This document provides an introduction to statistics and its uses in business. It outlines two main branches of statistics - descriptive statistics which involves collecting, summarizing and presenting data, and inferential statistics which uses data from a sample to draw conclusions about a larger population. The document then discusses key statistical concepts like variables, data, populations, samples, parameters and statistics. It explains how descriptive and inferential statistics are used to summarize data, draw conclusions, make forecasts and improve business processes. Finally, it introduces the DCOVA process for examining and concluding from data which involves defining variables, collecting data, organizing data, visualizing data and analyzing data.
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 provides an overview of a course on business statistics. It includes 10 chapters that cover topics like descriptive statistics, measures of central tendency, measures of dispersion, probability, and the use of Excel for statistical analysis. The document also provides learning objectives for each chapter. For example, chapter 2 focuses on descriptive statistics and covers collecting, processing, and presenting data. It aims to describe descriptive and inferential statistics and explain how to collect, classify, tabulate, and present data diagrammatically and graphically using various charts and graphs.
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.
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
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.
Statistics are used by organizations to measure and analyze business performance. American Express uses statistics such as total returns to shareholders, numbers of cardholders by age group, and cardholder spending by age to analyze business units, identify targeted customer groups, and inform marketing campaigns. Statistics on labor force characteristics by gender help conclude that male monthly incomes are typically higher than females, though this does not necessarily mean males spend more.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
This chapter introduces basic concepts in statistics including the difference between populations and samples, parameters and statistics. It discusses the two main branches of statistics - descriptive statistics which involves collecting, summarizing and presenting data, and inferential statistics which involves drawing conclusions about populations from samples. The chapter also covers different types of data that can be collected including categorical vs. numerical, discrete vs. continuous, and different measurement scales for levels of data.
The Course Aim, Purpose and Learning Outcomes
Course Aim and Purpose:
This course has aims provide a practical and approach to in the use of statistics in order for the students to gain an understanding about: -
Basic statistical theory
Management statistics used in different organizations; and
Statistical techniques used to undertake research.
Learning Outcomes:
It is intended for a student to gain an understanding: -
how to use computers to undertake statistical tasks
how to explore and understand data
How to display data.
how to investigate the relationship between variables.
about statistical confidence intervals
how to use and select basic statistical hypothesis tests
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.
In this chapter you learn:
Definition of Statistics & Identify variables in a statistics.
Types of Statistics
Distinguish b/w quantitative & qualitative variables.
Determine the 4 levels of measurement.
Identify populations & samples.
Distinguish different types of Sampling
This document discusses data analysis and presentation. It covers qualitative and quantitative analysis methods, scales of measurement that determine appropriate analysis, tools to support analysis, and theoretical frameworks like grounded theory. The purpose of analysis is to obtain useful information by describing, comparing, and identifying relationships in data. Findings should be presented rigorously with careful claims supported by evidence.
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.
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 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.
This document provides an overview of a business statistics course, including:
- Descriptions of fundamental statistical concepts like populations, samples, and types of data including nominal, ordinal, interval, and ratio scales.
- Examples of how to compute and represent different data types through frequency tables, histograms, scatter plots, and other graphs.
- Applications of statistics in fields like demography, econometrics, and the growing area of business analytics.
This document provides an outline for a Probability and Statistics course. It covers topics such as introduction to statistics, tabular and graphical representation of data, measures of central tendency and variation, probability, discrete and continuous distributions, and hypothesis testing. Descriptive statistics are used to summarize and describe data, while inferential statistics allow predictions and inferences about a larger data set based on a sample. Variables can be classified based on their scale of measurement as nominal, ordinal, interval, or ratio. Graphical representations include pie charts, histograms, bar graphs, and frequency polygons. Measures of central tendency include the mean, median, and mode.
Statistics can be defined in both a singular and plural sense. In the singular sense, it refers to statistical methods for collecting, analyzing, and interpreting numerical data. In the plural sense, it refers to the actual numerical facts or data collected. Statistics involves systematically collecting, organizing, presenting, analyzing, and interpreting numerical data to describe features and characteristics. It allows for comparing facts, establishing relationships, and facilitating policymaking and decision making. However, statistics only studies aggregates and averages, not individual cases, and results are true only on average. It also requires properly contextualizing and referencing results.
The document provides an introduction to panel data analysis. It defines time series data, cross-sectional data, and panel data, which combines the two. Panel data has advantages over single time series or cross-sectional data like more observations, capturing heterogeneity and dynamics. Panel data can be balanced or unbalanced, and micro or macro. The document demonstrates structuring panel data in Excel for empirical analysis in Eviews, including an activity to arrange time series data into a panel data format.
A confidence interval provides a range of values that is likely to include an unknown population parameter, based on a given confidence level. A 95% confidence level means there is a 95% chance the interval contains the true population parameter. Confidence intervals are useful because they allow researchers to account for sampling error/variability and make inferences about populations based on sample data. The higher the confidence level, the wider the interval needs to be to achieve that level of confidence.
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.
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.
This document discusses the different meanings and definitions of statistics. It explains that statistics has three different meanings: (1) plural sense referring to numerical facts and figures collected systematically, (2) singular sense referring to the science of collecting, analyzing, and presenting numerical data, and (3) plural of the word "statistic" referring to numerical quantities calculated from samples. The document also provides several definitions of statistics from different authors, describing it as the science of collecting, organizing, and interpreting quantitative data.
This document provides an overview of key terminology and concepts in statistics. It discusses topics like populations and samples, variables and their measurement, levels of measurement, research methods like correlational analysis and experiments, and mathematical notation used in statistics. The goal is to introduce readers to what statistics is about at a high level and prepare them for further study of important statistical concepts.
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 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.
This chapter introduces basic concepts in statistics including the difference between populations and samples, parameters and statistics. It discusses the two main branches of statistics - descriptive statistics which involves collecting, summarizing and presenting data, and inferential statistics which involves drawing conclusions about populations from samples. The chapter also covers different types of data that can be collected including categorical vs. numerical, discrete vs. continuous, and different measurement scales for levels of data.
The Course Aim, Purpose and Learning Outcomes
Course Aim and Purpose:
This course has aims provide a practical and approach to in the use of statistics in order for the students to gain an understanding about: -
Basic statistical theory
Management statistics used in different organizations; and
Statistical techniques used to undertake research.
Learning Outcomes:
It is intended for a student to gain an understanding: -
how to use computers to undertake statistical tasks
how to explore and understand data
How to display data.
how to investigate the relationship between variables.
about statistical confidence intervals
how to use and select basic statistical hypothesis tests
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.
In this chapter you learn:
Definition of Statistics & Identify variables in a statistics.
Types of Statistics
Distinguish b/w quantitative & qualitative variables.
Determine the 4 levels of measurement.
Identify populations & samples.
Distinguish different types of Sampling
This document discusses data analysis and presentation. It covers qualitative and quantitative analysis methods, scales of measurement that determine appropriate analysis, tools to support analysis, and theoretical frameworks like grounded theory. The purpose of analysis is to obtain useful information by describing, comparing, and identifying relationships in data. Findings should be presented rigorously with careful claims supported by evidence.
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.
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 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.
This document provides an overview of a business statistics course, including:
- Descriptions of fundamental statistical concepts like populations, samples, and types of data including nominal, ordinal, interval, and ratio scales.
- Examples of how to compute and represent different data types through frequency tables, histograms, scatter plots, and other graphs.
- Applications of statistics in fields like demography, econometrics, and the growing area of business analytics.
This document provides an outline for a Probability and Statistics course. It covers topics such as introduction to statistics, tabular and graphical representation of data, measures of central tendency and variation, probability, discrete and continuous distributions, and hypothesis testing. Descriptive statistics are used to summarize and describe data, while inferential statistics allow predictions and inferences about a larger data set based on a sample. Variables can be classified based on their scale of measurement as nominal, ordinal, interval, or ratio. Graphical representations include pie charts, histograms, bar graphs, and frequency polygons. Measures of central tendency include the mean, median, and mode.
Statistics can be defined in both a singular and plural sense. In the singular sense, it refers to statistical methods for collecting, analyzing, and interpreting numerical data. In the plural sense, it refers to the actual numerical facts or data collected. Statistics involves systematically collecting, organizing, presenting, analyzing, and interpreting numerical data to describe features and characteristics. It allows for comparing facts, establishing relationships, and facilitating policymaking and decision making. However, statistics only studies aggregates and averages, not individual cases, and results are true only on average. It also requires properly contextualizing and referencing results.
The document provides an introduction to panel data analysis. It defines time series data, cross-sectional data, and panel data, which combines the two. Panel data has advantages over single time series or cross-sectional data like more observations, capturing heterogeneity and dynamics. Panel data can be balanced or unbalanced, and micro or macro. The document demonstrates structuring panel data in Excel for empirical analysis in Eviews, including an activity to arrange time series data into a panel data format.
A confidence interval provides a range of values that is likely to include an unknown population parameter, based on a given confidence level. A 95% confidence level means there is a 95% chance the interval contains the true population parameter. Confidence intervals are useful because they allow researchers to account for sampling error/variability and make inferences about populations based on sample data. The higher the confidence level, the wider the interval needs to be to achieve that level of confidence.
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.
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.
This document discusses the different meanings and definitions of statistics. It explains that statistics has three different meanings: (1) plural sense referring to numerical facts and figures collected systematically, (2) singular sense referring to the science of collecting, analyzing, and presenting numerical data, and (3) plural of the word "statistic" referring to numerical quantities calculated from samples. The document also provides several definitions of statistics from different authors, describing it as the science of collecting, organizing, and interpreting quantitative data.
This document provides an overview of key terminology and concepts in statistics. It discusses topics like populations and samples, variables and their measurement, levels of measurement, research methods like correlational analysis and experiments, and mathematical notation used in statistics. The goal is to introduce readers to what statistics is about at a high level and prepare them for further study of important statistical concepts.
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 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.
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.
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.
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 to statistics. It defines statistics as the science of collecting, organizing, analyzing, and drawing conclusions from data. Data is defined as facts or figures collected for a specific purpose. The document outlines the characteristics of statistics and discusses the functions, scope and limitations of statistics. It also distinguishes between primary and secondary data, discrete and continuous data, and descriptive and inferential statistics.
This document provides an introduction and definition of statistics. It discusses statistics in both the plural and singular sense, as numerical data and as a method of study, respectively. It also outlines the basic terminologies in statistics such as data, population, sample, parameters, variables, and scales of measurement. Finally, it discusses the classification and applications of statistics as well as its limitations.
1) Statistics is the study of collecting, organizing, analyzing, and drawing conclusions from data. It involves sampling, hypothesis testing, and using statistical tests tailored to measurement scales and hypothesis types.
2) Descriptive statistics describe and summarize data quantitatively, while inferential statistics allow generalizing from samples to populations through statistical testing and other methods.
3) The document discusses differences between statistics and statistical data, types of data, levels of measurement, sampling techniques, and uses of statistics.
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.
This document provides an overview of quantitative techniques - 1 as part of a B.Com program. It discusses the meaning and definitions of statistics, the objectives of statistics which include determining values to represent data, estimating present trends and predicting the future. It also discusses descriptive and inferential statistics. The functions of statistics are presented, including presenting facts precisely, facilitating comparison, formulation and testing of hypotheses, forecasting, policy making, and measuring uncertainty. Learning objectives and outcomes are provided which are to understand the overview of statistics and data collection techniques. Examples of objectives and functions of statistics are discussed in detail. The session concludes with sample multiple choice questions to test understanding.
Chapter one Business statistics refereshYasin Abdela
1. Statistics is the science of collecting, organizing, analyzing, and interpreting numerical data. It helps make better decisions in fields like business and economics.
2. There are two main types of statistics: descriptive statistics which summarize and describe data, and inferential statistics which make inferences about populations based on samples.
3. The stages of a statistical investigation are data collection, organization, presentation, analysis, and interpretation of the data to draw conclusions.
This document introduces key concepts in statistics. It discusses the importance of observations in various fields like agriculture, industry, etc. It explains that statistics is used to make many important decisions in life by processing and analyzing numerical data under uncertain conditions. The document also distinguishes between descriptive and inferential statistics. It describes different types of variables like qualitative, quantitative, discrete, and continuous variables. Various methods of data presentation like frequency distributions and cross-tabulation are also introduced.
lecture-note-on-basic-statistics-prem-mann-introductory-statistics.pdfAtoshe Elmi
This document contains lecture notes on introductory statistics. It defines key terms like population, sample, variable, and qualitative and quantitative variables. It distinguishes between descriptive and inferential statistics. Descriptive statistics organizes and summarizes data using tables, graphs and measures. Inferential statistics uses samples to make predictions about populations. The document provides examples and exercises to help understand these basic statistical concepts.
Analysis of Waste Recycling Companies.pptxManikaGoyal13
This presentation aims to analyse the various waste recycling companies in India. It is a potential booming sector.
Currently I have analysed a single company, Gravita India.
Money Mindset Mastery_ Transform Your Financial Life.pdfpckhetal
Introduction
Purpose of the Book
Overview of why mastering your money mindset is crucial for financial success
Personal anecdotes or testimonials to illustrate the transformative power of a positive money mindset
What to Expect
Brief overview of the key topics covered in the book
Chapter 1: Understanding Your Money Mindset
Defining Money Mindset
Explanation of what a money mindset is
Different types of money mindsets: scarcity vs. abundance
Identifying Your Current Mindset
Self-assessment exercises
Common beliefs and attitudes towards money
Chapter 2: The Psychology of Money
Emotional Connections to Money
How emotions influence financial decisions
The role of childhood experiences and upbringing
Overcoming Money Anxiety
Techniques for managing financial stress
Mindfulness and money
Chapter 3: Breaking Negative Money Habits
Identifying Negative Patterns
Common self-sabotaging behaviors
Financial habits that hinder wealth accumulation
Strategies for Change
Practical steps to break negative habits
Success stories of individuals who transformed their money habits
Chapter 4: Building a Wealth Mindset
Characteristics of a Wealth Mindset
Traits and behaviors of financially successful individuals
The power of positive affirmations
Cultivating Abundance Thinking
Exercises to foster a mindset of abundance
Visualization techniques for financial success
Chapter 5: Setting Financial Goals
SMART Goals Framework
Specific, Measurable, Achievable, Relevant, Time-bound goals
Aligning Goals with Values
Ensuring financial goals reflect personal values and aspirations
Creating a vision board
Chapter 6: Practical Financial Management
Budgeting Basics
Creating and maintaining a budget
Tools and apps for effective budgeting
Saving and Investing
Importance of saving and strategies to save more
Introduction to investing and growing wealth
Chapter 7: Overcoming Financial Setbacks
Dealing with Debt
Strategies for managing and paying off debt
Understanding good debt vs. bad debt
Recovering from Financial Mistakes
Steps to bounce back from financial errors
Learning from setbacks and moving forward
Chapter 8: The Role of Gratitude and Generosity
Gratitude Practices
How gratitude can shift your financial perspective
Daily gratitude exercises
The Power of Giving
Benefits of generosity on personal wealth
Ways to give back and make a difference
Chapter 9: Building a Support System
Finding Financial Mentors
Importance of mentorship in financial growth
How to find and approach potential mentors
Creating a Money Mastery Group
Forming or joining a group for financial accountability
Benefits of collective financial wisdom
Chapter 10: Sustaining Your Money Mindset
Continual Learning
Resources for ongoing financial education
Books, courses, and seminars to consider
Routine Reviews and Adjustments
Regularly reviewing and adjusting your financial plans
Staying adaptable to life changes
2025年新版美国毕业证纽约州立大学莫里斯维尔农业技术学院文凭【q微1954292140】办理纽约州立大学莫里斯维尔农业技术学院毕业证(SUNY毕业证书)办本科毕业证【q微1954292140】纽约州立大学莫里斯维尔农业技术学院offer/学位证、留信官方学历认证(永久存档真实可查)采用学校原版纸张、特殊工艺完全按照原版一比一制作【q微1954292140】Buy State University of New York College of Agriculture & Technology at Morrisville Diploma购买美国毕业证,购买英国毕业证,购买澳洲毕业证,购买加拿大毕业证,以及德国毕业证,购买法国毕业证(q微1954292140)购买荷兰毕业证、购买瑞士毕业证、购买日本毕业证、购买韩国毕业证、购买新西兰毕业证、购买新加坡毕业证、购买西班牙毕业证、购买马来西亚毕业证等。包括了本科毕业证,硕士毕业证。
主营项目:
1、真实教育部国外学历学位认证《美国毕业文凭证书快速办理纽约州立大学莫里斯维尔农业技术学院在线制作硕士学历证书》【q微1954292140】《论文没过纽约州立大学莫里斯维尔农业技术学院正式成绩单》,教育部存档,教育部留服网站100%可查.
2、办理SUNY毕业证,改成绩单《SUNY毕业证明办理纽约州立大学莫里斯维尔农业技术学院买文凭》【Q/WeChat:1954292140】Buy State University of New York College of Agriculture & Technology at Morrisville Certificates《正式成绩单论文没过》,纽约州立大学莫里斯维尔农业技术学院Offer、在读证明、学生卡、信封、证明信等全套材料,从防伪到印刷,从水印到钢印烫金,高精仿度跟学校原版100%相同.
3、真实使馆认证(即留学人员回国证明),使馆存档可通过大使馆查询确认.
4、留信网认证,国家专业人才认证中心颁发入库证书,留信网存档可查.
《纽约州立大学莫里斯维尔农业技术学院毕业证定购美国毕业证书办理SUNY国外毕业证成绩单的办理流程》【q微1954292140】学位证1:1完美还原海外各大学毕业材料上的工艺:水印,阴影底纹,钢印LOGO烫金烫银,LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。
高仿真还原美国文凭证书和外壳,定制美国纽约州立大学莫里斯维尔农业技术学院成绩单和信封。做一个在线本科文凭SUNY毕业证【q微1954292140】办理美国纽约州立大学莫里斯维尔农业技术学院毕业证(SUNY毕业证书)【q微1954292140】在线制作硕士成绩单纽约州立大学莫里斯维尔农业技术学院offer/学位证办留学学历认证、留信官方学历认证(永久存档真实可查)采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决纽约州立大学莫里斯维尔农业技术学院学历学位认证难题。
美国文凭纽约州立大学莫里斯维尔农业技术学院成绩单,SUNY毕业证【q微1954292140】办理美国纽约州立大学莫里斯维尔农业技术学院毕业证(SUNY毕业证书)【q微1954292140】学历认证范本纽约州立大学莫里斯维尔农业技术学院offer/学位证成绩单工艺详解、留信官方学历认证(永久存档真实可查)采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决纽约州立大学莫里斯维尔农业技术学院学历学位认证难题。
美国文凭购买,美国文凭定制,美国文凭补办。专业在线定制美国大学文凭,定做美国本科文凭,【q微1954292140】复制美国State University of New York College of Agriculture & Technology at Morrisville completion letter。在线快速补办美国本科毕业证、硕士文凭证书,购买美国学位证、纽约州立大学莫里斯维尔农业技术学院Offer,美国大学文凭在线购买。
【q微1954292140】帮您解决在美国纽约州立大学莫里斯维尔农业技术学院未毕业难题(State University of New York College of Agriculture & Technology at Morrisville)文凭购买、毕业证购买、大学文凭购买、大学毕业证购买、买文凭、日韩文凭、英国大学文凭、美国大学文凭、澳洲大学文凭、加拿大大学文凭(q微1954292140)新加坡大学文凭、新西兰大学文凭、爱尔兰文凭、西班牙文凭、德国文凭、教育部认证,买毕业证,毕业证购买,买大学文凭,购买日韩毕业证、英国大学毕业证、美国大学毕业证、澳洲大学毕业证、加拿大大学毕业证(q微1954292140)新加坡大学毕业证、新西兰大学毕业证、爱尔兰毕业证、西班牙毕业证、德国毕业证,回国证明,留信网认证,留信认证办理,学历认证。从而完成就业。纽约州立大学莫里斯维尔农业技术学院毕业证办理,纽约州立大学莫里斯维尔农业技术学院文凭办理,纽约州立大学莫里斯维尔农业技术学院成绩单办理和真实留信认证、留服认证、纽约州立大学莫里斯维尔农业技术学院学历认证。学院文凭定制,纽约州立大学莫里斯维尔农业技术学院原版文凭补办,扫描件文凭定做,100%文凭复刻。
特殊原因导致无法毕业,也可以联系我们帮您办理相关材料:
1:在纽约州立大学莫里斯维尔农业技术学院挂科了,不想读了,成绩不理想怎么办???
2:打算回国了,找工作的时候,需要提供认证《SUNY成绩单购买办理纽约州立大学莫里斯维尔农业技术学院毕业证书范本》【Q/WeChat:1954292140】Buy State University of New York College of Agriculture & Technology at Morrisville Diploma《正式成绩单论文没过》有文凭却得不到认证。又该怎么办???美国毕业证购买,美国文凭购买,
How Abhay Bhutada Became India’s Highest Paid Executive with a Strong Social ...Raj Kumble
Explore how Abhay Bhutada rose to become India’s highest-paid executive with a ₹241.21 crore salary, while also making a meaningful impact through the Abhay Bhutada Foundation’s initiatives in education, science, and community welfare.
PPT1.pptx IMAHE PROCESSING AN TL THE BEST WAY TO SOLVED TASKjagat101213dhiman
In recent years, deep learning (DL) techniques, a subset of machine learning (ML), have
outperformed traditional ML approaches across numerous tasks, driven by several critical
advancements [3]. The proliferation of large datasets has been pivotal in enabling models to learn
intricate patterns and relationships, thereby significantly enhancing their performance [4].
Concurrently, advancements in hardware acceleration technologies, notably Graphics Processing
Units (GPUs) and Field-Programmable Gate Arrays (FPGAs) [5] have markedly reduced model
training times by facilitating rapid computations and parallel processing capabilities. These
advancements have substantially accelerated the training process.2 Moreover, enhancements in
algorithmic techniques for optimization and training have further augmented the speed and
efficiency of deep learning models, leading to quicker convergence and superior generalization
capabilities [4]. Deep learning techniques have demonstrated remarkable success across a wide
range of applications, including computer vision (CV), natural language processing (NLP), and
speech recognition. These applications underscore the transformative impact of DL in various
domains, where it continues to set new performance benchmarks [6, 7].
Deep learning models draw inspiration from the structure and functionality of the human
nervous system and brain. These models employ input, hidden, and output layers to organize
processing units. Within each layer, the nodes or units are interconnected with those in the layer
below, and each connection is assigned to a weight value. The units sum the inputs after multiplying
them by their corresponding weights [8]. Fig. 1 illustrates the relationship between AI, ML, and
DL, highlighting that machine learning and deep learning are subfields of artificial intelligence.
The objective of this research is to provide a comprehensive overview of various deep learning
models and compare their performance across different applications. In Section 2, we introduce a
fundamental definition of deep learning. Section 3 covers supervised deep learning models,
including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent
Neural Networks (RNN), Temporal Convolutional Networks (TCN), and Kolmogorov-Arnold
Networks (KAN). Section 4 reviews generative models such as Autoencoders, Generative
Adversarial Networks (GANs), and Deep Belief Networks (DBNs). Section 5 presents a
comprehensive survey of Transformer architecture. Deep Reinforcement Learning (DRL) is
discussed in Section 6, while Section 7 addresses Deep Transfer Learning (DTL). The principles
of hybrid deep learning are explored in Section 8, followed by a discussion of deep learning
applications in Section 9. Section 10 surveys the challenges in deep learning and potential
alternative solutions. In Section 11, we conduct experiments and analyze the performance of
different deep learning models using th
What are the Fundamentals of Economics??that 1 guy
Do you want to understand the basic economic questions that every nation faces?
This presentation explains why scarcity and choice are the basics of economics.
The Future of Debt Recovery_ Poonawalla Fincorp’s AI Revolution.pdfnickysharmasucks
This PowerPoint presentation outlines how Poonawalla Fincorp is redefining debt recovery through strategic use of AI. Beginning with a shift from reactive to proactive risk prediction, the slides highlight advancements in compliance, real-time customer engagement, operational automation, and ethical communication. Leadership under Arvind Kapil has propelled these initiatives, marrying technology with human empathy. The presentation concludes by emphasizing how Poonawalla Fincorp’s model is poised to set new industry standards in ethical, technology-enabled financial services.
Tareq Bushnaq is an Economics graduate from IE Business School. Previously, he interned as an M&A summer analyst at a regional investment bank in Dubai, where he worked on deals in a variety of sectors including industrials, healthcare, retail, hospitality, and technology. Prior to that, Tareq interned as an analyst at a corporate advisory firm in Madrid, where he specialized in growth equity transactions across Europe and North America.
1. 1 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[Please insert
any relevant
photo round this
box]
CHAPTER
1
[Introduction To Business Statistics]
[Subtopics:-]
[1.1] [Founder and Introduction Of Statistics]
[1.2] [Why Study Importance Of Statistics]
[1.3] [The Growth Of Statistics : Purpose Of Business Research Statistics
Modeling Strategies]
[1.4] [Definition Of Statistic]
[1.5] [Types Of Statistics]
[1.6] [Population versus Sample]
[1.7] [Basic Terms]
[1.8] [Elements Or Members]
[1.9] [Types Of Variables]
[Synopsis]:-
[Statistics introduce the business research TDC Theory Of Development
Cognition Concept :-]
[Statistics Interest To Educators, Researchers,
Entrepreneurs, Managers, Executives And
Students]
[May Adopt Their Understanding Becoming More
Analytical Using A Wide Range Of Other
Educational Material In Association Using
Statistics’ Techniques]
[Calculation Especially Engaging With Future
Decision Personal & Organization’s Productivity]
2. 2 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.1] [Founder and Introduction of Statistics]
[In the late 1920s, Dr. William Edward Deming has shown a great interest in Stewart’s work
on statistical control of processes and technical tool as well as control chart on industrial
production and management. In 1940, Dr. Dewing was hired by U.S Census Bureau
Department, enabled him to apply a complex statistical data together with the total
quality control management into industrial operations].
Figure 1: Quotes’ Founder Of Statistics: Dr. William Edward Deming
Table 1: Francis Galton, Karl Pearson, Dr. William Edwards Deming and Florence Night Angle
[Francis Galton is an English born in year
1822 and died in year
1911. He was accredited as one of the
principal founder of statistical theory. His
contributions to the statistics field by
introducing the standard deviation,
correlation, regression and the figures
application of human characteristics such
as height, weight, level of health condition
and so forth relating to University of South
Hampton].
[Karl Pearson (1857-1936) was a major
contributor to the early development of
statistics especially on chi-square test.]1
1
Karl Pearson (1857 – 1936), Department of Statistical Science, University College London (25 Sept 2008); Karl Pearson, School
Of Mathematics and Statistics, University of St Andrews (2003); John Aldrich (2008).
3. 3 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[Florence Night angle]
[1.2] [Why Study Importance of Statistics]
[Statistics plays a vital role almost in every field of our daily living activities inclusive
commercialization, business, physics, chemistry, economics, mathematics, biology,
botany, psychology, astronomy, so forth almost every career needs statistics theory
as well as application2]
[1.2.1] [Business Plan Production]
[Statistics plays an important role in business. A successful business man must be very quick
and accurate in decision making. For example, what behavioral dimensions or
characteristics would we expect to find in people with high achievement motivation in
business production? They would probably have the five following broad characteristics:]
[1.2.1.1] [Elements of Dimension on Number of Hours Employees’ Work Engagement]
[The characteristic will be the employees are constantly working in order to derive the
satisfaction of having achieved and accomplished. However, beyond working hours at
the workplace and at home, where they are likely to carry their unfinished assignments.
Thus, merely, observing and keeping track of the number of hours that they work would
provide an indication of the extent to which business or work production drives them.
Next, keeping track of how frequently people continue to preserve with their job despite
failures offers a good idea of how persevering people to be successful in their business
production. Another example if a Diploma In Accounting and Diploma In Business student
2
“Statistics Of Extremes Theory and Applications”, J Beirlant, Y Goegebeur, J Segers, JL Teugels (2006); Cited by 1692;
“Extreme Value Analysis Of Environmental Time Series An Application Trend Detection In General Level Ozone”, R L Smith,
Statistical Science Journal (1989), Cited 654.
4. 4 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
obtain D grading on three quizzes, it consider themselves are having difficulties to
preserve mind oriented achievement. Hence, if the student or business woman failing in
their assignment, they can be measured as by the number of setbacks people
experience on the job yet continue to work. Thus, below mentioned information we can
classify it as one of our independent or dependent variable measurement such as:-]
[a.] [The number of hours per week individuals spends on work-related activities]
[b.] [How persevering they are completing their daily tasks?]
[c.] [How frequently and what reasons they take time off from their jobs?]
[Moreover, in this chapter, the reader will be exposed with the understanding of the
concept of quality operation achievement motivation dimension. Next, the data can be
used as an example ways on how the researchers should be capable to recognize or
measure as data findings in the form of statistic classification. This can be referred as in
Figure 1 that may establish the facts or numerical with graphical presentation that may be
useful for your research data collection as a sampling for the whole population].
Figure 1 : Non-Communicable & Communicable Diseases WHO Human Health Statistics Analysis &
Estimation Report For 2004, 2015, 2030
[1.2.2] [Economics]
[Economics largely depends on statistics. The economist’s researcher is generally finding it
hard to relax and devote their attention to other work-related activity. Hence, the
statistical methods such as graphical presentation are used to prepare these accounts
that may establish the relationship between supply and demand on the country’s imports
or exports either in the form of neither inflation rates nor per capita income. Therefore,
without a good knowledge of statistics may delay the decision making analysis process.]
5. 5 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.2.2.1] [Elements of Dimension Unwillingness to Relax]
[Why researchers or economics are normally unwillingly to relax? Normally, as an
economist they need to provide multipurpose indicators for economists and administrators
which lead them become very busy to prepare these accounts. can be asking individuals
such as how often they think of work while away from workplace, what are their hobbies
and how often they spend their time when not at the workplace or cultural activities or
college or library or religious or sports recreation? Thus, these dimensions are measurable.]
[1.2.3] [Mathematics]
[Statistics plays a central role in almost all natural and social sciences. The methods used
are the most reliable by describing the probability averages numbers, dispersions,
estimation that varies of different techniques of pure mathematics like integration,
differentiation and algebra are used in statistics.
[1.2.3.1 Mathematics Requires Research Survey to Obtain Research Variables]
[Statistics and mathematics formula can be formed if there is research survey can be
found. Hence, surveys are useful and powerful for finding answers to research questions
but it cannot be done if it is not correctly targeted with appropriate problem solving skills
presentation which is based on types of variables measurement data comprises of
qualitative and quantitative.]
[Qualitative data]
[A qualitative data is to apply the research methodology which may
include the variable that cannot assume a numerical value but can
be classified into two or more nonnumeric categories such as :-]
Coding System
Decoding Systems
Transcript Data
Categorizing Data Rating
Interview
6. 6 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[Quantitative data]
[A quantitative data is to apply the research methodology using
questionnaires and hypothesis tests effects measurement on two or more
variables sampling that can be measured numerically. This is normally using
observational studies, experiments and probability perception which
associated with the continuous data that having with the numerical values,
percentages values or physical measurements. The two levels between:-
Interval Level Data
Ratio Level Data
[1.2.3.2] [Random Sampling]
[A sample drawn in such a way that each element of the population has some chance of
being selected is the same for each element of the population.]
1. [Write each of the 50 names on a separate piece of paper]
2. [Place all 50 slips in a box and mix them thoroughly]
3. [Randomly draw five slips from the box]
4. [Find any article from newspaper, magazines or internet regarding inferential
and descriptive statistics]
[1.2.3.3] [Stratified Sampling]
[The stratified sampling is an excellent way to ensure that a sample looks like a population
but smaller although it requires the most resources and information segregation of
demographic profile such as age, gender, race, income, political affiliation or
geographical location.]
[1.2.3.4] [Convenience Sampling]
[A convenience sample makes use of data that are convenient to gather. For
example a student could stand at the center of campus, or in front of the library or dining
hall, and sample people as they pass by. This data sometimes can be biased or hidden
data measurement.]
7. 7 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.2.3.5] [Identification Knowledge on Type of Population (P) Or Sample (S) characteristics]
Q1
= Answer
Population
Q2 =
Answer
Population
Q3 =
Answer
Sample
Q4 =
Answer
Sample
Q5 =
Answer
Population
Q6 =
Answer
Sample
Q7 =
Answer
Population
Q8 =
Answer
Sample
Q9 =
Answer
Population
[1.2.3.2] [Mathematics Requires Research Survey to Obtain Research Variables
- Population]
[Statistics and mathematics formula can be established if the population of the entire
group of people, events or things articles of international paper critiques that may
capture the interest to be identified by the researchers or business statistics students as a
target sample application of the business statistics. For example, if the CEO of Cosmopoint
College wants to provide quality healthy lifestyle promotion and wants to know more
about what kinds of knowledge research business statistics strategies adopted by College
Institutions, in New Zealand. Then all College Institutions situated in New Zealand will be
the target population of the study.]
[1.2.3.3] [Mathematics Requires To Obtain Research Variables -Element]
[Statistics and mathematics formula can be illustrated if there is an element of the single
entire member of the population available in the research study. For example, each
single professional blue collar worker prefers to exercise and coming to work during the
lecture and flexible hours may increases knowledge research and organization
productivity. Both are an example which can be the factors of the knowledge research
and organization productivity.]
8. 8 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.2.3.3.1] [Research Variables –Element of Research Gaps3]
[An element of research gaps are explaining the intention of the CEO to improve the
problems faced by the whole organization and within the country that probably creating
the implications to the whole population in association with the statistics and mathematics
formula.]
[1.2.3.3.2] [Element of Research Survey Using Questionnaires Measurement Scaling]
[Surveys are useful and beneficial for problem solving. Hence, the research questions are
needed to be constructed to determine your Chapter Four (4) Topic of Research Findings.
However, the research survey must have the correct questions, methodologies and
recommendations for Chapter Five (5). With the Chapter One (1) (Introduction), Chapter
Two (2) (Literature Review), Chapter Three (3) Research Methodology will help the CEO to
analyze whether your recommendations as a problem solving to justify the relationship
between your understanding of the research variable effects between the targeted
population and element variable interest sampling4 effects selected by the researchers or
business statistics students.]
[1.2.3.3.3] [Subject Measurement Scaling and Sampling Frame]
[A subject is a single member of the sample that can be also as an element member of
the population which to be analyzed. Next, the sampling frame is a listing refers to the
elements in the population from which the sample is drawn. Moreover, there are three
measurement scale in order to obtain the questionnaires measurement answer from the
respondents providing number of mathematics in statistics as follows:-]
3
J.K White and R.R. Ruh (1973), “Effects Of Personal Values On The Relationship Between Participation And Job Attitudes”,
Administrative Science Quarterly 18 (4), 509.
4
Schreiber et al. (2000) “A Coherent Cluster Of Insights, Experiences, Theories and Heuristics”; Janesick (2000); Miles and
Huberman (1994:3); Avis (2003): 1004.
9. 9 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.3] [The Growth of Statistics & Information Technology Leads To The Purpose of Business
Research Statistics Modelling Strategies]
[1.3.1] [Elements of Dimensions on Learning Statistics Mathematics]
[In other words, we can reasonably ascertain that a students’ dimension measurement in
the class learning statistics mathematics may be as follows:]
Figure 2: Concept of Theory Learning, Case Study, Examination, Knowledge Sharing and Business
Research Process Integration Of Human Process Reengineering. Sources Obtained From : Bosua &
Scheepers “Assessment Of Knowledge Model” (BSM) (2007), Yin (1994), Dul & Hak (2007) “Case
Study For Validation Approach”
I) Understanding
Preliminary Abstract
Literature Review Survey
Objectives, Subject, Problem
Statement, Data Collection
ii) Retention
Retains easily
What is learnt from the
learning kit revision may
able to remember and
easy to apply for future
career
iii) Application
Applies whatever has
been taught better grasp
of what learning is all
about
TDC Theoretical of
Distributed Cognition
material after some lapse of
time
Interpretation of findings, integrate, report
preparation other relevant material
& Presentation 2 Weeks Before Exam
Provide questionnaires
Observe
Interview
Answer
Question correctly
By providing example
10. 10 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.3.2] [Banking]
[Operational in banking definition is necessary to measure the relevant variables in the
business facilities such as current account, savings account, housing loan, hire purchase
loan, credit cards, debit cards, insurance that offers to the customers whom are having
varies of demographic profile variable which indicates by their :-
Age
Education
Gender
Financial Capabilities
Health’s Condition
Type Of Recreation
[1.3.3] [Accounting and Auditing]
[Statistics plays an important role on providing precision or approximation basis decision.
The correction of the values of current assets is made on the basis of the purchasing
power of money or its current value.]
[1.3.4] [Administration Management]
[Statistical data is very vital especially in preparing and estimating for the pro-forma
administration costs which known as expected expenditures and revenues from different
sources for our federal and provincial government budgets.]
[1.3.5] [Natural, Food Nutrition Safety, Environment, Sports, Tourism and Social Sciences]
[Statistical methods are commonly used for case study research analysis and testing the
significance effects results from the literature reviews, experiments, in depth community’s
feedback interview, food nutrition safety research, public health administration, sports
health promotion, cultural communications and information technology. ]
1.2.8 [Astronomy]
11. 11 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
Figure 3: Statistics Meteorology Department Uses Rainfall
Resources, To Explain The Weather Forecast. The research
Centre surveying on parents with children analysis
estimation made through Computer Statistic and Graphical
Models That Produces Research Data Findings Journal
Application
[1.4] [Definition Of Statistical Studies]
[Statistics are defined as a numerical fact or descriptive
piece of data obtained from a study of a large quantity
of population sampling either reviewing from the accounts,
finance, marketing, production, economics audit data
review. Statistical studies can be classified as either
experimental or observational5.]
Examples:
Account receivables shown on a client’s balance sheet
fairly represents the actual amount of receivables account.
Financial analysts use statistical information to guide their
Investments recommendations.
Brand managers can review the scanner statistics and the
Promotional activity statistics to gain better understanding
Relationship between promotional activities and sales.
Quality reflects to the X bar chart and Y production to
[1.4.2][Weather Forecasts]
[Statistics method will explain
on the weather forecast
sometime during the whole
day prior to the computer
model on weather
accessibility.
[Statistical methods are
important to estimate the
movement of stars by using
least squares statistics
method.]
[Astronomy is one of the
oldest branches of statistical
study. It deals with the
measurement of distance,
sizes, masses and densities of
heavenly bodies.]
[1.4.3][Predicting Medical,
Nutritional Studies, Issues or
Diseases]
[Statistics can be very useful
to explain on how valid rate
of effectiveness before any
drug, healthy food vitamins
minerals supplements can be
prescribed. the predicting
disease may hampering
human’s health. The rainfall
and disease explains to
people on the situational
uncontrollable epidemics.]
5 Chapter 1 Data and Statistics page 11, “Essentials Of Statistics For Business and Economics, Revised” David
R Anderson, University of Cincinnati, Dennis J Sweeney, University Of Cincinnati, Thomas A. Williams,
Rochester Institute Of Technology, South Western CENGAGE Learning, HA29.A587(2012),CCKK Library.
12. 12 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
monitor the average output emphasize the important
application growth of statistics and information data as
well as its economics and technology data information
collection should help to determine the estimation to
correct a production process.
[1.5] [Types Of Statistics]
[Based on Essentials of Statistics for Business and Economics
authors printed in Canada Nelson Education (2012) and
Deborah J Rumsey, there are three types of
Statistics:-]
Numerical (inferential, discrete, quantitative continuous)
Categorical or quantitative data
Ordinal Scale or rank quantitative data recorded
Descriptive Statistics (newspapers, summarized
Qualitative report)
[Category Of Data]
[1) The categorical data or qualitative data comprised of :]
[Names]
[Categories]
[2)The colors of M & Ms in a bag]
[An example of categorical data]
[3) The numerical data or quantitative data known as:-]
[Numerical values]
[Within approximately]
[2% of the actual percentage]
[The numbers of patient diagnosed with Dengue
is an example of descriptive and inferential graph
numerical data.]
[For many years, businesses have used statistical packages
to help managers use a wide range of statistically
techniques by automating the data processing and
calculations such techniques requirement.]
13. 13 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.6] [Population Versus Sample]
[1.6.1] [Quality Health Policy]
[Quality health systems are complex entities which control
By internal and external stakeholders with many differences of perception and knowledge. Ther
significant having a relationship between two tail positive
and negative precision level services as follows:-]
[Clinicians]
[Health Care Providers]
[Purchasers]
[Regulators]
[Government]
[Broader Public]
Sample is the group of
individuals who actually
participate in the study. The
individuals who is
interviewed can be refers as
applying qualitative type of
statistics collection data
study. People who could
have participants but did
not actually participate are
not considered part of your
sample. Example you email
200 people only 100 people
replying your email and this
100 individuals who reply
can be your sample.
Population is the broader group of people to whom
you intend to generalize the results of your study.
Your sample will always be a subset of your
population. Your exact population will depend on
the scope of your study. For instance, say your
research question asks if there is an association
between emotional intelligence and job
satisfaction in nurses who work in Sabah Women
Children Hospital. In this case, your population
might be nurses.
14. 14 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
1.7] [Basic Terms]
[Statistics is the art and science of data:-]
Collection
Analyzing
Presenting
Interpreting
[Nearly every college student majoring in business and
economics which requiring the students to take a course
in statistics with the ability to describe the typical statistical
applications for business plan and better economics
decision making analysis. Moreover, there are four scales
of measurement used to obtain data on a particular
variable inclusive :-]
Nominal
Ordinal
Interval
Ratio
[For purposes of statistical analysis, data can be classified as:
Categorical or Quantitative - Categorical data use labels or names to identify an
attribute of each element. Categorical data use either the
nominal or ordinal scale of measurement. Quantitative data are
are numerical values that indicate how much or how many.
Qualitative data –
Qualitative data are
normally having
a descriptive statistics
which having the tabular
data analysis example
matrix table, graphical.
Whereas for the inferential
statistics, it is explaining the
process of statistical
uses data where it was
obtained from a sample to
make estimates or test
hypotheses about the
characteristics of
a population.
“N” = Indicates the number
of
subjects
3Ms = Mean , Median, Mode
Significant Difference
= The measure of whether
results of research were due
to chance or less likely the
observation occurred by Yes
or No analysis in tally with
the elements of the variable.
Correlation = The degree
which factors to be related
to variables as well as
findings of the research
study.
15. 15 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.8] [Elements]
[An element pointer is a mean of storing the element
Variables are a means of storing a value and using it
Later contain input a value, component, calculations,
the systems enables people to identify the input as a
variable with formulae and calculations with a different
characteristics:-
Date
Decimal
Moneary
Pointer
Descriptive
statistics are
techniques and
methods that
are used to
describe a set
of values, both
graphically and
numerically to
describe data,
numbers and
graphs.
Inferential statistics
are techniques and
methods that are
used to make a
generalization or
evaluate a claim,
about a population
based on sample
data
“N” = Indicates the number of
subjects
3Ms = Mean , Median, Mode
Significant Difference
= The measure of whether
results of research were due to
chance or less likely the
observation occurred by Yes or
No analysis in tally with the
elements of the variable.
Correlation = The degree which
factors to be related to
variables as well as findings of
the research study.
Example : The Elements or Members or Population
Study Area – [Students and Lecturer]
[The Figure 3.6 explains that research study may
include the elements or members or a population
which comprises of a collection of people, objects, or
measurements that we are interested in analyzing
through a market research investigation (survey)
process. A survey that includes every member of the
population (which examines every member of a
16. 16 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[1.9] [Types Of Variables]
[Variables are concepts that you operationalize, or measure in a sample of data.
patient health performance which accountable for
medical health benefits rewards party especially to whom
are categorized as disable and with income per capita
below RM2,000. However, everyone is having accountablity for retaining the
basic necessity in life to avoid health risk. Moreover, for many
years, businesses have to use statistical packages to help
managers use wide range of statistically techniques by
automating the data processing and calculations techniques.
These applications inclusive MINITAB, SAS and SPSS are normally
available in any research centre or corporate organization.
The theoretical category information data as follows:-]
Independent Variable
Dependent Variable
17. 17 | P a g e C h a p t e r 1 : T h e I n t r o d u c t i o n O f S t a t i s t i c s
[References]
1. Fundamentals of Business Statistics, 6th Edition, Dennis J. Sweeney, Thomas A.
William, David R. Anderson, Thomson South Western, 2013.
2. Statistics, 3rd Edition, Lau Too Kya, Phang Yook Ngor & Zainudin Awang, Oxford
Fajar, 2015
Supplementary References Materials:
1. Basic Statistics for Business & Economics, 8th Edition, Douglas A. Lind, William G.
Marchall, Samuel A. Western, Mc Graw Hill, 2012.
2. Introductory Statistics, Neil A. Weiss, 8th Edition, Pearson, 2011.
3. The Applied Business Research : Quantitative and Qualitative Methods,
Measurement of Variables pg 189, Sampling pg 253, U.K Sekaran (1986), “Dual-
Career Families : Contemporary Organizational and Counseling Issues San Franciso:
Jossey Bass, J.K. White and R. R Ruh (1973), “Effects Of Personal Values On The
Relationship Between Participation and Job Attitudes : Administrative Science,
Quarterly 18 (4), 509.