also provides code examples for creating various graphs in R.
Statistics is the methodology used to interpret and draw conclusions from collected data. It provides methods for designing research studies, summarizing and exploring data, and making predictions about phenomena represented by the data. A population is the set of all individuals of interest, while a sample is a subset of individuals from the population used for measurements. Parameters describe characteristics of the entire population, while statistics describe characteristics of a sample and can be used to infer parameters. Basic descriptive statistics used to summarize samples include the mean, standard deviation, and variance, which measure central tendency, spread, and how far data points are from the mean, respectively. The goal of statistical data analysis is to gain understanding from data through defined steps.
This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.
This document provides an introduction to statistics, including what statistics is, who uses it, and different types of variables and data presentation. Statistics is defined as collecting, organizing, analyzing, and interpreting numerical data to assist with decision making. Descriptive statistics organizes and summarizes data, while inferential statistics makes estimates or predictions about populations based on samples. Variables can be qualitative or quantitative, and quantitative variables can be discrete or continuous. Data can be presented through frequency tables, graphs like histograms and polygons, and cumulative frequency distributions.
This document provides information on using SPSS for educational research. It discusses descriptive statistics, common statistical issues in research, procedures for creating a SPSS data file and conducting descriptive analyses. It also explains how to perform t-tests, analysis of variance (ANOVA), frequencies analysis and other statistical tests in SPSS. The document is intended as a guide for researchers on applying various statistical analyses in SPSS.
This document introduces descriptive statistics such as mean, median, mode, variance and standard deviation. It demonstrates how to calculate these statistics in Excel and Stata using sample student grade and unemployment rate datasets. Key charts for presenting data like histograms, bar charts and line charts are also illustrated for both programs. The concept of correlation is discussed and how to calculate the correlation coefficient to understand relationships between variables.
This document introduces the concept of data classification and levels of measurement in statistics. It explains that data can be either qualitative or quantitative. Qualitative data consists of attributes and labels while quantitative data involves numerical measurements. The document also outlines the four levels of measurement - nominal, ordinal, interval, and ratio - from lowest to highest. Each level allows for different types of statistical calculations, with the ratio level permitting the most complex calculations like ratios of two values.
This document provides an overview of the statistical software package SPSS (Statistical Package for the Social Sciences). It was originally developed in 1968 to facilitate statistical analysis in the social sciences and was later purchased by IBM in 2009 for over $1 billion. The document outlines SPSS's general capabilities for data management, analysis, and visualization including defining and coding variables, descriptive statistics, graphs, and other statistical analyses. It also defines different variable types, levels of measurement, and common descriptive statistics like measures of central tendency and dispersion.
Nominal data vs ordinal data - comparison chartIntellspot
Nominal data are categorized data that are distinguished by name only, with no inherent ordering or numeric value, such as gender or hair color. Ordinal data assigns categories an order but the differences between categories are unknown, such as rankings or letter grades. Both nominal and ordinal data can be assigned numbers but arithmetic cannot be performed on the numbers as they only represent order or category, not precise values.
10 everyday reasons why statistics are importantJason Edington
Statistics is used in many fields to analyze data and make predictions. It helps separate signals from noise. Examples given where statistics is used include stock markets, quality assurance, retail, insurance, political campaigns, genetic engineering, medical studies, weather forecasting, and emergency preparedness. The document emphasizes that an important reason to study statistics is to be better consumers of information and understand when data may be manipulated.
This document discusses different measures of central tendency including the mode, median, and mean. It provides examples of how to calculate each measure using both raw and grouped data. The mode is the most common value, and is appropriate for qualitative or nominal level data. The median is the middle value when data is ordered from lowest to highest, and is used for ordinal or interval level data. The mean is the average and is calculated by summing the product of each value and its frequency, divided by the total number of values. It requires interval level data. The appropriate measure depends on the level of measurement and research objective.
This document discusses objectives and techniques for data exploration, including understanding data, preparation for data mining, and interpreting results. It outlines univariate and multivariate descriptive statistics, various data visualization techniques like histograms and scatter plots, and provides a roadmap for exploring a data set through organizing, finding central points, understanding attribute spreads, visualizing distributions, pivoting data, identifying outliers, understanding relationships between attributes, visualizing those relationships, and visualizing high-dimensional data sets.
Definition, functions, scope, limitations of statistics; diagrams and graphs; basic definitions and rules for probability, conditional probability and independence of events.
This document provides an overview of exploratory data analysis (EDA). It discusses how EDA is used to generate and refine questions from data by visualizing, transforming, and modeling the data. Questions can come from hypotheses, problems, or the data itself. EDA plays a role in developing, testing, and refining theories, solving problems, and asking interesting questions about the data. The document emphasizes being skeptical of assumptions and open to multiple interpretations during EDA to maximize learning from the data. It introduces the dplyr and ggplot2 packages for selecting, filtering, summarizing, and visualizing data during the EDA process.
Discrete vs continuous data - comparison chartIntellspot
This document compares and contrasts discrete and continuous data. Discrete data has clear separations between values and can be counted in whole numbers, while continuous data falls along a continuous sequence and can be measured to finer degrees. Examples of discrete data include the number of students in a class or home runs in a game. Examples of continuous data include time, height, weight, and rainfall amounts. Discrete data is represented graphically with bar charts while continuous data uses histograms.
Correlation analysis measures the strength and direction of association between two or more variables. It is represented by the coefficient of correlation (r), which ranges from -1 to 1. A value of 0 indicates no association, 1 indicates perfect positive association, and -1 indicates perfect negative association. The scatter diagram is a graphical method to visualize the association between variables by plotting their values. Karl Pearson's coefficient is a commonly used algebraic method to calculate the coefficient of correlation from sample data.
Introductory Statistics discusses the definition and history of statistics. Statistics deals with quantitative or numerical data and is the scientific method of collecting, organizing, analyzing, and making decisions with quantitative data. Historically, Indian texts from the Mauryan period and Mughal period contained early forms of statistical analysis of topics like agriculture. The typical process of a statistical study involves defining objectives, identifying the population and characteristics, planning data collection, collecting and organizing data, performing statistical analysis, and drawing conclusions. Statistics is useful for simplifying complex data, quantifying uncertainty, discovering patterns to enable forecasting, and testing assumptions. Statistical techniques have various applications in fields like marketing, economics, finance, operations, human resources, information technology,
Regression Analysis presentation by Al Arizmendez and Cathryn LottierAl Arizmendez
We present an overview of regression analysis, theoretical construct, then provide a graphic representation before performing multiple regression analysis step by step using SPSS (audio files accompany the tutorial).
This document provides an introduction to key concepts in statistics. It discusses various statistical measures such as measures of central tendency (mean, median, mode), measures of dispersion (range, standard deviation), correlation, and different types of correlation (simple, partial, multiple). It also outlines common statistical methods like scatter diagrams, Karl Pearson's method, and rank correlation method. The role of computer technology in statistics is mentioned.
This document discusses different types of data and methods for presenting data. It describes qualitative and quantitative data, discrete and continuous data, and primary and secondary data. It also covers nominal and ordinal data. Common methods for presenting data include tabulation and various charts or diagrams. Tabulation involves organizing data into tables, following specific rules. Charts allow visualization of data and include bar charts, histograms, frequency polygons, cumulative frequency diagrams, scatter diagrams, line diagrams, and pie charts. Each chart has specific purposes and guidelines for effective presentation of data.
The document discusses exploratory data analysis (EDA) techniques in R. It explains that EDA involves analyzing data using visual methods to discover patterns. Common EDA techniques in R include descriptive statistics, histograms, bar plots, scatter plots, and line graphs. Tools like R and Python are useful for EDA due to their data visualization capabilities. The document also provides code examples for creating various graphs in R.
Statistics is the methodology used to interpret and draw conclusions from collected data. It provides methods for designing research studies, summarizing and exploring data, and making predictions about phenomena represented by the data. A population is the set of all individuals of interest, while a sample is a subset of individuals from the population used for measurements. Parameters describe characteristics of the entire population, while statistics describe characteristics of a sample and can be used to infer parameters. Basic descriptive statistics used to summarize samples include the mean, standard deviation, and variance, which measure central tendency, spread, and how far data points are from the mean, respectively. The goal of statistical data analysis is to gain understanding from data through defined steps.
This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.
This document provides an introduction to statistics, including what statistics is, who uses it, and different types of variables and data presentation. Statistics is defined as collecting, organizing, analyzing, and interpreting numerical data to assist with decision making. Descriptive statistics organizes and summarizes data, while inferential statistics makes estimates or predictions about populations based on samples. Variables can be qualitative or quantitative, and quantitative variables can be discrete or continuous. Data can be presented through frequency tables, graphs like histograms and polygons, and cumulative frequency distributions.
This document provides information on using SPSS for educational research. It discusses descriptive statistics, common statistical issues in research, procedures for creating a SPSS data file and conducting descriptive analyses. It also explains how to perform t-tests, analysis of variance (ANOVA), frequencies analysis and other statistical tests in SPSS. The document is intended as a guide for researchers on applying various statistical analyses in SPSS.
This document introduces descriptive statistics such as mean, median, mode, variance and standard deviation. It demonstrates how to calculate these statistics in Excel and Stata using sample student grade and unemployment rate datasets. Key charts for presenting data like histograms, bar charts and line charts are also illustrated for both programs. The concept of correlation is discussed and how to calculate the correlation coefficient to understand relationships between variables.
This document introduces the concept of data classification and levels of measurement in statistics. It explains that data can be either qualitative or quantitative. Qualitative data consists of attributes and labels while quantitative data involves numerical measurements. The document also outlines the four levels of measurement - nominal, ordinal, interval, and ratio - from lowest to highest. Each level allows for different types of statistical calculations, with the ratio level permitting the most complex calculations like ratios of two values.
This document provides an overview of the statistical software package SPSS (Statistical Package for the Social Sciences). It was originally developed in 1968 to facilitate statistical analysis in the social sciences and was later purchased by IBM in 2009 for over $1 billion. The document outlines SPSS's general capabilities for data management, analysis, and visualization including defining and coding variables, descriptive statistics, graphs, and other statistical analyses. It also defines different variable types, levels of measurement, and common descriptive statistics like measures of central tendency and dispersion.
Nominal data vs ordinal data - comparison chartIntellspot
Nominal data are categorized data that are distinguished by name only, with no inherent ordering or numeric value, such as gender or hair color. Ordinal data assigns categories an order but the differences between categories are unknown, such as rankings or letter grades. Both nominal and ordinal data can be assigned numbers but arithmetic cannot be performed on the numbers as they only represent order or category, not precise values.
10 everyday reasons why statistics are importantJason Edington
Statistics is used in many fields to analyze data and make predictions. It helps separate signals from noise. Examples given where statistics is used include stock markets, quality assurance, retail, insurance, political campaigns, genetic engineering, medical studies, weather forecasting, and emergency preparedness. The document emphasizes that an important reason to study statistics is to be better consumers of information and understand when data may be manipulated.
This document discusses different measures of central tendency including the mode, median, and mean. It provides examples of how to calculate each measure using both raw and grouped data. The mode is the most common value, and is appropriate for qualitative or nominal level data. The median is the middle value when data is ordered from lowest to highest, and is used for ordinal or interval level data. The mean is the average and is calculated by summing the product of each value and its frequency, divided by the total number of values. It requires interval level data. The appropriate measure depends on the level of measurement and research objective.
This document discusses objectives and techniques for data exploration, including understanding data, preparation for data mining, and interpreting results. It outlines univariate and multivariate descriptive statistics, various data visualization techniques like histograms and scatter plots, and provides a roadmap for exploring a data set through organizing, finding central points, understanding attribute spreads, visualizing distributions, pivoting data, identifying outliers, understanding relationships between attributes, visualizing those relationships, and visualizing high-dimensional data sets.
Definition, functions, scope, limitations of statistics; diagrams and graphs; basic definitions and rules for probability, conditional probability and independence of events.
Statistics as a subject (field of study):
Statistics is defined as the science of collecting, organizing, presenting, analyzing and interpreting numerical data to make decision on the bases of such analysis.(Singular sense)
Statistics as a numerical data:
Statistics is defined as aggregates of numerical expressed facts (figures) collected in a systematic manner for a predetermined purpose. (Plural sense) In this course, we shall be mainly concerned with statistics as a subject, that is, as a field of study
This document provides an overview of the course "Statistics for Managers" including its aims, learning outcomes, units of study, and references. The course aims to develop statistical thinking and abilities to understand and use data. It covers measures of central tendency and dispersion, graphical presentation of data, small sample tests, correlation and regression analysis. The learning outcomes include selecting the correct statistical method, building models for business applications, and distinguishing between cross-sectional and time series analysis. Key topics covered are introduction to statistics, measures of central tendency and dispersion, tabulation and graphical presentation of data, small sample tests, and correlation and regression analysis.
Basics of Research Types of Data ClassificationHarshit Pandey
This document provides an introduction and overview of research methods and statistics. It begins by outlining the origins and early contributors to statistics as a field, including its use in state administration starting in the 17th century. Key concepts in statistics such as variables, populations, samples, and levels of measurement are then defined. The document distinguishes between descriptive and inferential statistics, outlining common techniques for each. It concludes by discussing the scope and limitations of statistics as a scientific discipline.
Statistics can be used to analyze data, make predictions, and draw conclusions. It has a variety of applications including predicting disease occurrence, weather forecasting, medical studies, quality testing, and analyzing stock markets. There are two main branches of statistics - descriptive statistics which summarizes and presents data, and inferential statistics which analyzes samples to make conclusions about populations. Key terms include population, sample, parameter, statistic, variable, data, qualitative vs. quantitative data, discrete vs. continuous data, and the different levels of measurement. Important figures in the history of statistics mentioned are William Petty, Carl Friedrich Gauss, Ronald Fisher, and James Lind.
Statistics is the science of collecting, analyzing, and interpreting numerical data. It has evolved from early uses by governments to understand populations for taxation and military purposes. Modern statistics developed in the 18th-19th centuries and saw rapid growth in the 20th century with advances in computing. Statistics has two main branches - descriptive statistics which involves data presentation and inference statistics which uses data analysis to make estimates and test hypotheses. Statistics is widely used across many fields including business, economics, mathematics, and banking to facilitate decision making.
Notes of BBA /B.Com as well as BCA. It will help average students to learn Business Statistics. It will help MBA and PGDM students in Quantitative Analysis.
Statisticians help collect, analyze, and interpret numerical data to solve problems and make predictions. The steps of statistical analysis involve collecting information, evaluating it, and drawing conclusions. Statisticians work in a variety of fields such as medicine, government, education, business, and more. They help determine sampling methods, process data, and advise on the strengths and limitations of statistical results.
This document provides an introduction to business statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting numerical data. The document notes that statistics can refer to both quantitative information and the methods used to analyze that information. It describes the key stages of a statistical analysis: data collection, organization, presentation, analysis, and interpretation. The document also discusses whether statistics is a science or an art and the important functions of statistics like providing definiteness, enabling comparison, and aiding in prediction.
Statistics is the study of collecting, organizing, analyzing, and presenting data. It has a long history dating back to 1749. Statistical activities often use probability models and require probability theory. Key concepts in statistics like experimental design and statistical inference have impacted many fields. Statistics is used in many areas including business, education, psychology, health, engineering, and more. Descriptive statistics describes data while inferential statistics makes conclusions about populations from samples.
Statistics is a basic and important tool for professionals in all fields all over the worlds. This document provides the importance and scope of Statistics in major fields of study like a business, management, planning etc.
This document provides an overview of statistics as a field of study. It defines statistics as both the plural and singular form, describing aggregates of numerical data and the science dealing with collecting, organizing, and interpreting numerical data. The two main branches of statistics are described as descriptive statistics, which describes what is occurring in a data set, and inferential statistics, which allows making generalizations about a larger population based on a sample. Key terms like data, variables, population, sample, and parameter are also defined. The stages of a statistical investigation and applications, uses, and limitations of statistics are summarized.
It is an overview of main concepts in statistics for beginners.
It is prepared for middle school, high school students and free learners who are eager to know more about statistics.
It is prepared on short note format so as readers will understand the concepts easily.
Basic statistics by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
Statistics is the science of collecting, organizing, analyzing, and interpreting data. It involves collecting data, presenting data through tables and graphs, analyzing the data to draw conclusions, and interpreting the results. Statistics is used in many fields including business, government, health, sciences, and more to make data-driven decisions and draw valid conclusions about populations. Statistical thinking focuses on identifying and reducing variations in phenomena and will become increasingly important for citizens.
This document provides information about a statistics course, including:
- The course is taught by Prof. T RAMA KRISHNA RAO and covers 5 units: measures of central tendency, measures of variation, correlation analysis, index numbers, and time series analysis.
- Previous year question papers from 2016-2013 are provided, with questions on topics like defining statistics, classification vs tabulation, and representing data visually.
- Key concepts from the first unit on statistics are defined, like data, characteristics of statistics, importance and scope of statistics, and limitations of statistics. Data sources like primary and secondary data are also mentioned.
Chapter 1 Introduction to statistics, Definitions, scope and limitations.pptxSubashYadav14
This document provides an introduction to statistics, including definitions, scope, and limitations. It defines statistics as both numerical facts and the methods used to collect, analyze, and interpret those facts. Several authors' definitions of statistics are presented, emphasizing that statistics are aggregates of numerically expressed or estimated facts affected by multiple causes and collected systematically. The functions of statistics are described as simplifying data, enabling comparisons, and guiding policy decisions. The importance of statistics in fields like planning, business, economics, administration, and agriculture is discussed. Descriptive and inferential statistics are briefly introduced, as are some limitations of statistical analysis.
Statistics can be used in many fields to collect and analyze numerical data. It has applications in business, government, research, and more. Statistics involves collecting data, organizing it, presenting it visually through tables and charts, analyzing it using methods like averages and correlations, and interpreting the results. The scope of statistics has expanded significantly over time from just government administration to almost every area of research and decision making where quantitative information is involved.
Statistics can be used in many fields to collect and analyze numerical data. It has applications in business, government, research, and more. Statistics involves collecting data, organizing it, presenting it visually, analyzing it, and interpreting the results. The key stages of a statistical investigation are collection, organization, presentation, analysis, and interpretation. Statistics is both a science, in that it uses scientific methods, and an art, in that it involves applying statistical knowledge to solve problems. Its scope has expanded greatly over time from just government administration to many other domains where quantitative data is relevant.
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Hats have been integral to human culture for centuries, serving various purposes from protection against the elements to fashion statements. This article delves into hats' history, types, and cultural significance, exploring how they have evolved and their role in contemporary society.
Dr Tran Quoc Bao the first Vietnamese CEO featured by The Prestige List - Asi...Ignite Capital
In the rapidly evolving landscape of Asia-Pacific healthcare, influence isn’t just about the size of the hospital—it’s about vision, adaptability, and a relentless commitment to improving patient outcomes. From biotech giants in India to global hospital operators in Australia and Thailand, a select group of leaders is reshaping how healthcare is delivered, managed, and experienced.
This is Fortune’s Prestige List—a curated recognition of the most influential hospital and healthcare CEOs across the Asia-Pacific region. For the first time, a Vietnamese leader joins this elite circle: Dr. Tran Quoc Bao, CEO of Prima Saigon and City International Hospital.
🇻🇳 Dr. Tran Quoc Bao – Vietnam’s Pioneer in International Healthcare
When people think of world-class medical tourism, destinations like Thailand and Singapore often come to mind. But Vietnam is quickly emerging as a serious contender—and much of that momentum can be traced back to Dr. Tran Quoc Bao.
As CEO of Prima Saigon and City International Hospital, Dr. Bao is leading a quiet revolution. Under his guidance, these institutions have blended international standards with local empathy—delivering both advanced clinical care and culturally attuned patient experiences. What sets Dr. Bao apart is not just his medical background, but his embrace of AI-powered patient engagement and digital health marketing strategies. These innovations have made Prima Saigon a case study in modern hospital leadership.
His inclusion in this list is historic. He is the first and only Vietnamese healthcare CEO recognized among the region’s titans—proof that Vietnam is no longer catching up; it’s breaking ground.
“Our mission is simple,” Dr. Bao told Fortune. “Care should not be a privilege—it should be a promise. We want to bring global healthcare quality to Vietnamese people and welcome the world to experience care in Vietnam.”
🌏 The Asia-Pacific Healthcare Powerhouses
🇮🇳 Kiran Mazumdar-Shaw – The Biotech Trailblazer
Founder and Executive Chairperson of Biocon, Kiran Mazumdar-Shaw has turned the Indian biotech firm into a global leader in affordable medicines and biosimilars. Her influence extends from science labs to public policy, shaping not just companies, but entire health systems.
🇲🇾 Dr. Prem Kumar Nair – The Multinational Maestro
As Group CEO of IHH Healthcare, Dr. Nair leads one of the largest private hospital networks in the world, with operations in more than 10 countries. His strategy blends operational efficiency, digital transformation, and high clinical standards—especially in emerging markets.
🇹🇭 Victor K.K. Fung – The Medical Tourism Mogul
Bumrungrad International Hospital in Bangkok has become a global health destination under Fung’s leadership. Known for serving over a million patients from more than 190 countries annually, Fung has made medical tourism a strategic business model.
Paul Turovsky is a Financial Analyst with 5 years of experience, currently at H.I.G. Capital in Miami, Florida. His expertise lies in financial modeling, cost-saving strategies, and automation. Paul's meticulous financial analysis skills have contributed to a notable reduction in operational expenses.
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The future success of Times BPO hinges on a well-defined roadmap that centers around innovation, operational efficiency, and sustainable growth. As the BPO industry evolves with technological advancements and changing client expectations, Times BPO aims to position itself as a leader in delivering cutting-edge outsourcing solutions. This roadmap outlines key strategic initiatives, including digital transformation, workforce upskilling, enhanced client-centric approaches, and global market expansion. By leveraging automation, artificial intelligence, and data analytics, Times BPO plans to optimize service delivery while maintaining a strong focus on employee development and customer satisfaction. The company’s long-term vision is to establish a resilient, future-ready organization that adapts to market trends while meeting the dynamic needs of its clients. Through continuous innovation and a commitment to operational excellence, Times BPO is poised to achieve sustainable growth, improve competitive advantage, and solidify its reputation as a forward-thinking industry leader.
Solving Disintermediation in Ride-Hailingxnayankumar
An in-depth analysis of how Ola can combat revenue leakage through product design strategies that discourage off-platform transactions between drivers and riders.
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Allan Kinsella: A Life of Accomplishment, Service, Resiliency.Allan Kinsella
Allan Kinsella is a New Zealand leader in military, public service, and education. His life reflects resilience, integrity, and national dedication.
for more info. Visit: https://www.slideshare.net/slideshow/allan-kinsella-biography-director-assurance-ministry-for-primary-industries/276260716
NewBase 05 May 2025 Energy News issue - 1785 by Khaled Al Awadi_compressed.pdfKhaled Al Awadi
Greetings,
Hawk Energy is pleased to share with you its latest energy news from NewBase Energy
as per attached file NewBase 05 May 2025 Energy News issue - 1785 by Khaled Al Awadi
Regards.
Founder & Senior Editor NewBase Energy
Khaled M Al Awadi, Energy ConsultantGreetings,
Hawk Energy is pleased to share with you its latest energy news from NewBase Energy
as per attached file NewBase 05 May 2025 Energy News issue - 1785 by Khaled Al Awadi
Regards.
Founder & Senior Editor NewBase Energy
Khaled M Al Awadi, Energy ConsultantGreetings,
Hawk Energy is pleased to share with you its latest energy news from NewBase Energy
as per attached file NewBase 05 May 2025 Energy News issue - 1785 by Khaled Al Awadi
Regards.
Founder & Senior Editor NewBase Energy
Khaled M Al Awadi, Energy ConsultantGreetings,
Hawk Energy is pleased to share with you its latest energy news from NewBase Energy
as per attached file NewBase 05 May 2025 Energy News issue - 1785 by Khaled Al Awadi
Regards.
Founder & Senior Editor NewBase Energy
Khaled M Al Awadi, Energy Consultant
Alaska Silver: Developing Critical Minerals & High-Grade Silver Resources
Alaska Silver is advancing a prolific 8-km mineral corridor hosting two significant deposits. Our flagship high-grade silver deposit at Waterpump Creek, which contains gallium (the U.S. #1 critical mineral), and the historic Illinois Creek mine anchor our 100% owned carbonate replacement system across an expansive, underexplored landscape.
Waterpump Creek: 75 Moz @ 980 g/t AgEq (Inferred), open for expansion north and south
Illinois Creek: 525 Koz AuEq - 373 Koz @ 1.3 g/t AuEq (Indicated), 152 Koz @ 1.44 g/t AuEq (Inferred)
2024 "Warm Springs" Discovery: First copper, gold, and Waterpump Creek-grade silver intercepts 0.8 miles from Illinois Creek
2025 Focus: Targeting additional high-grade silver discoveries at Waterpump Creek South and initiating studies on gallium recovery potential.
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Platform Walkthrough & Q/A
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5. In our day to day life statistics conveys a variety
of meanings to people. We find statistics in
newspapers, journals, books, various reports,
speeches, classroom lectures etc.
Example:
There are 940 females per 1000 males in india.
For population UP in India stands at rank first.
Maharashtra stands at second position
having 9.29% of total population.
6. INTRODUCTION
Density of population in Maharashtra is 365
per km square. And the sex ratio is 925
females per 1000 males.
The literacy rate in Maharashtra is 82.91%.
Most literate state in india is Kerala with
93.9%.
Percentage of students whose name is
starting with letter A, B, D, Y,
The students who scored in
between 50 to 70.
7. ORIGIN AND GROWTH
ORIGIN
It is not a new discipline but as old as human society
itself. It has been in use since the existence of life on
earth.
The word statistics comes from the Italian word statista
(meaning statesman) or the German word statistik
which means a political state.
It was first used by Prof.Gottfried Achenwall (1719-
1772)
Achenwall first defined the statistics as the political
science of several countries.
The word statistics appeared first time in a famous book
– Elements of Universal Erudiction Science of statistics
is originated from two main sources
1. Government Records
2. Mathemetics
8. Govt. Records
During good old days it is regarded as the science of
statecraft. Because it was byproduct of the
administrative activity of the state.
This is earliest foundation because all cultures with a
recorded history had recorded statistics and the
recording as far as is known was done by govt. agents
for governmental purpose.
It has been in traditional function of govt. to keep
records of population, births, deaths, taxes, crop yields
etc.
As the data was collected for governmental purposes
statistics was then described as the science of kings or
the science of statecraft.
9. Statistics is said to be a branch of applied
mathematics.
The present body of statistical methods
particularly those concerned with drawing
inferences about population from sample is based
on the mathematic theories.
Numbers play an important role in statistics and
here the study of statistics involves methods of
refining numerical and non numerical information
in to useful forms.
10. There has been a phenomenal development
in the use of statistics in several fields.
Now a days it is a most important tool for
taking decisions in case of uncertainty.
Two main factors which are responsible
the development of statistics
1. Increased demand for statistics
2. Decreasing cost of statistics
11. In present, considerable development has taken place in
the field of business, commerce, governmental activities
and science. Statistics helps in formulating suitable
policies and as such its need is increasingly being felt in
all the spheres. Such as……….
a) Government: As there is enlargement in Govt.
functions
b) Sciences: Tremendous advancement in the existing
sciences & also development of new branches.
c) Research: Extensive research work is now being
undertaken by many people.
d) Time & Cost: Less time & cost to collect and process
the data with the help of electronic machines such as
calculators, computers etc.
12. Decreasing Cost of Statistics
Following factors helps to decrease the
cost
Development of statistical theories: eg.
Sampling Techniques
Use of electronic machines: eg.
Computer calculator
Tools for Analysis: eg. SPSS
13. STATISTICS - DEFINITION
There have been many definitions of the term
statistics:
1. Webster: The classified facts representing the
conditions of the people in a state. Especially
those facts which can be stated in numbers
or in tables of numbers or in any tabular
classified arrangement.
2. Yule & Kendall: By statistics we mean
quantitative data affected to a marked extent
by multiplicity of causes.
14. STATISTICS - DEFINITION
3 . Horace secrist: Statistics means
aggregates of facts affected to a
marked extent by multiplicity of
causes ,numerically
expressed, estimated according to
reasonable standards of
accuracy, collected in a systematic
manner for a predetermined purpose
and placed in relation to each other.
;
15. Above definition states certain charactristics which
numerical data must possess in order that they may
be called statistics.
Aggregates of facts:
Affected to a marked extent by multiplicity of
causes
Numerically expressed
Enumerated or estimated according to resonable
standards of accuracy
Collected in a systematic manner
Collected for pre-detemined purpose
Should be placed in Relation to each other.
16. STATISTICS - DEFINITION
Croxton and cowden : Statistics may be
defined as the collection, presentation
analysis and interpretation of numerical
data.
Very simple and precise definition and talks
about four stages of Statistics.
Collection
Presentation
Analysis
Interpretation
18. Data
Data: This is simply a group of results of any
scientific measurement.
Eg. Collection of numbers representing height of
students in a class, Marks in a class.
19. Variable
This is a characteristic that can assume different
values and is usually represented by x.
Eg. Age of students, sales figure of a commodity.
Etc.
20. Population
A population or universe is the totality
of items or things under consideration.
It is the collection of all values of the
variable under study.
OR
The complete collection of all elements
(scores, people, measurements, and so
on) to be studied. The collection is
complete in the sense that it includes all
subjects to be studied.
21. Sample
It is the portion of population or
universe under consideration. And
sampling is the process of selecting
sample.
OR
A sub-collection of elements drawn
from a population
22. STATISTICS: SCIENCE & ART
Its a subject of Debate.
Science: systematized body of knowledge. It studies
cause and effect relationship and attempts to
make generalizations in the form of scientific
principles or laws. It describes objectivity and
avoids vague judgments. Science is Knowledge.
Art: refers to the skill of handling facts so as to
achieve a given objective. Having concern with
ways & means of presenting and handling data
making inferences logically and drawing relevant
conclusions. Art is Action.
Statistics: not a body of knowledge but a body of
methods for obtaining knowledge.
23. Functions of Statistics
Presents facts in a definite form
Simplifies mass of figures
Facilitates comparison
Helps in formulating & testing
hypothesis
Helps in prediction
Helps in formulation of suitable
policies.
24. Definiteness
To present general statements in a precise and
definite form. As numerical statements are
more convincing.
Example: The production of wheat in India in
2006-07 was higher than that in 2005-06.
“The production of wheat for the year 2006-07
was 72.5 million tonnes compared to 69.4
million tonnes for 2005-06.”
25. Condensation
Helps in condensing mass of data in to
few significant figures. Statistical
methods present a meaningful overall
information from mass of data.
Example: Income position of people of
India from a record of individual
incomes of the entire population. (Per
capita Income)
26. Comparison
Unless figures are compared with
others of same kind they are often
devoid of any meaning.
Example: The production of rice in
2009-10 is likely to be 100 million
tonnes as compared to 96 million
tonnes in 2008-09.
27. Formulation & Testing
Hypothesis
Statistical methods are helpful in
formulating and testing hypothesis & to
develop new theories.
Example: Hypothesis like whether
chloromycetin is effective in curing typhoid.
Whether students have benefitted from the
extra coaching.etc.
Technique: Chi-square Test
28. Prediction
Plans & policies of organisations are
invaribly formulated well in advance of
the time of their implementation.
Statistical methods provide helpful
means for forecasting future events.
Example: How much cement should be
produced by a cement company in
coming year.
Technique: Simulation
29. Formulation of Policies
Statistics provide the basic material for
framing suitable policies.
Example: Data about population- its
distribution by age & sex, rate &
growth of it. Migration, area etc. helps
in determining the future needs such
as
food, clothing, housing, education, recr
eational
facilities, water, electricity, transportati
on, system etc.
31. Statistics & the State
Since ancient times the ruling kings chiefs
have relied on statistics in framing suitable
military and fiscal policies. Eg. crimes,
military strength, population, taxes etc.
Today all ministers and departments of
govt. (Finance, Transport, Defence,
Railway, Food Commerce, Post &
Telegraph, or Agriculture, etc.) depend
heavily on factual data for their efficient
functioning.
32. Statistics & Business
With growing size & ever increasing
competition the problems of the business
are becoming complex & they are using
more & more statistics in decision making.
Business activities can broadly grouped in
to
Production, Purchase, Finance, Personnel,
Accounting, Market & Product
Research, Quality Control.
Each & every area is rely on statistics for
their effective functioning.
33. Statistics & Economics
In the year 1890 Prof.Alfred Marshall the
renowned economist observed that “ Statistics are
the straw out of which I, Like every other
economist, have to make bricks.’’
Economics is concerned with Production,
Distribution, Consumption, saving & investment
of income.
Example: What to produce, how to produce, and
for whom to produce – to answer these questions
we need a lot of statistical data in absence of
which it is not possible to arrive at correct
decision.
Statistics of production help in adjusting the
supply to demand.
34. Statistics & Economics
Statistics of consumption enable us to find out the
way in which people of different strata spend there
income.
To solve the problems of rising prices, growing
population, unemployment, poverty etc. one has to
rely on statistics.
It plays role not only in formulating of economic
policies but also evaluating their effects.
We use statistics in Measurement of Gross national
product, Input output analysis, money &
banking, Consumer finance, Public Finance, business
cycles,Competition, oligopoly &
monopoly, comparison of market prices, cost & profit
of individual firms, prices & Population etc.
36. Statistics & Natural Sciences
Statistical techniques are proved very useful in
the study of natural sciences. Like: Biology,
medicine, zoology, botany etc.
Example: In diagnosing the correct disease the
doctor has to rely on data like body
temperature, pulse rate, BP etc.
Similarly in judging the efficacy of a particular
drug for curing a disease experiments have to
be conducted and the success or failure would
depend upon the number of people who are
cured after using a drug.
37. Statistics And Research
Most of the advancement in knowledge has taken
place because of experiments conducted with the
help of Statistical methods.
Example: Experiments about crop yields &
different types of fertilizers and different types of
soils or the growth of animals under different diets
and environments are designed and analysed with
the help of statistical methods.
Statistics affect research medicine & public health.
We can not complete our research work without
statistics.
38. Limitations of Statistics
Statistics does not deal with individual
measurements.
Statistics only deals with Quantitative
Characteristics.
Statistical results are true only on an
average.
Statistics is only one of the methods of
studying a problem.
Statistics can be misused.