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 document summarizes the key topics and concepts covered in Chapter 2 of the 9th edition of the business statistics textbook "Presenting Data in Tables and Charts". The chapter discusses guidelines for analyzing data and organizing both numerical and categorical data. It then covers various methods for tabulating and graphing univariate and bivariate data, including tables, histograms, frequency distributions, scatter plots, bar charts, pie charts, and contingency tables.
Basic Business Statistics Chapter 3 Numerical 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
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
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 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.
The moving average formula of demand forecasting is explained herein with the help of an example in an easily understandable way. The ppt contains the meaning and formula of moving average along with an example.
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 provides an overview of business research methods. It discusses what business research is, common business research methods like questionnaires, interviews and analyzing public data. It also outlines the business research process from defining the problem to analyzing and reporting results. Specific sampling techniques are explained like simple random sampling and stratified sampling. Multivariate analysis methods and correlation are described. Different statistical tests are introduced, like t-tests, ANOVA tests and regression analysis.
This document summarizes the key topics and concepts covered in Chapter 2 of the 9th edition of the business statistics textbook "Presenting Data in Tables and Charts". The chapter discusses guidelines for analyzing data and organizing both numerical and categorical data. It then covers various methods for tabulating and graphing univariate and bivariate data, including tables, histograms, frequency distributions, scatter plots, bar charts, pie charts, and contingency tables.
Basic Business Statistics Chapter 3 Numerical 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
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
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 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.
The moving average formula of demand forecasting is explained herein with the help of an example in an easily understandable way. The ppt contains the meaning and formula of moving average along with an example.
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 provides an overview of business research methods. It discusses what business research is, common business research methods like questionnaires, interviews and analyzing public data. It also outlines the business research process from defining the problem to analyzing and reporting results. Specific sampling techniques are explained like simple random sampling and stratified sampling. Multivariate analysis methods and correlation are described. Different statistical tests are introduced, like t-tests, ANOVA tests and regression analysis.
This document summarizes key concepts from Chapter 1 of an introductory statistics textbook. It defines statistics, distinguishes between populations and samples, parameters and statistics, and descriptive and inferential statistics. It also classifies data types and levels of measurement, and discusses experimental design concepts like data collection methods and sampling techniques.
This document discusses different types of research designs used in business research. It describes exploratory research design as the most flexible and loosely structured design used to explore and gain clarity on a problem. Qualitative research focuses on in-depth understanding through techniques like interviews without numerical measurements. Descriptive research provides comprehensive explanations of phenomena through structured techniques like surveys of large samples. Conclusive research tests propositions using quantitative methods like cross-sectional and longitudinal studies.
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.
Research Method for Business chapter 11-12-14Mazhar Poohlah
This document provides guidance on determining appropriate sample sizes based on population size. It states that for populations under 100, the entire population should be surveyed. For populations around 500, a sample size of 50% is recommended, while for populations around 1,500, a sample size of 20% is recommended. Beyond a population of 5,000, a sample size of 400 may be adequate regardless of total population size. The document also provides a table comparing strengths and weaknesses of different sampling techniques, including probability and non-probability methods.
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.
Mba i qt unit-4_probability and probability distributionsRai University
This document discusses probability and probability distributions. It defines key probability concepts like events, probabilities of events, complements of events, unions and intersections of events, conditional probabilities, independence, random variables, and different types of probability distributions. It provides examples of discrete random variables that follow binomial and Poisson distributions and continuous random variables that can be described using probability density functions and families of distributions like the normal distribution.
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.
Elements Of Research Design | Purpose Of Study | Important Of Research Design |FaHaD .H. NooR
This document discusses key elements of research design including the purpose of a study, type of investigation, study setting, population, time horizon, and importance of considering research design early. It describes exploratory, descriptive and hypothesis testing purposes. Correlational and causal studies are covered as well as field, lab and contrived settings. Individuals, groups, organizations can be units of analysis. Cross-sectional and longitudinal time horizons are presented. Reliability including stability over time and internal consistency are also summarized.
This document provides an overview of the key topics in Chapter 6 on the normal distribution, including:
1) It introduces continuous probability distributions and defines the normal distribution as the most important continuous probability distribution.
2) It explains how the normal distribution can be standardized to have a mean of 0 and standard deviation of 1, known as the standardized normal distribution.
3) It outlines the types of problems that will be solved using the normal distribution, including finding probabilities and percentiles for both the normal and standardized normal distribution.
Quantitative management is not a modern business idea but a management theory that came into existence after World War II. Business owners initially used it in Japan to pick up the pieces of the devastation caused by the war and started taking baby steps toward reconstruction. It focuses on the following elements of business operations:
Customer satisfaction
Business value enhancement
Empowerment of employees
Creating synergy among teams
Creating quality products
Preventing defects
Being responsible for quality
Focusing on continuous improvement
Leveraging statistical measurement
Remaining focused on the processes
Commitment to refinement and learning
Quantitative techniques in management as a collection of mathematical and statistical tools. They’re known by different names, such as management science or operation research. In modern business methods, statistical techniques are also viewed as a part of quantitative management techniques.
When appropriately used, quantitative approaches to management can become a powerful means of analysis, leading to effective decision-making. These techniques help resolve complex business problems by leveraging systematic and scientific methods.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
Class lecture notes # 2 (statistics for research)Harve Abella
The document discusses different types of variables and scales of measurement used in research. It defines qualitative and quantitative variables, and describes discrete and continuous quantitative variables. It also outlines four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples. The document emphasizes that statistics play a vital role in research design, validity/reliability testing, data organization and interpretation, and determining significance of findings.
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.
Quartiles divide a sorted data set into quarters based on the values. The first quartile (Q1) is the median between the smallest number and the overall median. The second quartile (Q2) is the median. The third quartile (Q3) is the median between the overall median and highest value. In an example data set of 11 numbers, the quartiles were Q1=5, Q2=7, and Q3=9.
Data analysis for effective decision makingsyed ahmed
This document provides an overview of data analysis and decision making. It defines data analysis as reviewing large amounts of raw data to determine conclusions. There are two main types of data analysis: qualitative analysis, which examines categorical data through methods like interviews, and quantitative analysis, which analyzes numerical data using descriptive statistics. Decision making is choosing alternatives based on preferences. Tools for decision making include SPSS, Stata, and Mstat C. SPSS allows entry and analysis of data through functions like frequencies, measures of central tendency, and correlation. The document provides resources for learning SPSS.
This document provides an overview of topics related to research and statistics, including research problems, variables, hypotheses, data collection, presentation, and analysis using SPSS. It discusses key concepts such as descriptive versus inferential statistics, point and interval estimates, and confidence intervals for means and proportions. The document serves as an introduction to research methodology and statistical analysis concepts.
This document discusses descriptive statistics and how to summarize data. It covers measures of central tendency like mode, mean, and median. It also discusses measures of variation such as range and standard deviation. Examples are given to illustrate the different types of central tendencies and measures of variation used. Readers are instructed to analyze graphs in magazines and newspapers to identify what statistics are used to influence readers.
This document contains information about a student named Vinay Aradhya enrolled in an MBA program. It includes responses to four questions on statistics.
Q1 defines characteristics of statistics such as dealing with numerical data collection and analysis, and being affected by multiple causes. Q2 explains that statistical averages summarize data, facilitate comparison, and provide a basis for decision making. It lists requisites of a good average.
Q3 lists characteristics of the Chi-square test as being based on frequencies, non-parametric, and useful for testing independence between attributes. Q4 defines a cost of living index and discusses methods for its construction, including the aggregate expenditure and family budget methods with an example of each.
Categorical data analysis refers to methods for analyzing discrete or categorical response variables. Common distributions for categorical data include the Bernoulli, binomial, Poisson, and multinomial distributions. Chi-square tests can be used to test goodness of fit, independence, and homogeneity for categorical data. The chi-square test statistic compares observed and expected frequencies in one or more categories. A larger chi-square value provides more evidence to reject the null hypothesis of a good fit or independence between variables.
1. The document discusses different sampling methods including simple random sampling, systematic random sampling, stratified sampling, and cluster sampling.
2. It provides examples of how each sampling method works and how samples are selected from the overall population.
3. Exercises are provided to determine which sampling method should be used for different scenarios involving selecting samples from identified populations.
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 overview of a lesson on data sources, variables, and measurement scales. The lesson will cover primary and secondary data sources, qualitative and quantitative variables, and nominal, ordinal, interval, and ratio measurement scales. Students will learn to identify data sources, define different variable types, and recognize the appropriate scale of measurement for a given variable. The lesson aims to help students understand how data is collected and analyzed depending on its characteristics.
This document summarizes key concepts from Chapter 1 of an introductory statistics textbook. It defines statistics, distinguishes between populations and samples, parameters and statistics, and descriptive and inferential statistics. It also classifies data types and levels of measurement, and discusses experimental design concepts like data collection methods and sampling techniques.
This document discusses different types of research designs used in business research. It describes exploratory research design as the most flexible and loosely structured design used to explore and gain clarity on a problem. Qualitative research focuses on in-depth understanding through techniques like interviews without numerical measurements. Descriptive research provides comprehensive explanations of phenomena through structured techniques like surveys of large samples. Conclusive research tests propositions using quantitative methods like cross-sectional and longitudinal studies.
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.
Research Method for Business chapter 11-12-14Mazhar Poohlah
This document provides guidance on determining appropriate sample sizes based on population size. It states that for populations under 100, the entire population should be surveyed. For populations around 500, a sample size of 50% is recommended, while for populations around 1,500, a sample size of 20% is recommended. Beyond a population of 5,000, a sample size of 400 may be adequate regardless of total population size. The document also provides a table comparing strengths and weaknesses of different sampling techniques, including probability and non-probability methods.
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.
Mba i qt unit-4_probability and probability distributionsRai University
This document discusses probability and probability distributions. It defines key probability concepts like events, probabilities of events, complements of events, unions and intersections of events, conditional probabilities, independence, random variables, and different types of probability distributions. It provides examples of discrete random variables that follow binomial and Poisson distributions and continuous random variables that can be described using probability density functions and families of distributions like the normal distribution.
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.
Elements Of Research Design | Purpose Of Study | Important Of Research Design |FaHaD .H. NooR
This document discusses key elements of research design including the purpose of a study, type of investigation, study setting, population, time horizon, and importance of considering research design early. It describes exploratory, descriptive and hypothesis testing purposes. Correlational and causal studies are covered as well as field, lab and contrived settings. Individuals, groups, organizations can be units of analysis. Cross-sectional and longitudinal time horizons are presented. Reliability including stability over time and internal consistency are also summarized.
This document provides an overview of the key topics in Chapter 6 on the normal distribution, including:
1) It introduces continuous probability distributions and defines the normal distribution as the most important continuous probability distribution.
2) It explains how the normal distribution can be standardized to have a mean of 0 and standard deviation of 1, known as the standardized normal distribution.
3) It outlines the types of problems that will be solved using the normal distribution, including finding probabilities and percentiles for both the normal and standardized normal distribution.
Quantitative management is not a modern business idea but a management theory that came into existence after World War II. Business owners initially used it in Japan to pick up the pieces of the devastation caused by the war and started taking baby steps toward reconstruction. It focuses on the following elements of business operations:
Customer satisfaction
Business value enhancement
Empowerment of employees
Creating synergy among teams
Creating quality products
Preventing defects
Being responsible for quality
Focusing on continuous improvement
Leveraging statistical measurement
Remaining focused on the processes
Commitment to refinement and learning
Quantitative techniques in management as a collection of mathematical and statistical tools. They’re known by different names, such as management science or operation research. In modern business methods, statistical techniques are also viewed as a part of quantitative management techniques.
When appropriately used, quantitative approaches to management can become a powerful means of analysis, leading to effective decision-making. These techniques help resolve complex business problems by leveraging systematic and scientific methods.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
Class lecture notes # 2 (statistics for research)Harve Abella
The document discusses different types of variables and scales of measurement used in research. It defines qualitative and quantitative variables, and describes discrete and continuous quantitative variables. It also outlines four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples. The document emphasizes that statistics play a vital role in research design, validity/reliability testing, data organization and interpretation, and determining significance of findings.
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.
Quartiles divide a sorted data set into quarters based on the values. The first quartile (Q1) is the median between the smallest number and the overall median. The second quartile (Q2) is the median. The third quartile (Q3) is the median between the overall median and highest value. In an example data set of 11 numbers, the quartiles were Q1=5, Q2=7, and Q3=9.
Data analysis for effective decision makingsyed ahmed
This document provides an overview of data analysis and decision making. It defines data analysis as reviewing large amounts of raw data to determine conclusions. There are two main types of data analysis: qualitative analysis, which examines categorical data through methods like interviews, and quantitative analysis, which analyzes numerical data using descriptive statistics. Decision making is choosing alternatives based on preferences. Tools for decision making include SPSS, Stata, and Mstat C. SPSS allows entry and analysis of data through functions like frequencies, measures of central tendency, and correlation. The document provides resources for learning SPSS.
This document provides an overview of topics related to research and statistics, including research problems, variables, hypotheses, data collection, presentation, and analysis using SPSS. It discusses key concepts such as descriptive versus inferential statistics, point and interval estimates, and confidence intervals for means and proportions. The document serves as an introduction to research methodology and statistical analysis concepts.
This document discusses descriptive statistics and how to summarize data. It covers measures of central tendency like mode, mean, and median. It also discusses measures of variation such as range and standard deviation. Examples are given to illustrate the different types of central tendencies and measures of variation used. Readers are instructed to analyze graphs in magazines and newspapers to identify what statistics are used to influence readers.
This document contains information about a student named Vinay Aradhya enrolled in an MBA program. It includes responses to four questions on statistics.
Q1 defines characteristics of statistics such as dealing with numerical data collection and analysis, and being affected by multiple causes. Q2 explains that statistical averages summarize data, facilitate comparison, and provide a basis for decision making. It lists requisites of a good average.
Q3 lists characteristics of the Chi-square test as being based on frequencies, non-parametric, and useful for testing independence between attributes. Q4 defines a cost of living index and discusses methods for its construction, including the aggregate expenditure and family budget methods with an example of each.
Categorical data analysis refers to methods for analyzing discrete or categorical response variables. Common distributions for categorical data include the Bernoulli, binomial, Poisson, and multinomial distributions. Chi-square tests can be used to test goodness of fit, independence, and homogeneity for categorical data. The chi-square test statistic compares observed and expected frequencies in one or more categories. A larger chi-square value provides more evidence to reject the null hypothesis of a good fit or independence between variables.
1. The document discusses different sampling methods including simple random sampling, systematic random sampling, stratified sampling, and cluster sampling.
2. It provides examples of how each sampling method works and how samples are selected from the overall population.
3. Exercises are provided to determine which sampling method should be used for different scenarios involving selecting samples from identified populations.
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 overview of a lesson on data sources, variables, and measurement scales. The lesson will cover primary and secondary data sources, qualitative and quantitative variables, and nominal, ordinal, interval, and ratio measurement scales. Students will learn to identify data sources, define different variable types, and recognize the appropriate scale of measurement for a given variable. The lesson aims to help students understand how data is collected and analyzed depending on its characteristics.
This document provides an introduction to basic statistical concepts. It discusses the differences between descriptive and inferential statistics. Descriptive statistics summarize and describe a data set, while inferential statistics make generalizations from a sample to a population. The document focuses on measures of central tendency, which summarize the average or middle of a data set, and measures of dispersion, which describe how spread out the values are. It gives examples of common measures like the mean, range, and standard deviation. The purpose is to convince readers of the usefulness and importance of understanding basic statistical concepts.
Chapter 8 Confidence Interval Estimation
Estimation Process
Point Estimates
Interval Estimates
Confidence Interval Estimation for the Mean ( Known )
Confidence Interval Estimation for the Mean ( Unknown )
Confidence Interval Estimation for the Proportion
General management theory and practice-bttssanjayjha
This document provides an overview of a course on general management theory and practice. It includes:
1. An outline of the course contents which covers topics such as planning, organizing, staffing, directing, and controlling.
2. Descriptions of management definitions, principles, functions, levels, and skills. It discusses management as both an art and a science.
3. Details on management approaches like Fayol's principles of management and the roles and differences between managers and leaders.
4. Lists of managerial skills needed at different levels and the roles and functions of top, middle, and frontline managers.
Alan borrowed $5,000 from Bruce but went bankrupt before the repayment date. Under the influence of alcohol, Bruce agreed to accept less money from Alan. However, Bruce can still demand the full amount because he lacked capacity while intoxicated. Two cases establish that partial payment cannot discharge a full debt unless with additional consideration, like a gift. Bruce provided no new consideration by agreeing to accept less money while inebriated.
Unit I - Management Science and Theory and PracticeArgon David
This document outlines the topics that will be covered in Unit 1 of Principles of Management, including the evolution of management thought, social responsibility and ethics, global and comparative management, the functions of management, planning, objectives, strategies, policies, decision making, and global planning.
Sampling technique for 2 nd yr pbbsc nsgsindhujojo
This document provides information on sampling terminology and methods. It defines key terms like population, sample, sampling frame, and element. It also describes various probability and non-probability sampling techniques. Specifically, it covers simple random sampling in detail, including how to select a simple random sample using lottery method, random number tables, or a computer. It explains the difference between sampling with and without replacement. Finally, it briefly discusses stratified sampling and how it involves dividing a population into homogeneous subgroups or strata before sampling.
Ch 1 introduction to management and organizations mgt arab world editionNardin A
This document provides an overview of key concepts from Chapter 1 of the textbook "Management: Arab World Edition". It discusses what managers do, including the four main functions of management and Mintzberg's managerial roles. It also examines the characteristics of organizations and why studying management is important, highlighting the universality of management concepts. Additionally, it explores the potential influences on an "Arab model of management" and factors that have shaped Arab managerial thought and practice over time.
This document discusses human behavior as a scientific subject matter from the perspective of a business psychology course. It covers topics like the causes of individual differences, heredity and environment, mechanics of heredity, and sex determination. The key points are that individual differences arise from a combination of hereditary and environmental factors. Heredity involves the transmission of traits from parents to offspring through genes and chromosomes, while environment influences development after conception.
This document discusses research methodology and sampling techniques. It defines key terms like population, sample, census, and probability and non-probability sampling. It describes different sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and their advantages and disadvantages. Finally, it discusses issues around internet sampling and methods like using web site visitors, panels, and opt-in lists.
This document discusses different types of sampling methods used in qualitative research. It defines key terms like sample, random sampling, and non-probability sampling. It then explains different sampling techniques in more detail, including simple random sampling, systematic random sampling, stratified random sampling, multi-stage cluster sampling, convenience sampling, snowball sampling, quota sampling, accidental sampling, panel sampling, and improving response rates. The document emphasizes that qualitative researchers are more concerned with understanding phenomena in depth than statistical validity or generalizability.
Sampling Methods in Qualitative and Quantitative ResearchSam Ladner
This document discusses different types of sampling methods used in qualitative and quantitative research. It outlines the different assumptions researchers make regarding sampling in qualitative versus quantitative studies. A variety of sampling techniques are described for different research contexts such as ethnographic fieldwork, interviews, and content analysis.
The document discusses sample and sampling techniques used in research. It defines key terms like population, sample, sampling, and element. It describes two main sampling techniques - probability sampling which uses random selection, and non-probability sampling which uses non-random methods. Some examples of probability sampling techniques include simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. Examples of non-probability sampling include convenience sampling, quota sampling, and purposive sampling. Sample size is determined using formulas like Slovin's formula.
Sampling is the process of selecting a subset of individuals from within a population to estimate characteristics of the whole population. There are several sampling techniques including simple random sampling, stratified sampling, cluster sampling, systematic sampling, and non-probability sampling. Each technique has advantages and disadvantages related to accuracy, cost, and generalizability. Proper sampling helps reduce sampling errors and increase the reliability of making inferences about the population from a sample.
The document discusses entrepreneurship and the entrepreneurial process. It defines entrepreneurship as taking risks to create new value through time and effort. The key functions of entrepreneurs are innovation, risk taking, and organizing new businesses. The stages of the entrepreneurial process include opportunity analysis, planning, acquiring funding, implementing plans, and growing the business. A business plan is also essential, outlining the industry, product/services, marketing, finances, and risks of the new venture.
This document outlines the components of an introductory statistics course, including assignments, exams, topics, and learning objectives. It will cover descriptive and inferential statistics, data sources and types, limitations of statistics, and applications in economics and business. Key concepts include populations and samples, parameters and statistics, descriptive versus inferential statistics, sources of data, and types of variables. The goals are for students to properly present information, draw conclusions from samples, improve processes, and obtain reliable forecasts using statistics.
This document provides guidance on analyzing and reporting data from a research project. It discusses key steps in data analysis including organizing the raw data, conducting quantitative analysis such as comparing responses across demographic groups, and drawing conclusions. When reporting results, it emphasizes clearly stating the research objective and methodology, objectively reporting the data findings, and interpreting the implications of the results. Integrity in reporting is important, and unexpected findings should not be fabricated but rather used as an opportunity to improve future research.
data collection for elementary statistics by Taban RashidRashidTaban
The document provides an overview of key concepts in statistics. It discusses topics like population and sample, parameter and statistic, qualitative and quantitative data, descriptive and inferential statistics. It also covers data collection methods like questionnaires, interviews, and observation. The main objectives are to understand the meaning and branches of statistics, describe the role of statistics in business management, and learn basic statistical concepts.
Statistics is the science of collecting, organizing, analyzing, and interpreting data. There are different types of data and levels of measurement. Common data collection methods include surveys, experiments, and observations. Statistics involves descriptive methods to summarize data, probability to assess likelihoods, and inferential techniques like estimation and hypothesis testing to generalize from samples to populations. Studying statistics provides useful tools and techniques for decision-making in many business and management fields.
The document discusses research methodology and defines research. It provides examples of what constitutes research and what does not. Research is defined as a systematic, logical process that includes understanding the problem, reviewing literature, collecting and analyzing data, drawing conclusions, and generalizing findings. The document also discusses types of research questions, purposes of research, and common challenges in conducting research.
Comment this post (DL) W3-T1Pricewaterhouse Coopers is one of th.docxmccormicknadine86
Comment this post (DL) W3-T1
Pricewaterhouse Coopers is one of the Big 4 accounting firms and they are fully embraced in data analytics and innovative technology. “We deploy the transformative capabilities of data and analytics from strategy and design through to execution and technology enablement” (PricewaterhouseCoopers, 2019, para 2). They support their clients with both technology and data analysis.
PWC educates their customers in what their data analysis means for their business and how to best interpret the results. “We put leading-edge data and analytics to work solving our clients’ most critical issues, so they can focus on defining their future – faster” (PricewaterhouseCoopers, 2019, para 2). PWC is continuously moving forward with data analysis and any innovations that will further benefit their clients’ decision-making.
Much the same as PWC, Ernst & Young are also one of the Big 4 and offer data analytics services for their clients. They have taken their technology integration a step further with the use of artificial intelligence, “Infusion means that by embedding analytics and artificial intelligence (AI) into the very core of your business processes, we can help you drive capital allocation strategies and investment decisions, create an end-to-end digital audit, generate new revenue opportunities, manage risk, conduct investigations, measure financial and nonfinancial performance, capture tax big data to inform decisions, increase customer satisfaction, and improve the customer experience” (Ernst & Young, n.d., para 2). It is evident that E&Y is there for their clients in all ways that data analytics and innovative technology allow them to be. They too continue to seek new ways to benefit their clients and their use of data.
New technology appears to be the bridge necessary to close the gap between big data and it analytics. Acquiring the data is only part of the process, while “The ability to analyze meaningful and relevant data and convert data to information, knowledge, and ultimately action in time to favorably influence an organization is a key competitive differentiator” (Bumblauskas, Nold, Bumblauskas, & Igou, 2017, p. 703). The current use of technology in data analytics is still suffering some barriers with knowledgeable professionals, training and education. Moving forward, these barriers should steadily disappear as data analytics further integrates with the technology and it becomes a regular occurrence in the business structure.
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All organizations disregarding their size needs meaningful data and insights specially when the organization needs to understand their target market and customer needs, it even play a crucial role in anticipating their preferences. For organizations big data provide a picture that helps understand trends, patterns and preferences from a large database produce when people interact with them and each other (simplilearn.com).
Based on the vid ...
Top of FormA random telephone survey of adults (aged and older.docxturveycharlyn
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A random telephone survey of adults (aged and older) was conducted by research corporation on behalf of an online tax preparation and e-filing service. The survey results showed that of those surveyed planned to file their taxes electronically.
a. Develop a descriptive statistic that can be used to estimate the percentage of all taxpayers who file electronically.
(to nearest whole number)
b. The survey reported that the most frequently used method for preparing the tax return is to hire an accountant or professional tax preparer. If of the people surveyed had their tax return prepared this way, how many people used an accountant or professional tax preparer?
(to nearest whole number)
c. Other methods that the person filing the return often used include manual preparation, use of an online tax service, and use of a software tax program. Would the data for the method for preparing the tax return be considered categorical or quantitative?
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The Tennessean, an online newspaper located in Nashville, Tennessee, conducts a daily poll to obtain reader opinions on a variety of current issues. In a recent poll, readers responded to the following question: "If a constitutional amendment to ban a state income tax is placed on the ballot in Tennessee, would you want it to pass?" Possible responses were Yes, No, or Not Sure.
a. What was the sample size for this poll?
b. Are the data categorical or quantitative?
c. Would it make more sense to use averages or percentages as a summary of the data for this question?
d. Of the respondents in the United States, said Yes, they would want it to pass. How many individuals provided this response? (Round your answer to the integer.)
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The Bureau of Transportation Statistics Omnibus Household Survey is conducted annually and serves as an information source for the U.S. Department of Transportation. In one part of the survey the person being interviewed was asked to respond to the following statement: "Drivers of motor vehicles should be allowed to talk on a hand-held cell phone while driving." Possible responses were strongly agree, some what agree, some what disagree, and strongly disagree. Forty-five respondents said that they strongly agree with this statement, said that they some what agree, said they some what disagree, and said they strongly disagree with this statement.
a. Do the responses for this statement provide categorical or quantitative data?
b. Would it make more sense to use averages or percentages as a summary of the responses for this statement?
c. What percentage of respondents strongly agree with allowing drivers of motor vehicles to talk on a hand-held cell phone while driving?
(to the nearest whole number)
d. Do the results indicate general support for or against allowing drivers of motor vehicles to talk on a hand-held cell phone while driving?
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Figure 1.7 provides a bar chart showing the annual revenue for ...
The document discusses key performance indicator (KPI) dashboards and benchmarking for higher education institutions. It outlines the case for good communication of financial and operational data through dashboards to highlight potential problems. It describes effective dashboard principles like understanding context, perceiving and presenting data accurately and linking data to mission and strategy. Benchmarking is presented as a way to maintain viability by comparing performance to peers. Examples of common higher education KPIs and benchmarking groups are provided.
Qualitative research data is interpretive and descriptive in nature. The best way to organize and manage qualitative data is through coding or grouping the data to look for patterns in the findings. Good qualitative data management involves having a clear file naming system, a data tracking system, and securely storing data during and after the research process. Qualitative data collection methods aim to understand people's experiences through techniques like interviews, observations, and focus groups to gain an in-depth perspective.
1) The document discusses sampling in marketing research, which involves selecting a subset of individuals from a population to estimate characteristics of the whole population. It describes key concepts like sampling elements, population, sampling frame, and different sampling techniques.
2) It explains the advantages of sampling such as being economical, faster to execute, and able to provide comprehensive information. The sampling design process involves defining the target population, determining the sampling frame, selecting a technique, determining sample size, and executing the process.
3) Different sampling techniques are described such as probability and non-probability sampling. Types of errors in sampling like sampling error and non-sampling error are also outlined. Examples of sampling methods for different contexts like telephone surveys
This chapter introduces statistics and discusses why managers need statistics. It covers key topics like descriptive versus inferential statistics, data collection methods, survey design, sampling techniques, and types of errors. The chapter aims to provide an overview of statistical concepts and their application for business managers.
1. Intro to Business Statistics 2 8th EDITION 2022.pdfejieealquizar2
This chapter introduces statistics and discusses why managers need statistics. It covers key topics like descriptive versus inferential statistics, data collection methods, survey design, sampling techniques, and types of errors. The chapter aims to provide an overview of statistical concepts and their application for business managers.
The document discusses key concepts in statistics for managers, including defining populations and samples, descriptive versus inferential statistics, different sampling methods, types of data and measurement scales, and sources of error in surveys. It aims to explain why understanding statistics is important for managers to properly analyze and present information, make decisions based on data, and improve processes. The chapter goals are to introduce fundamental statistical concepts and methods in data collection.
This document provides an overview of decision support systems and marketing research. It discusses key concepts such as marketing intelligence, decision support systems, marketing databases, and the roles and steps of marketing research. The three main roles of marketing research are described as descriptive, diagnostic, and predictive. Various research methods like surveys, experiments, observation, and secondary data are also summarized.
The document provides an overview of research methodology. It defines research as a systematic process of inquiry aimed at discovering and generating new knowledge. The key aspects of research covered include formulating research problems and objectives, reviewing previous literature, developing testable hypotheses, designing the research study, collecting and analyzing data, and interpreting and reporting findings. Examples are given of each step of the research process. Criteria for good research such as clearly defined concepts and terms, consideration of limitations, and usefulness of findings are also discussed.
Tennessee Higher Education and the Use of Decision Support Systems in Strate...Jeff Hinds
Business Intelligence products and processes have helped public and private organization to identify opportunities and trends that are both internal and external. For years the concepts of Business Intelligence, data mining, “Big” data, have become an important part of strategic planning and decision making within many successful organizations. Higher education has been expressing the importance of research, statistical analysis, data modeling, and business decisions based on good information. In fact, higher education institutions have created entire academic programs around these topics. While this information is written and reported within professional periodicals and events, the question stands about how higher education is taking advantage of business intelligence as part of their strategic planning and decision making processes.
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This document discusses different methods for conducting needs assessments, including surveys, interviews, focus groups, and reviewing institutional data. It provides an overview of the types of data each method can collect and their strengths and limitations. The document also lists 12 steps for conducting a needs assessment from NOAA and provides examples of how needs assessment data from multiple sources can be triangulated to develop a more accurate understanding. Lastly, it provides several links to additional resources on needs assessments and program planning.
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...ssuserf63bd7
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Learning Objectives
To understand the escalating importance of logistics and supply-chain management as crucial tools for competitiveness.
To learn about materials management and physical distribution.
To learn why international logistics is more complex than domestic logistics.
To see how the transportation infrastructure in host countries often dictates the options open to the manager.
To learn why international inventory management is crucial for success.
Learning Objectives
Describe alternative organizational structures for international operations.
Highlight factors affecting decisions about the structure of international organizations.
Indicate roles for country organizations in the development of strategy and implementation of programs.
Outline the need for and challenges of controls in international operations.
Learning Objectives
Outline the process of strategic planning in the context of the global marketplace.
Examine both the external and internal factors that determine the conditions for development of strategy and resource allocation.
Illustrate how best to utilize the environmental conditions within the competitive challenges and resources of the firm to develop effective programs.
Suggest how to achieve a balance between local and regional/global priorities and concerns in the implementation of strategy.
Learning Objectives
To learn how firms gradually progress through an internationalization process.
To understand the strategic effects of internationalization.
To study the various modes of entering international markets.
To understand the role and functions of international intermediaries.
To learn about the opportunities and challenges of cooperative market development.
Learning Objectives
To gain an understanding of the need for research.
To explore the differences between domestic and international research.
To learn where to find and how to use sources of secondary information.
To gain insight into the gathering of primary data.
To examine the need for international management information systems.
Learning Objectives
To understand the special concerns that must be considered by the international manager dealing with emerging market economies.
To survey the vast opportunities for trade offered by emerging market economies.
To understand why economic change is difficult and requires much adjustment.
To become aware that privatization offers new opportunities for international trade and investment.
Learning Objectives
To review types of economic integration among countries
To examine the costs and benefits of integrative arrangements
To understand the structure of the European Union and its implications for firms within and outside Europe
To explore the emergence of other integration agreements, especially in the Americas and Asia
To suggest corporate response to advancing economic integration
Learning Objectives
To understand how currencies are traded and quoted on world financial markets
To examine the links between interest rates and exchange rates
To understand the similarities and differences between domestic sources of capital and international sources of capital
To examine how the needs of individual borrowers have changed the nature of the instruments traded on world financial markets in the past decade
To understand how the debt crises of the 1980s and 1990s are linked to the international financial markets and exchange rates
Learning Objectives
To understand the fundamental principles of how countries measure international business activity, the balance of payments
To examine the similarities of the current and capital accounts of the balance of payments
To understand the critical differences between trade in merchandise and services and why international investment activity has recently been controversial in the United States
To review the mechanical steps of how exchange rates are transmitted into altered trade prices and eventually trade volumes
To understand how countries with different government policies toward international trade and investments, or different levels of economic development, differ in their balance of payments
Learning Objectives
To learn how firms gradually progress through an internationalization process.
To understand the strategic effects of internationalization.
To study the various modes of entering international markets.
To understand the role and functions of international intermediaries.
To learn about the opportunities and challenges of cooperative market development.
Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests
Chapter Topic:
Hypothesis Testing Methodology
Z Test for the Mean ( Known)
p-Value Approach to Hypothesis Testing
Connection to Confidence Interval Estimation
One-Tail Tests
t Test for the Mean ( Unknown)
Z Test for the Proportion
Potential Hypothesis-Testing Pitfalls and Ethical Issues
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4.2 Identify Variables as Quantitative or Categorical
4.2 Review Concepts about Data
Data that name Categories are called Cat_______, which are sometimes called Qualitative Variables
Number data in which numbers are measured are called Qu________
Quantitative data always have
Who? *Essential Answering those question answers can provide the context about data evaluation.
What? * Essential
When 4-2 Consider the context of the data
Where
Why
How
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