This is composed by me, and this was instructed by Esteemed Sir Haji Ahmed Solangi, during my academic session for semester 3rd 2014 in University of Sindh Laar Campus @ Badin.
The document discusses business statistics and its importance. It defines statistics as the study of collecting, organizing, analyzing, and interpreting numerical data. There are five stages to statistical investigation: data collection, organization, presentation, analysis, and interpretation of results. Statistics helps simplify complex data, facilitate comparison between data sets, test hypotheses, formulate policies, and derive valid inferences. However, statistics has limitations as it does not study individuals, statistical laws are approximations rather than exact, and it only analyzes aggregated data rather than individual observations.
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.
This document discusses several definitions of economics provided by prominent economists over time. It begins by summarizing Adam Smith's definition from 1776 that viewed economics as the science of wealth. It then discusses Alfred Marshall's 1890 definition that considered economics the study of mankind in business. Next, it outlines Lionel Robbins' 1932 definition that defined economics as studying human behavior related to scarce means and alternative uses. Finally, it provides Paul Samuelson's modern definition from 1948 that viewed economics as concerning how society employs its resources. The document then briefly discusses the main divisions of economics as consumption, production, exchange, distribution, and public finance.
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.
This document provides an introduction to business statistics for a 4th semester BBA course. It defines statistics as the collection, analysis, and interpretation of numerical data. Descriptive statistics are used to summarize data through measures of central tendency, dispersion, graphs and tables. Inferential statistics allow generalization from samples to populations through estimation of parameters and hypothesis testing. The key terms of population, sample, parameter, and statistic are defined. Variables are characteristics that can take on different values and are classified as qualitative or quantitative. Quantitative variables are further divided into discrete and continuous types. Descriptive statistics simply describe data while inferential statistics make inferences about unknown population characteristics based on samples.
1. The document discusses the introduction to statistics, providing definitions and explaining key concepts. It describes how statistics is used in various fields like education, business, medical research, and agriculture.
2. Statistics is defined as the science of collecting, organizing, summarizing, presenting, analyzing, and interpreting data. It can be used as both a science and an art. Statistics has various applications in fields like administration, business, education, and medical and agricultural research.
3. The document outlines the basic terminology used in statistics, including data, variables, observations, quantitative and qualitative data, continuous and discrete variables. It distinguishes between primary and secondary data and their characteristics.
Application of Univariate, Bi-variate and Multivariate analysis Pooja k shettySundar B N
This document discusses different types of statistical analysis used to analyze data. Univariate analysis examines one variable at a time through methods like frequency distributions, histograms, and pie charts. Bivariate analysis considers the relationship between two variables, such as income and weight. Multivariate analysis studies three or more variables simultaneously, with applications in fields like social science, climatology, and medicine.
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,
This document provides an introduction to index numbers and their uses and characteristics. It defines an index number as a method to measure changes in price levels over time, typically expressed as a percentage. Index numbers are specialized averages that measure changes in phenomena over periods of time and allow comparisons of prices, quantities, and values between different years by relating them to a base year. The document then discusses various methods for calculating index numbers, including fixed base methods, chain base methods, weighted index numbers using Laspeyres, Paaschee and Fisher's ideal formulas, and the Marshall-Edgeworth method.
This document provides an overview of key topics in statistics for management. It covers statistical surveys, classification and presentation of data, measures used to summarize data, probabilities, theoretical distributions, sampling and sampling distributions, estimation, hypothesis testing for large and small samples, and chi-square, F-distribution, analysis of variance, correlation, regression, business forecasting, and time series analysis. The document serves as an introduction to important statistical concepts and methods relevant for management.
1. The document provides an introduction to regression models and panel data, outlining key concepts such as the definition of panel data, benefits of using panel data including controlling for individual heterogeneity, and limitations of panel data including problems with data collection.
2. Panel data involves observing the same cross-section of individuals, countries, firms etc. over multiple time periods, allowing analysis of both time and individual variability.
3. Using panel data offers advantages over cross-sectional or time series data alone, such as better accounting for unobserved heterogeneity and enabling analysis of dynamic adjustments over time.
This document provides an overview of statistics as a field of study. It discusses the meaning and importance of statistics, as well as data collection methods like census and sampling. Classification, tabulation, and diagrammatic/graphic presentation of data are also covered. The document outlines key statistical concepts like estimation, hypothesis testing, and applications of statistics in various disciplines like industry, commerce, agriculture and more. Common statistical techniques like correlation, regression, probability distributions, and statistical quality control are also mentioned.
This is a short introduction course to Stata statistical software version 9. The course still applies to later versions of Stata, too. The course duration was 9 hours. It has been given at the Faculty of Economics and Political Science, Cairo University.
This document provides an overview of time series analysis. It defines a time series as numerical data obtained at regular time intervals that occurs in many domains like economics and finance. The goals of time series analysis are to describe, summarize, fit models to, and forecast time series data. Time series are different from other data as observations are not independent. The document discusses the various components of time series including trends, seasonality, cycles, and irregular variations. It provides examples of decomposing time series into these components to better understand the underlying patterns in the data.
The document provides an overview of a business statistics course, including topics covered, applications in different business fields, and examples of descriptive statistics. The course covers topics such as data collection, descriptive statistics, statistical inference, and the use of computers for analysis. Descriptive statistics are used to summarize parts cost data from 50 car tune-ups, finding an average cost of $79. Inferential statistics are used to estimate population characteristics based on sample data.
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.
Here are some common sources of primary and secondary data:
Primary data sources:
- Surveys (questionnaires, interviews)
- Experiments
- Observations
- Focus groups
Secondary data sources:
- Government data (census data, vital statistics)
- Published research studies
- Organizational records and documents
- Media reports
- Commercial data providers
This document discusses methods for measuring trends in time series data. It describes secular trends as long-term movements in data over time, which can be upward, downward, or stagnant. Four common methods for measuring trends are discussed: graphical/freehand method, method of semi-averages, moving averages method, and least squares method. The graphical method involves visually fitting a smooth curve to the data points, while the semi-averages method divides the data into two sets and connects the midpoints using a straight line. Strengths and weaknesses of each approach are also presented.
Statistics is the collection and analysis of data. There are two main branches: descriptive statistics, which organizes and summarizes data, and inferential statistics, which uses descriptive statistics to make predictions. Statistics starts with a question and uses data to provide information to help make decisions. It is widely used in business, health, education, research, social sciences, and natural resources.
This chapter introduces the basic concepts and terminology of statistics. It discusses two main branches of statistics - descriptive statistics which involves collecting, organizing and summarizing data, and inferential statistics which allows drawing conclusions about populations from samples. The chapter also covers variables, populations, samples, parameters, statistics and how to organize and visualize data through tables, charts and graphs. It emphasizes that statistics helps turn data into useful information for decision making in business.
This document provides an introduction to statistics, covering key topics such as what statistics is, its functions, applications in business, and subject matter. Statistics is defined as both a set of numerical data and a set of techniques for collecting, organizing, analyzing, and interpreting quantitative data. It serves functions like simplifying complex facts, providing comparisons, and forecasting. Statistics is used widely in business decision making across areas like marketing, finance, and operations. The subject matter of statistics has two parts - descriptive statistics, which summarizes data, and inferential statistics, which makes conclusions about large groups by studying samples.
This document discusses panel data and methods for analyzing it. Panel data contains observations on multiple entities like individuals, states, or school districts that are observed at different points in time. This allows controlling for factors that are constant over time but vary across entities. Fixed effects regression is introduced as a method that eliminates the effect of any time-invariant characteristics. The document provides examples of how to specify fixed effects models using binary regressors or demeaning the data, and notes these produce identical estimates.
This document provides an overview of questionnaire surveys and their design. It discusses key aspects of developing and conducting a questionnaire survey such as defining objectives, conceptualizing variables, exploring concepts through focus groups and interviews, designing the questionnaire, and testing methods. The document also covers factors that affect questionnaire surveys, advantages and disadvantages, psychology of asking questions, and ensuring validity and reliability. The overall summary is that this document outlines best practices for designing and implementing a rigorous questionnaire survey from start to finish.
This document provides an introduction and overview of labor economics. It discusses key topics including:
- What labor economics studies, including the interaction of workers and employers in labor markets and how this determines wages, employment, and income.
- The microeconomic and macroeconomic techniques used to study labor markets, including individual behavior and interactions between labor markets and other markets.
- The importance of labor economics for understanding socioeconomic issues, its quantitative impact on national income, and unique characteristics of labor.
- How the field has evolved from a more descriptive, historical approach to incorporating applied microeconomic and macroeconomic theory.
Primary and Secondary Data collection - Ajay Anoj & GokulAJAY ANOJ KUMAR
The document discusses primary and secondary sources of data collection for research. It defines primary data as data collected directly by the researcher for the purposes of the research project. Secondary data is defined as data that was previously collected by others. The document outlines various methods for collecting both primary and secondary data, including surveys, interviews, observation, and reviewing published sources. It also compares primary and secondary data and discusses best practices for selecting an appropriate data collection method based on factors like the research objective, availability of funds, and required precision.
This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.
This document discusses the use of dummy variables in econometric modeling. It begins by explaining that some variables cannot be quantified numerically and provides examples where dummy variables would be used. It then discusses how dummy variables are incorporated into regression models, including intercept dummy variables, slope dummy variables, and dummy variables for multiple categories. The document also covers seasonal dummy variables and concludes by explaining the Chow test and dummy variable test for testing structural stability using dummy variables.
This document provides an overview of time series analysis and its key components. It discusses that a time series is a set of data measured at successive times joined together by time order. The main components of a time series are trends, seasonal variations, cyclical variations, and irregular variations. Time series analysis is important for business forecasting, understanding past behavior, and facilitating comparison. There are two main mathematical models used - the additive model which assumes data is the sum of its components, and the multiplicative model which assumes data is the product of its components. Decomposition of a time series involves discovering, measuring, and isolating these different components.
This document summarizes key descriptive statistics measures used to describe data, including measures of location (mean, median, mode) and measures of variability (range, interquartile range, variance, standard deviation, coefficient of variation). It provides examples calculating these measures using a sample data set of monthly rents for one-bedroom apartments. Formulas and explanations are given for how to compute each measure.
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
This document provides an introduction to index numbers and their uses and characteristics. It defines an index number as a method to measure changes in price levels over time, typically expressed as a percentage. Index numbers are specialized averages that measure changes in phenomena over periods of time and allow comparisons of prices, quantities, and values between different years by relating them to a base year. The document then discusses various methods for calculating index numbers, including fixed base methods, chain base methods, weighted index numbers using Laspeyres, Paaschee and Fisher's ideal formulas, and the Marshall-Edgeworth method.
This document provides an overview of key topics in statistics for management. It covers statistical surveys, classification and presentation of data, measures used to summarize data, probabilities, theoretical distributions, sampling and sampling distributions, estimation, hypothesis testing for large and small samples, and chi-square, F-distribution, analysis of variance, correlation, regression, business forecasting, and time series analysis. The document serves as an introduction to important statistical concepts and methods relevant for management.
1. The document provides an introduction to regression models and panel data, outlining key concepts such as the definition of panel data, benefits of using panel data including controlling for individual heterogeneity, and limitations of panel data including problems with data collection.
2. Panel data involves observing the same cross-section of individuals, countries, firms etc. over multiple time periods, allowing analysis of both time and individual variability.
3. Using panel data offers advantages over cross-sectional or time series data alone, such as better accounting for unobserved heterogeneity and enabling analysis of dynamic adjustments over time.
This document provides an overview of statistics as a field of study. It discusses the meaning and importance of statistics, as well as data collection methods like census and sampling. Classification, tabulation, and diagrammatic/graphic presentation of data are also covered. The document outlines key statistical concepts like estimation, hypothesis testing, and applications of statistics in various disciplines like industry, commerce, agriculture and more. Common statistical techniques like correlation, regression, probability distributions, and statistical quality control are also mentioned.
This is a short introduction course to Stata statistical software version 9. The course still applies to later versions of Stata, too. The course duration was 9 hours. It has been given at the Faculty of Economics and Political Science, Cairo University.
This document provides an overview of time series analysis. It defines a time series as numerical data obtained at regular time intervals that occurs in many domains like economics and finance. The goals of time series analysis are to describe, summarize, fit models to, and forecast time series data. Time series are different from other data as observations are not independent. The document discusses the various components of time series including trends, seasonality, cycles, and irregular variations. It provides examples of decomposing time series into these components to better understand the underlying patterns in the data.
The document provides an overview of a business statistics course, including topics covered, applications in different business fields, and examples of descriptive statistics. The course covers topics such as data collection, descriptive statistics, statistical inference, and the use of computers for analysis. Descriptive statistics are used to summarize parts cost data from 50 car tune-ups, finding an average cost of $79. Inferential statistics are used to estimate population characteristics based on sample data.
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.
Here are some common sources of primary and secondary data:
Primary data sources:
- Surveys (questionnaires, interviews)
- Experiments
- Observations
- Focus groups
Secondary data sources:
- Government data (census data, vital statistics)
- Published research studies
- Organizational records and documents
- Media reports
- Commercial data providers
This document discusses methods for measuring trends in time series data. It describes secular trends as long-term movements in data over time, which can be upward, downward, or stagnant. Four common methods for measuring trends are discussed: graphical/freehand method, method of semi-averages, moving averages method, and least squares method. The graphical method involves visually fitting a smooth curve to the data points, while the semi-averages method divides the data into two sets and connects the midpoints using a straight line. Strengths and weaknesses of each approach are also presented.
Statistics is the collection and analysis of data. There are two main branches: descriptive statistics, which organizes and summarizes data, and inferential statistics, which uses descriptive statistics to make predictions. Statistics starts with a question and uses data to provide information to help make decisions. It is widely used in business, health, education, research, social sciences, and natural resources.
This chapter introduces the basic concepts and terminology of statistics. It discusses two main branches of statistics - descriptive statistics which involves collecting, organizing and summarizing data, and inferential statistics which allows drawing conclusions about populations from samples. The chapter also covers variables, populations, samples, parameters, statistics and how to organize and visualize data through tables, charts and graphs. It emphasizes that statistics helps turn data into useful information for decision making in business.
This document provides an introduction to statistics, covering key topics such as what statistics is, its functions, applications in business, and subject matter. Statistics is defined as both a set of numerical data and a set of techniques for collecting, organizing, analyzing, and interpreting quantitative data. It serves functions like simplifying complex facts, providing comparisons, and forecasting. Statistics is used widely in business decision making across areas like marketing, finance, and operations. The subject matter of statistics has two parts - descriptive statistics, which summarizes data, and inferential statistics, which makes conclusions about large groups by studying samples.
This document discusses panel data and methods for analyzing it. Panel data contains observations on multiple entities like individuals, states, or school districts that are observed at different points in time. This allows controlling for factors that are constant over time but vary across entities. Fixed effects regression is introduced as a method that eliminates the effect of any time-invariant characteristics. The document provides examples of how to specify fixed effects models using binary regressors or demeaning the data, and notes these produce identical estimates.
This document provides an overview of questionnaire surveys and their design. It discusses key aspects of developing and conducting a questionnaire survey such as defining objectives, conceptualizing variables, exploring concepts through focus groups and interviews, designing the questionnaire, and testing methods. The document also covers factors that affect questionnaire surveys, advantages and disadvantages, psychology of asking questions, and ensuring validity and reliability. The overall summary is that this document outlines best practices for designing and implementing a rigorous questionnaire survey from start to finish.
This document provides an introduction and overview of labor economics. It discusses key topics including:
- What labor economics studies, including the interaction of workers and employers in labor markets and how this determines wages, employment, and income.
- The microeconomic and macroeconomic techniques used to study labor markets, including individual behavior and interactions between labor markets and other markets.
- The importance of labor economics for understanding socioeconomic issues, its quantitative impact on national income, and unique characteristics of labor.
- How the field has evolved from a more descriptive, historical approach to incorporating applied microeconomic and macroeconomic theory.
Primary and Secondary Data collection - Ajay Anoj & GokulAJAY ANOJ KUMAR
The document discusses primary and secondary sources of data collection for research. It defines primary data as data collected directly by the researcher for the purposes of the research project. Secondary data is defined as data that was previously collected by others. The document outlines various methods for collecting both primary and secondary data, including surveys, interviews, observation, and reviewing published sources. It also compares primary and secondary data and discusses best practices for selecting an appropriate data collection method based on factors like the research objective, availability of funds, and required precision.
This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.
This document discusses the use of dummy variables in econometric modeling. It begins by explaining that some variables cannot be quantified numerically and provides examples where dummy variables would be used. It then discusses how dummy variables are incorporated into regression models, including intercept dummy variables, slope dummy variables, and dummy variables for multiple categories. The document also covers seasonal dummy variables and concludes by explaining the Chow test and dummy variable test for testing structural stability using dummy variables.
This document provides an overview of time series analysis and its key components. It discusses that a time series is a set of data measured at successive times joined together by time order. The main components of a time series are trends, seasonal variations, cyclical variations, and irregular variations. Time series analysis is important for business forecasting, understanding past behavior, and facilitating comparison. There are two main mathematical models used - the additive model which assumes data is the sum of its components, and the multiplicative model which assumes data is the product of its components. Decomposition of a time series involves discovering, measuring, and isolating these different components.
This document summarizes key descriptive statistics measures used to describe data, including measures of location (mean, median, mode) and measures of variability (range, interquartile range, variance, standard deviation, coefficient of variation). It provides examples calculating these measures using a sample data set of monthly rents for one-bedroom apartments. Formulas and explanations are given for how to compute each measure.
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
Introduction to statistics...ppt rahulRahul Dhaker
This document provides an introduction to statistics and biostatistics. It discusses key concepts including:
- The definitions and origins of statistics and biostatistics. Biostatistics applies statistical methods to biological and medical data.
- The four main scales of measurement: nominal, ordinal, interval, and ratio scales. Nominal scales classify data into categories while ratio scales allow for comparisons of magnitudes and ratios.
- Descriptive statistics which organize and summarize data through methods like frequency distributions, measures of central tendency, and graphs. Frequency distributions condense data into tables and charts. Measures of central tendency include the mean, median, and mode.
This course introduces students to statistical techniques for business decision making. Students will learn to analyze and present business data using appropriate software and statistical tools. Topics covered include descriptive statistics, probability, sampling, hypothesis testing, regression analysis, and comparing means of two and three groups. Assessments include a midterm, project, and final exam. Statistics are used to organize and analyze information to make it more easily understood, allowing judgments about the world. Descriptive statistics describe characteristics of data sets, while inferential statistics allow inferences about populations from data samples.
This chapter discusses various methods for organizing and presenting data through tables and graphs. It covers techniques for categorical data like summary tables, bar charts, pie charts and Pareto diagrams. For numerical data, it discusses ordered arrays, stem-and-leaf displays, frequency distributions, histograms, frequency polygons and ogives. It also introduces methods for presenting multivariate categorical data using contingency tables and side-by-side bar charts. The goal is to choose the most effective way to summarize and communicate patterns in the data.
This document summarizes key concepts from an introduction to organizational behavior course. It defines organizational behavior as the study of human behavior in organizational settings and how it interfaces with the organization. It discusses different models of OB and the major contributing disciplines. It also summarizes several seminal studies including the Hawthorne experiments which highlighted the importance of social and psychological factors in organizations.
This document discusses the role of statistics in business decision making. It describes descriptive statistics, which presents data in a way that is easier to understand through charts and graphs. Descriptive statistics measures central tendency and the spread of data using metrics like mean, median, mode, range, and standard deviation. The document also covers inferential statistics, which analyzes data samples to estimate parameters and test hypotheses. Examples are given of how statistics are used in various business contexts like Wall Street analysis and clothing design to draw conclusions from raw data and inform future decisions.
This document provides an overview and instructions for using IBM SPSS Statistics 19. It includes tutorials for basic functions like opening data files, running analyses, and viewing results. It also covers more advanced topics such as reading different data file types, using the Data Editor to enter and define variable properties, handling missing data, and working with multiple data sources. The document is intended to help new users learn the main capabilities and interface of IBM SPSS Statistics.
The document provides non-consolidated and consolidated financial statements for companies XYZ Inc. and ABC Ltd. over four years. It shows assets, liabilities, equity, revenues and expenses increasing over time. Exchange rates fluctuated between years. The financial statements indicate the companies grew in size and profitability through the four years.
Basic Business Statistics Chapter 3Numerical Descriptive Measures
Chapters Objectives:
Learn about Measures of Center.
How to calculate mean, median and midrange
Learn about Measures of Spread
Learn how to calculate Standard Deviation, IQR and Range
Learn about 5 number summaries
Coefficient of Correlation
There are two main types of data: primary and secondary. Primary data is collected directly by the researcher and is more accurate, but takes more time to collect. Secondary data is collected by others and is easier to obtain but less accurate. A population includes all individuals relevant to a study, while a sample is a subset of the population that is studied. Common data collection methods include interviews, questionnaires, and pilot surveys to test questions. Data can be quantitative, involving numerical values, or qualitative, involving non-numerical attributes. Quantitative data can be continuous, like heights, or discrete, like shoe sizes.
This document provides an overview of key concepts in descriptive statistics that are covered in Chapter 3, including measures of central tendency, variation, and shape. It introduces the mean, median, mode, variance, standard deviation, range, interquartile range, and coefficient of variation as common statistical measures used to describe the properties of numerical data. Examples are given to demonstrate how to calculate and interpret these descriptive statistics. The chapter aims to help readers learn how to calculate summary measures for a population and construct graphical displays like box-and-whisker plots.
Find best statistics experts to do your Business Statistics Assignments. Just send us an email at support@homeworkguru.com with your homework assignment and we will help you with the best statistics resources available online.
This chapter discusses techniques for time-series forecasting and index numbers. It begins by explaining the importance of forecasting for governments, businesses and other organizations. It then outlines common qualitative and quantitative forecasting approaches, with a focus on time-series methods that use historical data patterns to predict future values. The chapter describes how to decompose a time series into trend, seasonal, cyclical and irregular components. It also explains techniques for smoothing time-series data, including moving averages and exponential smoothing. Finally, it covers methods for time-series forecasting based on trend lines, including linear, quadratic, exponential and other models.
This document provides an introduction to statistics and statistical concepts. It covers topics such as course objectives, purposes of statistics, population and sampling, types of data and variables, levels of measurement, and nominal level of measurement. The key points are that statistics can describe, summarize, predict and identify relationships in data, and that there are different levels of variables from nominal to ratio scales.
Comparative study of eCommerce portals - jabong, yebhi, myntraVineela Kanapala
This document compares three Indian ecommerce portals: Jabong, Yebhi, and Myntra. It describes the features of each portal, including payment options, product browsing, and analytics tools. It also provides a critical analysis of pros and cons for each. Statistics are presented on search interest and traffic for the three portals based on Google Trends and other traffic data sources. Jabong has the highest traffic and revenue, while Myntra has the fastest page loading times and largest product selection. Overall the document analyzes and compares key aspects of the three major Indian ecommerce companies.
This document discusses inventory management. It defines inventory as physical goods held by an organization awaiting use, processing, or sale. The purpose of holding inventory is to ensure continuous production and sales despite fluctuating demand. Effective inventory management aims to maintain optimal inventory levels to balance costs with avoiding stockouts. Tools for inventory management include determining stock levels, safety stocks, economic order quantity, ABC analysis, and inventory reports. The document also outlines different inventory ordering systems.
Probability and Statistics,
Gamma Function,
Formulas,
Numerical,
Practical Application,
BITS Pilani Curriculum,
First Year Notes
For more study material, visit:
www.akshansh.weebly.com
This document provides an overview of statistics as a subject. It begins by defining statistics as both numerical data and statistical methods. It then discusses various types of data including primary and secondary data. Key aspects of working with data are covered such as classification, tabulation, presentation, analysis, and interpretation. The importance of statistics in fields like business, economics, and education is highlighted. Limitations of statistics and causes of distrust are also reviewed.
This document provides an introduction to statistics and data collection methods. It discusses key concepts such as:
1. The difference between economic and non-economic activities, and definitions of common economic roles like consumers, producers, service holders and service providers.
2. The stages of collecting statistical data, including primary and secondary sources, methods of collecting primary data, and the differences between primary and secondary data.
3. Methods of organizing raw data through classification, frequency distributions, and other statistical techniques. Common approaches to presenting organized data are also outlined, including tables, diagrams and graphs.
4. Sampling methods like census surveys and sample surveys are introduced, along with the differences between them. Key organizations involved in
DEFINITION OF STATISTICS,IMPORTANCE & LIMITATIONS OF STATISTICS,STATISTICAL INVESTIGATION,COLLECTION OF DATA,SOURCES OF DATA,PRIMARY DATA,SECONDARY DATA,QUESTIONNAIRE,SCHEDULE,TABULATION OF DATA,COLLECTION OF DATA,STATISTICS
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.
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.
Mini project file for MBA student of aktu first years9101hubham
Statistics is applied in many fields to help promote human welfare and frame suitable policies. Governments are major collectors and users of statistical data. Statistics also helps businesses analyze activities and make informed decisions through market research. Statistical data and methods aid in understanding economic problems and forming economic policies. Statistics is used in psychology, education, and natural sciences to measure human traits and abilities through tests, and aid medical diagnosis by analyzing factual health data.
This document discusses the scope and uses of statistics across various fields such as planning, economics, business, industry, mathematics, science, psychology, education, war, banking, government, sociology, and more. It outlines functions of statistics like presenting facts, testing hypotheses, forecasting, policymaking, enlarging knowledge, measuring uncertainty, simplifying data, deriving valid inferences, and drawing rational conclusions. It also covers characteristics, advantages, and limitations of statistics.
Statistics is the science of collecting, organizing, analyzing, and interpreting numerical data. It can involve collecting data from an entire population or a sample. The data is used to describe situations accurately and make predictions. Some key aspects of statistics include collecting data through surveys or existing sources, organizing and presenting it in tables or graphs, analyzing patterns in the data, and interpreting the results.
Statistics is the systematic collection, organization, analysis, and interpretation of data. It plays an important role in decision making by helping extract meaningful information from raw data. There are two main types of statistics - descriptive statistics which summarizes and presents data, and inferential statistics which makes inferences, tests hypotheses, and determines relationships in the data. Statistics has many applications in fields like business, medicine, economics and more. It helps simplify complex data, enable comparisons, identify trends, and aid decision making. Common statistical terms include population, sample, variables, attributes, and parameters. Data can be collected through various methods including direct observation, interviews, questionnaires, and more.
Statistics is the science of collecting, organizing, summarizing, analyzing, and interpreting data. It has its origins in Latin and other languages and refers to quantitative aspects of data management and meaningful interpretation. Statistics can be used in both plural and singular senses - referring either to numerical data or the methods used to analyze data. It is useful for converting random data into understandable information to aid in decision making. Statistics has important applications in business, government, industry, economics, and other fields for functions like presenting information simply, comparing facts, formulating policies, and forecasting.
1. The document discusses the meaning, uses, functions, importance and limitations of statistics. It defines statistics as the collection, presentation, analysis and interpretation of numerical data.
2. Statistics has various uses across different fields such as policy planning, management, education, commerce and accounts. It helps present facts precisely and enables comparison, correlation, formulation and testing of hypotheses, and forecasting.
3. While statistics is important for planning, administration, economics and more, it also has limitations such as only studying aggregates, numerical data, and being an average. Statistics can also be misused if not used carefully by experts.
This document provides an introduction and overview of statistics. It discusses that statistics refers to both the collection and analysis of quantitative data, as well as the scientific methods used. The document outlines the key stages of statistics including data collection, organization, presentation, analysis and interpretation. It also discusses the nature of statistics as both a science and an art. The subject matter is divided into descriptive and inferential statistics. Limitations, scope, functions and importance of statistics are also summarized.
This document provides an introduction to statistics, including definitions, objectives, functions, scope, and limitations. It defines statistics as the science of collecting, analyzing, and interpreting quantitative data. The objectives of statistics include making sense of large data sets and using data to forecast trends and examine changes. Statistics has broad applications across government, business, economics, science and other fields. However, it also has limitations such as ignoring qualitative factors and not revealing all details. The document also outlines the steps involved in a statistical investigation, including planning and executing a study.
This document provides an introduction to statistics. It defines statistics as the collection, organization, analysis, and interpretation of numerical data. It discusses the key characteristics of statistics such as being aggregate facts, numerically expressed, and collected systematically. It also outlines some common measures of central tendency used in statistics like the mean, median, and mode which summarize the central or typical values in a data set. Finally, it discusses the importance of presenting data through tables and charts to facilitate analysis and interpretation.
For a detailed explanation Watch the Youtube video:
https://youtu.be/cZlGTckM1AE
introduction to statistics,origin definition,characteristics of statistics, Data collection- primary data, secondary data, difference, sources of primary and secondary data collection, questionnaire vs schedules, limitations of statistics, scrutiny of data
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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
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🌏 The Asia-Pacific Healthcare Powerhouses
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Greetings,
It is our pleasure to share with you our latest energy news from
NewBase 08 May 2025 Energy News issue - 1786 by Khaled Al Awadi
Regards
Founder & Senior Editor - NewBase Energy
Khaled M Al Awadi, Energy Consultant
MS & BS Mechanical Engineering (HON), USAGreetings,
It is our pleasure to share with you our latest energy news from
NewBase 08 May 2025 Energy News issue - 1786 by Khaled Al Awadi
Regards
Founder & Senior Editor - NewBase Energy
Khaled M Al Awadi, Energy Consultant
MS & BS Mechanical Engineering (HON), USAGreetings,
It is our pleasure to share with you our latest energy news from
NewBase 08 May 2025 Energy News issue - 1786 by Khaled Al Awadi
Regards
Founder & Senior Editor - NewBase Energy
Khaled M Al Awadi, Energy Consultant
MS & BS Mechanical Engineering (HON), USAGreetings,
It is our pleasure to share with you our latest energy news from
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Founder & Senior Editor - NewBase Energy
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Business Statistics Notes for Business and Commerce Department
1. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 1
CHAPTER 1
INTRODUCTION
Origin & growth of Statistics
The word Statistics has been derived from;
Latin word status, Italian word Statista, German word Statistik means a ‘Political State’
or The state means art.
The word statistics is found in Shakespeare’s Hamlet (1602 A.D).
In Milton’s ‘Paradise Regained’ (1671 A.D) which is used in the sense of a person
who is well versed in State matters, Helping High Officers and framing government
Policies.
W.Hooper in 1770 A.D used the word Statistics in his translation of elements of
Erudition well known book of the ancient times which was written by the Baron B.F
Bielfel contains definition of the subject as the science that teach us what is Political
arrangement all the modern states of know world.
In 1787 European Professor E.A.W Zimermann defined the world Statistics as the
branch of Political knowledge which has relative Power of the several modern states
the power arising from their natural advantages the industry and Civilization of their
inhabitants and the wisdom of their Government.
In the 18th
century the term ‘Statistical Inquiry’ was used by John Sinclair as ’Inquiry
respecting’ the population the political circumstances, the Production of a country and
their matter.
During 18th
century the term statistics became more popular & began to be used in the
sense of numerical Royal statistical Society was founded in England in 1834.
Meaning of Statistics
The word Statistics is used in two different senses plural and singular , and its plural form
it refers to the numerical data, collected in systematic manner with some definite aim or
object in view such as number of persons suffering from Malaria in different colonies of
Badin.
2. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 2
In the singular form the word statistics means the science of statistics or the subject itself.
It includes methods and principles concerned with collection, analysis and interpretation
of numerical data.
Relationship of Statistics with other Science
1. Statistics and Commerce
a) Organization of Business
b) Production
c) Scientific Management & Business forecasting
d) Purchases
2. Statistics & Economics
a) Consumption
b) Production
c) Exchange
d) Public Finance
e) Input-output analysis
3. Statistics & State affairs
4. Statistics & Economics Planning
5. Statistics & Mathematics
6. Statistics & Astronomy
7. Statistics & Meteorology
8. Statistics & Agriculture
9. Statistics & Biology
10. Statistics, Maths & Economics
3. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 3
CHAPTER # 2
CHARATERISTICS & DIVISION OF STATISTICS
Statistic’s Definition:
“Statistics are aggregate of facts affected to a marked extent by the multiplicity of causes
numerically expressed ,enumerated or estimated according to a seasonable standard of
accuracy ,collected in a systematic manner, for a predetermined purpose and place in
relation to each other”(by: Horace Secrist)
Modified Definition of Statistics:
“Statistics are the numerical statements of facts capable of analysis and interpretation and
the science of Statistics is the study of the principle and the methods in collecting
presenting analysis and interpreting the numerical in any field of inquiry”.
Characteristics
1) Statistics are aggregate of facts
2) Statistics are affected to a marked extent by multiplicity of cause
3) Statistics are numerically expressed
4) Statistics are estimated according to a reasonable standard of Statistics
5) Statistics must be collected in systematic manner
6) Statistics must be collected for predetermined purpose
7) Statistics must be placed in relation to each other
Division of Statistics
1) Statistical Method
2) Applied Statistics
2.1) Description applied Statistics
2.2) Scientific applied Statistics
4. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 4
Statistical Method:
It is include all the definite rules of procedure and techniques which are used in the
collection, classification, tabulation comparison and interpretation of data relating to any
particular inquiry. Or
Statistical Methods are devices by which complex and numerical data are systematically
treated so as to present comprehensive and intelligible view of them.
Steps of Statistical Method
1) Collection of Data 2) Classification of Data 3) Tabulation of Data 4)Presentation
of Data 5)Interpretation of Data 6) Forecasting
Importance of Statistics
1) Statistics discloses casual connection between related facts.
2) Statistics are an aid to supervision.
3) Statistics are the eyes of Administration.
4) Planning w/o statistics cannot be imagining.
5) Statistics is indispensable in social studies.
6) Statistics is indispensable for state.
7) Statistics constitute a record of the part knowledge.
8) Statistics is the arithmetic of human welfare/wellbeing.
9) Utility to Bankers.
10) Utility in Agriculture.
11) Utility in Insurance Companies.
12) Utility of Government.
13) Utility to Brokers, Speculators & Investors.
14) Utility to Business & Management.
15) Usefulness in Commerce.
16) Importance in Economics.
17) Desirability in Research.
18) Universal Applicability.
Limitation of Statistics
A. Statistics Laws are true on the average
B. Statistics does not study quantitative phenomenon
5. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 5
C. Statistics does not study Individuals
D. Statistics cannot applied on heterogeneous data
E. Statistics is liable to be misused
Functions of statistics
1) Statistics simplifies complexities
2) Statistics enlarged Individuals experience
3) Statistics tests the Laws of other Sciences
4) Statistics guide in forming policies
5) Statistics enables realization of magnitude
Functions of Statistician
1) A Statistician first of all draws a plan of the inquiry
2) He decides the scope of inquiry
3) He supervise the work of the field investigators who are appointed in the collection
of the data
4) He prepares a suitable questionnaire for conducting the inquiry
5) He presents the data collected by the investigators
6) He conducts the various statistical parameters of the data collected
7) He draws certain conclusions from the various statistical parameters, thus
computed by interpreting the data
8) He forecasts the happenings of various phenomenons with the help of the
conclusion drawn
6. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 6
CHAPTER #3
DATA
Preliminaries to the collection of data
1) Object & Scope of inquiry
2) Nature & Type of inquiry
3) Statistical Units
4) Degree of accuracy
2) Nature & Type of inquiry
Primary or Secondary data
Census or Sample
Open or Confidential
Direct or indirect
Regular or Adhoc
Collection of Primary and Secondary Data
1) Direct Personal Investigation: collecting data by you.
Advantages
This information collected by this method is reliable and accurate.
It is a good method for intensive investigation.
This method gives a satisfactory results provided scope of inquiry narrow.
Disadvantages
This method is not suitable for extensive inquiry.(e.g. population)
It requires a lot of expenses at time.
This bias on the part of investigator can damage the whole inquiry.
Sometimes the information may select to the answer the question.
2-Indirect Personal Investigation: collecting data by asking question from surrounding.
a) Precautions of collecting Indirect Personal Investigation.
7. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 7
Informant should;
Have full knowledge of the problem.
Be in a position to expenses by him correctly.
Be free from bias.
Not gives color to the facts.
b) More ever proper allowance should be made for the inherent optimism or pessimism of
the informant .e.g. if informer suffers due to you by information, you have to compensate
him.
c) Absolute reliance should not be given to the information given by one person but a
number of persons should be interviewed to express their news.
Advantages
It is less expensive and takes less time.
The information is collected from witness who does not feel shy in giving the exact
information.
It is a good method for conducting an extensive inquiry.
Disadvantages
Sometimes the time taken by the witness in replying the questions may be the pretty
long.
It is possible that the witness may not have full knowledge of the problem.
3-Investigation through questionnaire: according to this method a list of standard of
question relating to the particular investigation as prepare this list of question is called
questionnaire.
Advantages
It is the less expensive.
Collection of information can be from wide area.
This method can ensure a reasonable standard of accuracy.
Disadvantages
Most of the informants do not take the trouble of filling in the questionnaire &
sometime do not even the return the questionnaire.
8. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 8
Those who answer the questions give vague answer, those may be many errors in the
answer because there will be none to the explain questionnaire.
Precaution
a) A policed letter should be written to the informant emphasing the needs and the
usefulness of the problem under investigation.
b) choice of questionnaire: i) questions should be clear and understanding ii) they should
be few in number & easy iii) question should cooperative in nature.
4- Investigation through questionnaire by enumeration:
Advantages
This method is very useful for extensive inquiry.
In this method there is not expectation of vague answer.
Answers supplied are complete.
No-time wasted in collecting the questionnaire since the enumerates collect himself.
Disadvantages
This method is very expensive.
Accuracy in this methods depends upon the proper choice and training of enumerates.
5-Investigation through reports: investigating by different reports concerned with
problem.
6- Investigation through websites: visiting different website for collecting the data and
investigating the collected data for handling the problem.
Classification of Data
Characteristics of ideal classification given below;
1) It should be unambiguous.
2) Classes should be exhaustive & manually exclusive.
3) It should be stable.
4) It should be flexible.
9. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 9
Basis of Classification
1) Geographical/ Spatial Classification: Investigator should provide clear information
where he is going to investigate e.g. which area, location, places.
2) Chronological Classification: this classification concerned with the continuous time
series and sequence e.g. population of 18 crore from 1998-2014.
3) Qualitative Classification: This classification depends upon the qualities of a person
etc, e.g. a person’s skills, intelligence because these can be observed not measured.
4) Simple Classification: It is concerned with the classification of the data in two section
or parts e.g. considering gender classification ,it can be subdivided into two more section
like male and female.
5) Manifold Classification: it is concerned with the classification of data may be
divided in more section or parts. e.g. same above we take gender divided into two male
and female then more it may be subdivided into rural and urban then color then married
and unmarried etc.
10. Business Statistics Business Administration-2k13
Compiled by: Seetal Daas University of Sindh Laar Campus @ Badin 10