Statistics is the study of collecting, organizing, summarizing, and interpreting data. It helps understand uncertainty and make informed decisions based on data. Key concepts in statistics include measures of central tendency like mean, median and mode, measures of variability like range and standard deviation, and understanding data distributions and shapes. Statistical thinking focuses on understanding variation in systems and using descriptive and inferential statistics to transform data into information and knowledge.
This document provides an overview of key statistical concepts including:
- Population, sample, random sample, bias, sources of bias, data, types of data, statistic, parameter, mean, median, mode, variation, standard deviation, normal distribution, standard scores, and standard error.
It defines these terms and provides examples to illustrate concepts like how the mean, median and mode can differ in a distribution, how standard deviation measures variation, and how standard error relates to sampling from a population.
The document discusses different types of graphs that can be used to represent data distributions, including bar charts, histograms, frequency polygons, pie charts, and ogives. It also explains the concepts of symmetrical and asymmetrical distributions, with normal, negatively skewed, and positively skewed distributions as examples. Metrics like mean, median, mode, standard deviation, and coefficient of skewness are used to characterize the different distribution shapes.
Measures of variability describe how spread out numbers in a data set are. The standard deviation quantifies this spread and is calculated by taking the square root of the variance. The variance measures how far data values are from their average value. Calculating the standard deviation involves finding the mean, deviations from the mean, squaring the deviations, summing them, and taking the square root. Standardizing scores into z-scores allows comparisons across data sets by giving each score in relation to the mean and standard deviation of its data set.
This document provides 3 lessons from large corporations to help survive change. The first lesson is that to do nothing and sit idle, you must be safely out of reach from potential threats. The second lesson is that while bullshit can get you temporary success, it won't sustain you long term. The third lesson is that even if you benefit from a situation, others may not have your best interests in mind, so remain cautious.
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 document discusses the importance of talent management for companies' global competitiveness. It highlights that top CEOs like Bill Gates and Jack Welch spend over 50% of their time recruiting and developing talent. Talent management involves identifying, developing, and retaining the best people. The CEO's role is crucial, as they are responsible for connecting people to the business vision, identifying top talent, and creating a learning culture where employees can continuously upgrade their skills. Effective talent management processes, like those at IBM, Volkswagen, and Shell, help companies develop diverse leadership pipelines and tie compensation to developing talent.
Screenagers and the digital window rscon3 summer 2011Joquetta Johnson
The document discusses how young people today spend nearly 10 hours a day engaged with digital screens like TVs, computers, phones and video games. It notes that 93% of American teens use the internet and over half create profiles on social networking sites. While teens are called "digital natives", the data shows they are comfortable with technology but not always as technically savvy as believed. The document advocates for embracing digital tools like YouTube, mobile phones, and interactive websites to engage students in reading, learning, and creating in the digital age. It stresses the need for teachers to adapt instruction to today's digital students.
This document provides an overview of key statistical concepts including:
- Population, sample, random sample, bias, sources of bias, data, types of data, statistic, parameter, mean, median, mode, variation, standard deviation, normal distribution, standard scores, and standard error.
It defines these terms and provides examples to illustrate concepts like how the mean, median and mode can differ in a distribution, how standard deviation measures variation, and how standard error relates to sampling from a population.
The document discusses different types of graphs that can be used to represent data distributions, including bar charts, histograms, frequency polygons, pie charts, and ogives. It also explains the concepts of symmetrical and asymmetrical distributions, with normal, negatively skewed, and positively skewed distributions as examples. Metrics like mean, median, mode, standard deviation, and coefficient of skewness are used to characterize the different distribution shapes.
Measures of variability describe how spread out numbers in a data set are. The standard deviation quantifies this spread and is calculated by taking the square root of the variance. The variance measures how far data values are from their average value. Calculating the standard deviation involves finding the mean, deviations from the mean, squaring the deviations, summing them, and taking the square root. Standardizing scores into z-scores allows comparisons across data sets by giving each score in relation to the mean and standard deviation of its data set.
This document provides 3 lessons from large corporations to help survive change. The first lesson is that to do nothing and sit idle, you must be safely out of reach from potential threats. The second lesson is that while bullshit can get you temporary success, it won't sustain you long term. The third lesson is that even if you benefit from a situation, others may not have your best interests in mind, so remain cautious.
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 document discusses the importance of talent management for companies' global competitiveness. It highlights that top CEOs like Bill Gates and Jack Welch spend over 50% of their time recruiting and developing talent. Talent management involves identifying, developing, and retaining the best people. The CEO's role is crucial, as they are responsible for connecting people to the business vision, identifying top talent, and creating a learning culture where employees can continuously upgrade their skills. Effective talent management processes, like those at IBM, Volkswagen, and Shell, help companies develop diverse leadership pipelines and tie compensation to developing talent.
Screenagers and the digital window rscon3 summer 2011Joquetta Johnson
The document discusses how young people today spend nearly 10 hours a day engaged with digital screens like TVs, computers, phones and video games. It notes that 93% of American teens use the internet and over half create profiles on social networking sites. While teens are called "digital natives", the data shows they are comfortable with technology but not always as technically savvy as believed. The document advocates for embracing digital tools like YouTube, mobile phones, and interactive websites to engage students in reading, learning, and creating in the digital age. It stresses the need for teachers to adapt instruction to today's digital students.
Bpo Industry, Created On Tuesday, May 23, 2006 Arunesh Chand MankotiaConsultonmic
India has a large democratic system and independent judiciary with a growing economy focused in the services sector including IT and ITES. The ITES sector in India is large and growing rapidly, fueled by an abundant English-speaking, low-cost workforce as well as improving infrastructure and validation from global corporations outsourcing work to India. While India faces competition from countries like Ireland, it has emerged as the preferred offshore destination for ITES due to its large talent pool, low costs, and strategic location.
1) The document discusses Professor Michael Porter's research on clusters and competitiveness. It argues that a nation's prosperity is determined by productivity, not comparative advantage.
2) Productivity depends on both the value and efficiency of production. Nations compete by offering the most productive business environment. Wealth is created by choices, not endowments.
3) A nation's productivity and growth are determined by internal company operations/strategy and external quality of the business environment, including clusters of competing and collaborating firms in an industry.
The document provides guidelines for project tuning meetings, which are designed to help teachers improve their classroom projects. The guidelines include norms of being constructive yet respectful. Meetings follow a 6-step protocol: 1) Presenter gives an overview and dilemma question. 2) Clarifying questions are asked. 3) Probing questions are asked to get more details without advice. 4) Discussion of the dilemma question without the presenter. 5) Presenter responds. 6) Debrief on the process. The facilitator ensures each step is followed and the discussion remains focused on improving the project, not criticizing the presenter.
The document is an agenda for a lesson on women's history month that includes analyzing texts to create and support a claim about the role of women. It includes links to speeches by Sojourner Truth and Queen Latifah to examine their claims about women's rights. Students are prompted to reflect on the texts using prompts about adjectives, emotions, interesting things, and questions. The lesson aims to have students understand the authors' arguments and reflect on how the texts made them feel.
Taking effective notes, managing study time and environment, and using study methods like acronyms, flashcards, and study groups are key to studying more effectively. The document provides guidance on the three stages of note taking, establishing a dedicated study place, and specific study techniques including using acronymic sentences, pegwords, loci mapping, and the ASPIRE system to optimize learning. Forming an effective study group requires selecting motivated classmates, setting goals and agendas, and ensuring all members contribute while maintaining a positive environment.
1. El documento describe comandos básicos de configuración y administración de switches y routers Cisco CCNA, incluyendo comandos para configurar VLANs, puertos de acceso y troncales, direccionamiento IP, SSH, y más.
2. Se explican los pasos para configurar SSH en un switch o router, recuperar contraseñas y archivos de configuración, y realizar copias de seguridad de la configuración.
3. También se incluyen comandos para verificar el estado de interfaces, tablas de direccionamiento, y otros pará
The document provides an overview of descriptive statistics. It discusses how data can be qualitative (categorical) or quantitative and organized graphically or numerically. For qualitative data, common graphs are bar graphs and pie charts. These show the relative frequency of outcomes in different categories. For quantitative data, histograms are often used to show the frequency or relative frequency distribution of numeric values. The document gives examples of organizing both types of data into tables and converting them into graphical representations.
The document discusses various statistical measures used to describe data, including measures of central tendency (mean, median, mode) and measures of variability (range, variance, standard deviation, percentiles, quartiles). It provides examples of calculating each measure for sample data sets. It also discusses how data can be organized and displayed graphically using histograms, bar graphs, and other visualizations. The goal of descriptive statistics is to summarize key aspects of a data set, such as its central tendency and variability, which provides critical information for understanding the data.
This document provides an overview of different methods for displaying and describing data, including graphs, measures of center, and measures of spread. It discusses bar graphs, pie charts, stem-and-leaf plots, histograms, and calculating the mean, median, quartiles, interquartile range, and standard deviation. Examples are provided using data on football scores to demonstrate these concepts. Key terms like outliers, shape, and transformations of data are also introduced.
Descriptive Statistics Part II: Graphical Descriptiongetyourcheaton
The document provides information on descriptive statistics and graphical descriptions of data, including bar charts, pie charts, histograms, and cumulative frequency distributions. It discusses how to construct these various graphs using Excel and includes examples and questions to describe and interpret the graphs. Key information that can be obtained from these graphs includes the mode, range, percentages of observations within certain classes or below/above certain values, and comparing values across categories.
1. The document discusses various methods for summarizing categorical and quantitative data through tables and graphs, including frequency distributions, relative frequency distributions, bar charts, pie charts, dot plots, histograms, and ogives.
2. An example using data on customer ratings from a hotel illustrates frequency distributions and pie charts.
3. Another example using costs of auto parts demonstrates frequency distributions, histograms, and ogives.
This was a presentation I gave to my firm's internal CPE in December 2012. It related to correlation and simple regression models and how we can utilize these statistics in both income and market approaches.
This document provides examples and explanations of various graphical methods for describing data, including frequency distributions, bar charts, pie charts, stem-and-leaf diagrams, histograms, and cumulative relative frequency plots. It demonstrates how to construct these graphs using sample data on student weights, grades, ages, and other examples. The goal is to help readers understand different ways to visually represent data distributions and patterns.
A Pareto chart is a type of bar chart used to identify problems. It arranges data in descending order of frequency or impact, separating the major issues from minor ones. This allows users to focus on addressing the top 20% of causes that create 80% of the problems. To construct a Pareto chart, data is collected, categorized, and plotted as bars with associated frequencies and cumulative percentages. This visual format makes it easy to identify priority issues to improve.
This document provides information on different types of charts and graphs used in statistics. It defines bar graphs, pie charts, histograms, frequency polygons, ogives, pictograms and discusses their uses, advantages and disadvantages. Examples are given for each type of graph to demonstrate how they are constructed and how data is represented visually. Key information on choosing appropriate scales and plotting points for different graphs is also presented.
This document provides an overview of introductory statistics concepts including:
- Descriptive statistics such as frequency distributions, histograms, and measures of central tendency are used to summarize and present data.
- Inferential statistics such as estimation and hypothesis testing are used to draw conclusions about populations based on sample data.
- Data can be organized and presented through tables, graphs including bar charts, pie charts, and scatter plots.
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.
Applied Business Statistics ,ken black , ch 2AbdelmonsifFadl
This document provides an overview of methods for visualizing data, including:
- Constructing frequency distributions to summarize ungrouped data by organizing it into class intervals and frequencies.
- Calculating class midpoints, relative frequencies, and cumulative frequencies for frequency distributions.
- Common statistical graphs like histograms, frequency polygons, ogives, pie charts, stem-and-leaf plots, Pareto charts, and scatter plots that can be used to visualize grouped or ungrouped data.
Descriptive statistics provide a concise summary of data in a meaningful way through methods like measures of central tendency (mean, median, mode), measures of dispersion, frequency distributions, and graphs. It allows for simpler interpretation of large data sets but does not allow for generalization beyond the sample or testing of hypotheses. Descriptive statistics clarify patterns in the data but have limitations since conclusions cannot be drawn about populations beyond the sample. Common techniques include tabulation, graphical representation like histograms and calculation of mean, median and mode to describe and compare distributions.
The document discusses biostatistics and statistics. It defines biostatistics as the application of statistics to topics in biology, with a focus on health applications such as survival analysis and longitudinal data analysis. It also discusses the role of biostatisticians in guiding experimental design, analyzing data, and interpreting results. The document then defines statistics and describes some key concepts in statistics including data collection, presentation of data, and drawing inferences from data. It discusses various methods of presenting data numerically and graphically, including tables, graphs, charts and diagrams. It also covers measures of central tendency like mean, median and mode, as well as measures of dispersion such as range, variance and standard deviation.
This document provides a summary of a lecture on frequency graphs. It discusses histograms, frequency polygons, smoothed frequency curves, and cumulative frequency distributions. Histograms represent class frequencies as vertical rectangles, with the total area equal to the total frequency. Frequency polygons connect the midpoints of class intervals by straight lines. Smoothed frequency curves approximate the histogram as class intervals get smaller. Cumulative frequency distributions cumulate the frequencies from lowest to highest class to create an ogive curve for determining positional measures. The lecture aims to help trainees understand and interpret different types of frequency graphs.
Graphs, charts, and tables ppt @ bec domsBabasab Patil
This document discusses various methods for organizing and presenting quantitative data, including frequency distributions, histograms, stem-and-leaf diagrams, pie charts, bar charts, line charts, scatter plots, and strategies for grouping continuous data into classes. Key topics covered include constructing frequency distributions, interpreting relative frequencies, guidelines for determining class widths and intervals, and using graphs and charts to visualize categorical and multivariate data.
Bpo Industry, Created On Tuesday, May 23, 2006 Arunesh Chand MankotiaConsultonmic
India has a large democratic system and independent judiciary with a growing economy focused in the services sector including IT and ITES. The ITES sector in India is large and growing rapidly, fueled by an abundant English-speaking, low-cost workforce as well as improving infrastructure and validation from global corporations outsourcing work to India. While India faces competition from countries like Ireland, it has emerged as the preferred offshore destination for ITES due to its large talent pool, low costs, and strategic location.
1) The document discusses Professor Michael Porter's research on clusters and competitiveness. It argues that a nation's prosperity is determined by productivity, not comparative advantage.
2) Productivity depends on both the value and efficiency of production. Nations compete by offering the most productive business environment. Wealth is created by choices, not endowments.
3) A nation's productivity and growth are determined by internal company operations/strategy and external quality of the business environment, including clusters of competing and collaborating firms in an industry.
The document provides guidelines for project tuning meetings, which are designed to help teachers improve their classroom projects. The guidelines include norms of being constructive yet respectful. Meetings follow a 6-step protocol: 1) Presenter gives an overview and dilemma question. 2) Clarifying questions are asked. 3) Probing questions are asked to get more details without advice. 4) Discussion of the dilemma question without the presenter. 5) Presenter responds. 6) Debrief on the process. The facilitator ensures each step is followed and the discussion remains focused on improving the project, not criticizing the presenter.
The document is an agenda for a lesson on women's history month that includes analyzing texts to create and support a claim about the role of women. It includes links to speeches by Sojourner Truth and Queen Latifah to examine their claims about women's rights. Students are prompted to reflect on the texts using prompts about adjectives, emotions, interesting things, and questions. The lesson aims to have students understand the authors' arguments and reflect on how the texts made them feel.
Taking effective notes, managing study time and environment, and using study methods like acronyms, flashcards, and study groups are key to studying more effectively. The document provides guidance on the three stages of note taking, establishing a dedicated study place, and specific study techniques including using acronymic sentences, pegwords, loci mapping, and the ASPIRE system to optimize learning. Forming an effective study group requires selecting motivated classmates, setting goals and agendas, and ensuring all members contribute while maintaining a positive environment.
1. El documento describe comandos básicos de configuración y administración de switches y routers Cisco CCNA, incluyendo comandos para configurar VLANs, puertos de acceso y troncales, direccionamiento IP, SSH, y más.
2. Se explican los pasos para configurar SSH en un switch o router, recuperar contraseñas y archivos de configuración, y realizar copias de seguridad de la configuración.
3. También se incluyen comandos para verificar el estado de interfaces, tablas de direccionamiento, y otros pará
The document provides an overview of descriptive statistics. It discusses how data can be qualitative (categorical) or quantitative and organized graphically or numerically. For qualitative data, common graphs are bar graphs and pie charts. These show the relative frequency of outcomes in different categories. For quantitative data, histograms are often used to show the frequency or relative frequency distribution of numeric values. The document gives examples of organizing both types of data into tables and converting them into graphical representations.
The document discusses various statistical measures used to describe data, including measures of central tendency (mean, median, mode) and measures of variability (range, variance, standard deviation, percentiles, quartiles). It provides examples of calculating each measure for sample data sets. It also discusses how data can be organized and displayed graphically using histograms, bar graphs, and other visualizations. The goal of descriptive statistics is to summarize key aspects of a data set, such as its central tendency and variability, which provides critical information for understanding the data.
This document provides an overview of different methods for displaying and describing data, including graphs, measures of center, and measures of spread. It discusses bar graphs, pie charts, stem-and-leaf plots, histograms, and calculating the mean, median, quartiles, interquartile range, and standard deviation. Examples are provided using data on football scores to demonstrate these concepts. Key terms like outliers, shape, and transformations of data are also introduced.
Descriptive Statistics Part II: Graphical Descriptiongetyourcheaton
The document provides information on descriptive statistics and graphical descriptions of data, including bar charts, pie charts, histograms, and cumulative frequency distributions. It discusses how to construct these various graphs using Excel and includes examples and questions to describe and interpret the graphs. Key information that can be obtained from these graphs includes the mode, range, percentages of observations within certain classes or below/above certain values, and comparing values across categories.
1. The document discusses various methods for summarizing categorical and quantitative data through tables and graphs, including frequency distributions, relative frequency distributions, bar charts, pie charts, dot plots, histograms, and ogives.
2. An example using data on customer ratings from a hotel illustrates frequency distributions and pie charts.
3. Another example using costs of auto parts demonstrates frequency distributions, histograms, and ogives.
This was a presentation I gave to my firm's internal CPE in December 2012. It related to correlation and simple regression models and how we can utilize these statistics in both income and market approaches.
This document provides examples and explanations of various graphical methods for describing data, including frequency distributions, bar charts, pie charts, stem-and-leaf diagrams, histograms, and cumulative relative frequency plots. It demonstrates how to construct these graphs using sample data on student weights, grades, ages, and other examples. The goal is to help readers understand different ways to visually represent data distributions and patterns.
A Pareto chart is a type of bar chart used to identify problems. It arranges data in descending order of frequency or impact, separating the major issues from minor ones. This allows users to focus on addressing the top 20% of causes that create 80% of the problems. To construct a Pareto chart, data is collected, categorized, and plotted as bars with associated frequencies and cumulative percentages. This visual format makes it easy to identify priority issues to improve.
This document provides information on different types of charts and graphs used in statistics. It defines bar graphs, pie charts, histograms, frequency polygons, ogives, pictograms and discusses their uses, advantages and disadvantages. Examples are given for each type of graph to demonstrate how they are constructed and how data is represented visually. Key information on choosing appropriate scales and plotting points for different graphs is also presented.
This document provides an overview of introductory statistics concepts including:
- Descriptive statistics such as frequency distributions, histograms, and measures of central tendency are used to summarize and present data.
- Inferential statistics such as estimation and hypothesis testing are used to draw conclusions about populations based on sample data.
- Data can be organized and presented through tables, graphs including bar charts, pie charts, and scatter plots.
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.
Applied Business Statistics ,ken black , ch 2AbdelmonsifFadl
This document provides an overview of methods for visualizing data, including:
- Constructing frequency distributions to summarize ungrouped data by organizing it into class intervals and frequencies.
- Calculating class midpoints, relative frequencies, and cumulative frequencies for frequency distributions.
- Common statistical graphs like histograms, frequency polygons, ogives, pie charts, stem-and-leaf plots, Pareto charts, and scatter plots that can be used to visualize grouped or ungrouped data.
Descriptive statistics provide a concise summary of data in a meaningful way through methods like measures of central tendency (mean, median, mode), measures of dispersion, frequency distributions, and graphs. It allows for simpler interpretation of large data sets but does not allow for generalization beyond the sample or testing of hypotheses. Descriptive statistics clarify patterns in the data but have limitations since conclusions cannot be drawn about populations beyond the sample. Common techniques include tabulation, graphical representation like histograms and calculation of mean, median and mode to describe and compare distributions.
The document discusses biostatistics and statistics. It defines biostatistics as the application of statistics to topics in biology, with a focus on health applications such as survival analysis and longitudinal data analysis. It also discusses the role of biostatisticians in guiding experimental design, analyzing data, and interpreting results. The document then defines statistics and describes some key concepts in statistics including data collection, presentation of data, and drawing inferences from data. It discusses various methods of presenting data numerically and graphically, including tables, graphs, charts and diagrams. It also covers measures of central tendency like mean, median and mode, as well as measures of dispersion such as range, variance and standard deviation.
This document provides a summary of a lecture on frequency graphs. It discusses histograms, frequency polygons, smoothed frequency curves, and cumulative frequency distributions. Histograms represent class frequencies as vertical rectangles, with the total area equal to the total frequency. Frequency polygons connect the midpoints of class intervals by straight lines. Smoothed frequency curves approximate the histogram as class intervals get smaller. Cumulative frequency distributions cumulate the frequencies from lowest to highest class to create an ogive curve for determining positional measures. The lecture aims to help trainees understand and interpret different types of frequency graphs.
Graphs, charts, and tables ppt @ bec domsBabasab Patil
This document discusses various methods for organizing and presenting quantitative data, including frequency distributions, histograms, stem-and-leaf diagrams, pie charts, bar charts, line charts, scatter plots, and strategies for grouping continuous data into classes. Key topics covered include constructing frequency distributions, interpreting relative frequencies, guidelines for determining class widths and intervals, and using graphs and charts to visualize categorical and multivariate data.
This document provides an overview and examples of various statistical concepts and tools, including:
- Useful statistical measures such as mean, median, mode, range, variance, and standard deviation.
- The normal distribution and how to calculate proportions of values that fall within a certain range using normal distribution tables or Excel functions.
- Common values from the normal distribution such as what proportion of values fall within 1, 2, or 3 standard deviations of the mean.
- Six Sigma "sigma values" and how they correspond to defects per million opportunities.
- Visualization tools like histograms, Pareto charts, stem-and-leaf plots, scatter graphs, multi-vari charts, and box plots; including
This document provides an overview and agenda for a hands-on introduction to data science. It includes the following sections: Data Science Overview and Intro to R (90 minutes), Exploratory Data Analysis (60 minutes), and Logistic Regression Model (30 minutes). Key topics that will be covered include collecting and analyzing data to find insights to help decision making, predicting problems before they occur, using analytics to improve operations and innovations, and examples of predicting loan defaults. Machine learning concepts such as supervised and unsupervised learning and common machine learning models will also be introduced.
This document provides an overview and agenda for a hands-on introduction to data science. It includes the following sections: Data Science Overview and Intro to R (90 minutes), Exploratory Data Analysis (60 minutes), and Logistic Regression Model (30 minutes). The document then covers key concepts in data science including collecting and analyzing data to find insights to help decision making, using analytics to improve operations and innovations, and predicting problems before they occur. Machine learning and statistical techniques are also introduced such as supervised and unsupervised learning, parameters versus statistics, and calculating variance and standard deviation.
This document provides information on key performance indicators (KPIs) for recruitment. It discusses important metrics to track such as quality of hire, turnover and retention, hiring manager satisfaction, cost per hire, time to fill, time to hire, source of hire, and offer acceptance rate. Calculating and analyzing these metrics can help improve the efficiency and effectiveness of an organization's recruitment and hiring processes.
This document discusses average handling time (AHT) and time to fill for recruiting metrics. It defines AHT as the average duration of one transaction, including talk time, hold time, and related tasks. The formula for calculating AHT is provided. Time to fill is defined as the number of calendar days it takes to fill a position from when it is approved to when an offer is accepted. Ways to potentially reduce AHT and time to fill are suggested, such as creating standardized questions and call structures. Industry benchmarks for average time to fill are also cited.
Project report for fly ash brick single unitConsultonmic
This document provides a project report for establishing a fly ash brick production facility with an annual target of producing 1 crore bricks. It details the production capacity of different brick sizes that can be made. It also outlines the machinery required including a fly ash brick making machine, mixture pan, belt conveyor, moulds, and automatic systems. Production is estimated at 20,500 to 40,000 bricks per 8 hour shift depending on size. Estimates are provided for expenses including power consumption, shed size, labor costs, materials and their rates. The manufacturing process and staffing needs are outlined.
The document proposes establishing a business to produce fly ash bricks as an environmentally friendly alternative to traditional clay bricks. Key points:
1) Fly ash, a byproduct of coal combustion in thermal power plants, is currently an environmental pollutant. The business would utilize fly ash to manufacture bricks, eliminating it from the ecosystem.
2) The proposed location is near many coal power plants and industries, ensuring a low-cost supply of fly ash. Government regulations also require fly ash brick use within 100km of power plants.
3) An annual production target of 5.11 million bricks is estimated, requiring 9 acres of land, machinery, 16 employees, and a capital investment of ~Rs. 40 lak
The document discusses how the internet has revolutionized recruitment by making job seekers and opportunities more accessible online through tools like job boards, company websites, and e-recruitment solutions. These solutions allow recruiters to advertise openings, receive applications, screen candidates, and manage the entire recruitment process digitally in a cost-effective manner. More advanced online tools provide functionality like prescreening, skills assessments, and applicant tracking to help recruiters find and evaluate qualified candidates. As technology evolves, recruitment is integrating more with HR systems and using online interviews, videos and communities to enhance the hiring process.
Digital marketing & Advertising/Branding Start up Recruitment/Structure Plan Consultonmic
This document outlines a plan for starting a digital marketing and advertising company. It discusses setting up profit centers, hiring sales, account management, and delivery teams. Key responsibilities for positions like the profit center head and account manager are described. The document also outlines the services to be offered, including market research, branding, media planning, design, advertising, and public relations. It discusses recruitment and onboarding processes and lists potential competitor companies in Mumbai, Bangalore, and Pune.
This document discusses bench management and rotation in the IT services sector. It defines bench as resources who are not currently assigned to projects. Effective bench management aims to maintain an optimal bench rate to gain flexibility and develop employee skills between projects. Key challenges include resources lacking skills or having niche skills with low demand. Best practices involve analyzing capability against demand, aligning training, and developing tools to connect resources to opportunities. Rotation involves moving employees between jobs to broaden their skills and exposure, with guidelines and oversight needed for success. Case studies show benefits like improved deployment rates but also potential costs and risks that require mitigation.
The document discusses bench management and rotation in the IT services sector. It defines bench as resources rolled off from projects. Bench management aims to maintain an optimal bench rate to gain flexibility and develop skills between projects. Challenges include resources lacking skills or having niche skills with low demand. Best practices involve analyzing capability gaps, aligning training, and developing platforms to match bench skills to demand. Rotation exposes employees to different experiences to enhance skills and satisfaction. Guidelines, clear communication, and careful planning are needed to implement rotation effectively. The key is balancing growth, productivity, and retention through bench management and rotation strategies.
The document lists seven things not to do after a meal for optimal digestion. These include smoking, which is as bad as smoking ten cigarettes; eating fruits immediately, which can bloat the stomach; drinking tea, as it contains acids that can harden proteins; loosening one's belt, which can cause intestinal problems; bathing, as it decreases blood flow to the digestive system; walking immediately, as it interferes with nutrient absorption; and sleeping right after eating, as food won't digest properly and can cause gastrointestinal issues. The document encourages sharing the information with others to increase awareness.
Consultonomic Solutions for Strategic Growth - Educational Institutes (Mahar...Consultonmic
Consultonomic Solutions provides strategic consulting services to educational institutes in Maharashtra, India. Their key services include strategic affiliations and partnerships with domestic and international universities, assistance with admissions including marketing, branding, and onboarding operations, and additional value-added initiatives to help institutes build their brand and compete effectively. They have a detailed 11-step plan to help institutes grow which focuses on web marketing, channel sales partnerships, university relationships, events, print/radio/online advertising, direct marketing, career fairs, unique course offerings, subject matter experts, and web-based value additions. Their goal is to help institutes strategically increase their student intake and market position through comprehensive strategic consulting solutions.
Strategic business proposal eent - green brick project - arunesh chand mank...Consultonmic
Strategic Project to Set up One of the largest Fly Ash Bricks Industrial Area in the world. All the the technical & financials are on actual FY- 2014-15 .
All rights reserved to Arunesh Chand Mankotia
Best Ad Banners Ever - Arunesh Chand MankotiaConsultonmic
This advertisement promotes banners but provides no information about the types of banners, why they are effective, what company produces them, or how to purchase them. It consists of only a headline declaring them the best banners ever and a name at the end with no context.
Canteen user survey format - Developed by Arunesh Chand MankotiaConsultonmic
The school is conducting a survey to get feedback from parents on the school canteen to help inform decisions about its future. The survey asks about frequency of canteen use, types of foods purchased, ratings of variety, quality, cost and healthy options, experiences volunteering, and suggestions for improvements. Responses will be kept confidential.
The document proposes a cashless prepaid food card system for IIT Roorkee's canteens and mess. The system would allow students to purchase food and snacks using the card, helping them budget their spending. It would also enable centralized management of payments for catering teams. The institute could then analyze raw material costs and food consumption in detail. The key features highlighted include portable payment devices, exhaustive reporting on expenses and item consumption, centralized information across all locations, single system management of multiple facilities and vendors, and ability to track and account for item-wise food subsidies. Students would be issued unique barcoded cards to make purchases, which would be scanned at checkout. Their transaction history and balances could then be viewed on card
Indoor advertising concept NAMO - Arunesh Chand MankotiaConsultonmic
This document provides an overview of advertising. It discusses how advertising is a form of marketing communication used to promote products, services, or ideas. Advertisers pay to deliver sponsored messages through various media channels including newspapers, magazines, television, radio, websites, and more. The document then gives a brief history of advertising in media like radio and television and how advertising has evolved with new technologies and targeting capabilities. It also categorizes and classifies different types of advertising.
The document is an advertisement for NAMO, a notable advertising media organization that specializes in indoor advertising. Some key points:
- NAMO utilizes internet-driven indoor displays like billboards, display boards, and TVs to deliver cost-effective advertising that targets specific audiences.
- Indoor advertising is effective as it receives undivided attention in a relaxed environment for 1-2 minutes, with high ad recall and memorization.
- NAMO offers various advertising packages on indoor displays located in businesses across different locations in India, with pricing options for monthly, 6-month, or annual plans.
- Features include displaying static or video ads, proof of performance tracking, and options for coupons
This document provides a project report for building an offline and online food and beverage business called Consultonomic Enterprize. It outlines the organization and management team, business concept including menus and designs, market analysis, marketing strategy, operations plan, investment analysis, growth plan, and financial projections. The business will operate Bros & Bikers Cafe in Roorkee catering to students and travelers, and a cafeteria inside the IIT Roorkee hostel with common procurement and production between the units. It details the staffing structure, standard operating procedures, and goals for high quality customer service and cleanliness.
3. Dealing with Uncertainty
The price of L&T stock will be higher
in six months than it is now.
versus
The price of L&T stock is likely
to be higher in six months than it
is now.
4. Dealing with Uncertainty
If the union budget deficit is as high as
predicted, interest rates will remain high
for the rest of the year.
versus
If the union budget deficit is as high
as predicted, it is probable that
interest rates will remain high for
the rest of the year.
5. Statistical Thinking
Statistical thinking is a philosophy of learning
and action based on the following fundamental
principles:
All work occurs in a system of interconnected
processes;
Variation exists in all processes, and
Understanding and reducing variation are the
keys to success.
6. Statistical Thinking
Systems and Processes
A system is a number of components that
are logically and sometimes physically
linked together for some purpose.
7. Statistical Thinking
Systems and Processes
A process is a set of activities operating on a system
that transforms inputs to outputs. A business process is
groups of logically related tasks and activities, that
when performed utilizes the resources of the business
to provide definitive results required to achieve the
business objectives.
8. Making Decisions
Data, Information, Knowledge
q Data: specific observations of measured numbers.
q Information: processed and summarized data
yielding facts and ideas.
q Knowledge: selected and organized information
that provides understanding, recommendations, and
the basis for decisions.
9. Making Decisions
Descriptive and Inferential Statistics
Descriptive Statistics include graphical and
numerical procedures that summarize and
process data and are used to transform data
into information.
10. Making Decisions
Descriptive and Inferential Statistics
Inferential Statistics provide the bases for
predictions, forecasts, and estimates that are
used to transform information to knowledge.
11. The Journey to Making Decisions
Decision
Knowledge
Experience, Theory,
Literature, Inferential
Statistics, Computers
Information
Descriptive Statistics,
Probability, Computers
Begin Here:
Data
Identify the
Problem
15. Classification of Variables
Discrete Numerical Variable
A variable that produces a response that
comes from a counting process.
16. Classification of Variables
Continuous Numerical Variable
A variable that produces a response that is
the outcome of a measurement process.
17. Classification of Variables
Categorical Variables
Variables that produce responses that
belong to groups (sometimes called
“classes”) or categories.
18. Measurement Levels
Nominal and Ordinal Levels of Measurement
refer to data obtained from categorical
questions.
• A nominal scale indicates assignments to
groups or classes.
• Ordinal data indicate rank ordering of items.
19. Frequency Distributions
A frequency distribution is a table used to organize data.
The left column (called classes or groups) includes
numerical intervals on a variable being studied. The
right column is a list of the frequencies, or number of
observations, for each class. Intervals are normally of
equal size, must cover the range of the sample
observations, and be non-overlapping.
20. Construction of a Frequency
Distribution
Rule 1: Intervals (classes) must be inclusive and non-
overlapping;
Rule 2: Determine k, the number of classes;
Rule 3: Intervals should be the same width, w; the width
is determined by the following:
(Largest Number - Smallest Number)
w = Interval Width =
Number of Intervals
Both k and w should be rounded upward, possibly to the next largest integer.
21. Construction of a Frequency
Distribution
Quick Guide to Number of Classes for a Frequency Distribution
Sample Size Number of Classes
Fewer than 50 5 – 6 classes
50 to 100 6 – 8 classes
over 100 8 – 10 classes
22. Example of a Frequency Distribution
A Frequency Distribution for the Suntan Lotion Example
Weights (in mL) Number of Bottles
220 less than 225 1
225 less than 230 4
230 less than 235 29
235 less than 240 34
240 less than 245 26
245 less than 250 6
23. Cumulative Frequency
Distributions
A cumulative frequency distribution contains the
number of observations whose values are less than the
upper limit of each interval. It is constructed by
adding the frequencies of all frequency distribution
intervals up to and including the present interval.
24. Relative Cumulative Frequency
Distributions
A relative cumulative frequency distribution
converts all cumulative frequencies to
cumulative percentages
25. Example of a Frequency Distribution
A Cumulative Frequency Distribution for the Sun tan Lotion
Example
Weights (in mL) Number of Bottles
less than 225 1
less than 230 5
less than 235 34
less than 240 68
less than 245 94
less than 250 100
26. Histograms and Ogives
A histogram is a bar graph that consists of vertical bars
constructed on a horizontal line that is marked off with
intervals for the variable being displayed. The
intervals correspond to those in a frequency
distribution table. The height of each bar is
proportional to the number of observations in that
interval.
27. Histograms and Ogives
An ogive, sometimes called a cumulative line graph, is
a line that connects points that are the cumulative
percentage of observations below the upper limit of
each class in a cumulative frequency distribution.
28. Histogram and Ogive for Example 1
Histogram of Weights
40 100
35 90
80
30
70
Frequency
25 60
20 50
15 40
30
10
20
5 10
0 0
224.5 229.5 234.5 239.5 244.5 249.5
Interval Weights (mL)
29. Stem-and-Leaf Display
A stem-and-leaf display is an exploratory data analysis
graph that is an alternative to the histogram. Data are
grouped according to their leading digits (called the stem)
while listing the final digits (called leaves) separately for
each member of a class. The leaves are displayed
individually in ascending order after each of the stems.
31. Tables
- Bar and Pie Charts -
Frequency and Relative Frequency Distribution for
Top Company Employers Example
Number of
Industry Employees Percent
Tourism 85,287 0.35
Retail 49,424 0.2
Health Care 39,588 0.16
Restaurants 16,050 0.06
Communications 11,750 0.05
Technology 11,144 0.05
Space 11,418 0.05
Other 21,336 0.08
32. Tables
- Bar and Pie Charts -
Bar Chart for Top Company Employers Example
1999 Top Company Employers in Central Florida
0.35
0.2
0.16
0.06 0.08
0.05 0.05 0.05
e
gy
e
il
ism
er
s
ns
ta
ar
nt
ac
th
lo
t io
Re
C
ra
ur
Sp
O
no
ica
au
th
To
ch
al
st
un
Te
He
Re
m
m
Co
Industry Category
33. Tables
- Bar and Pie Charts -
Pie Chart for Top Company Employers Example
1999 Top Company Employers in Central Florida
Others
29% Tourism
35%
Health Care
16% Retail
20%
34. Pareto Diagrams
A Pareto diagram is a bar chart that displays the
frequency of defect causes. The bar at the left indicates
the most frequent cause and bars to the right indicate
causes in decreasing frequency. A Pareto diagram is use
to separate the “vital few” from the “trivial many.”
few many.
35. Line Charts
A line chart, also called a time plot, is a series of data plotted
at various time intervals. Measuring time along the horizontal
axis and the numerical quantity of interest along the vertical
axis yields a point on the graph for each observation. Joining
points adjacent in time by straight lines produces a time plot.
36. Line Charts
Growth Trends in Internet Use by Age
1997 to 1999
35
Millions of Adults
31.3 32.7
30
25 26.3
20 20.2 18.5
15 16.5 15.8 17.2
13.8 13 14.2
10 9.8 11.4
7.5
5 5
0 Age 18 to 29
Age 30 to 49
98
99
9
O 7
O 8
7
8
9
7
8
l-9
l-9
l-9
r- 9
r- 9
r- 9
-9
-9
n-
n-
ct
ct
Ju
Ju
Ju
Age 50+
Ap
Ap
Ap
Ja
Ja
April 1997 to July 1999
37. Parameters and Statistics
A statistic is a descriptive measure computed from a
sample of data. A parameter is a descriptive
measure computed from an entire population of
data.
38. Measures of Central Tendency
- Arithmetic Mean -
A arithmetic mean is of a set of data is the
sum of the data values divided by the
number of observations.
39. Sample Mean
If the data set is from a sample, then the sample
n
mean, X , is:
∑x i
x1 + x2 + + xn
X= i =1
=
n n
40. Population Mean
If the data set is from a population, then the
population mean, µ , is:
N
∑x
x1 + x2 + + xn
i
µ= =i =1
N N
41. Measures of Central Tendency
- Median -
An ordered array is an arrangement of data in either
ascending or descending order. Once the data are
arranged in ascending order, the median is the value such
that 50% of the observations are smaller and 50% of the
observations are larger.
If the sample size n is an odd number, the median,
Xm, is the middle observation. If the sample size n
is an even number, the median, Xm, is the average
median
of the two middle observations. The median will
be located in the 0.50(n+1)th ordered position.
position
42. Measures of Central Tendency
- Mode -
The mode, if one exists, is the most
frequently occurring observation in the
sample or population.
43. Shape of the Distribution
The shape of the distribution is said to be
symmetric if the observations are balanced,
or evenly distributed, about the mean. In a
symmetric distribution the mean and median
are equal.
44. Shape of the Distribution
A distribution is skewed if the observations are not
symmetrically distributed above and below the mean.
A positively skewed (or skewed to the right)
distribution has a tail that extends to the right in the
direction of positive values. A negatively skewed (or
skewed to the left) distribution has a tail that extends
to the left in the direction of negative values.
45. Shapes of the Distribution
Symmetric Distribution
10
9
8
7
Frequency
6
5
4
3
2
1
0
1 2 3 4 5 6 7 8 9
Positively Skewed Distribution Negatively Skewed Distribution
12 12
10 10
8 8
Frequency
Frequency
6 6
4 4
2 2
0 0
1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9
46. Measures of Central Tendency
- Geometric Mean -
The Geometric Mean is the nth root of the product of n
numbers:
X g = n ( x1 • x2 • • xn ) = ( x1 • x2 • • xn )1/ n
The Geometric Mean is used to obtain mean growth over
several periods given compounded growth from each
period.
47. Measures of Variability
- The Range -
The range is in a set of data is the
difference between the largest and
smallest observations
48. Measures of Variability
- Sample Variance -
The sample variance, s2, is the sum of the squared
differences between each observation and the sample
mean divided by the sample size minus 1.
n
∑ (x − X )
i
2
s2 = i =1
n −1
49. Measures of Variability
- Short-cut Formulas for Sample
Variance -
Short-cut formulas for the sample variance are:
n (∑ xi ) 2
∑ xi − n ∑ xi2 − nX 2
s 2 = i =1 or s2 =
n −1 n −1
50. Measures of Variability
- Population Variance -
The population variance, σ2, is the sum of the squared
differences between each observation and the population
mean divided by the population size, N.
N
∑ (x − µ)
i
2
σ2 = i =1
N
51. Measures of Variability
- Sample Standard Deviation -
The sample standard deviation, s, is the positive square
root of the variance, and is defined as:
n
∑ (x − X )
i
2
s= s = 2 i =1
n −1
52. Measures of Variability
- Population Standard Deviation-
The population standard deviation, σ, is
N
∑ (x − µ)
i
2
σ= σ = 2 i =1
N
53. The Empirical Rule
(the 68%, 95%, or almost all rule)
For a set of data with a mound-shaped histogram, the Empirical
Rule is:
• approximately 68% of the observations are contained with a
distance of one standard deviation around the mean; µ± 1σ
• approximately 95% of the observations are contained with a
distance of two standard deviations around the mean; µ± 2σ
• almost all of the observations are contained with a distance
of three standard deviation around the mean; µ± 3σ
54. Coefficient of Variation
The Coefficient of Variation, CV, is a measure of relative
dispersion that expresses the standard deviation as a
percentage of the mean (provided the mean is positive).
The sample coefficient of variation is
s
CV = × 100 if X > 0
X
The population coefficient of variation is
σ
CV = ×100 if µ > 0
µ
55. Percentiles and Quartiles
Data must first be in ascending order. Percentiles
separate large ordered data sets into 100ths. The Pth
percentile is a number such that P percent of all the
observations are at or below that number.
Quartiles are descriptive measures that separate large
ordered data sets into four quarters.
56. Percentiles and Quartiles
The first quartile, Q1, is another name for the 25th
percentile. The first quartile divides the ordered data
percentile
such that 25% of the observations are at or below this
value. Q1 is located in the .25(n+1)st position when
the data is in ascending order. That is,
(n + 1)
Q1 = ordered position
4
57. Percentiles and Quartiles
The third quartile, Q3, is another name for the 75th
percentile. The first quartile divides the ordered
percentile
data such that 75% of the observations are at or
below this value. Q3 is located in the .75(n+1)st
position when the data is in ascending order. That
is,
3(n + 1)
Q3 = ordered position
4
58. Interquartile Range
The Interquartile Range (IQR) measures the spread
in the middle 50% of the data; that is the difference
between the observations at the 25th and the 75th
percentiles:
IQR = Q3 − Q1
59. Five-Number Summary
The Five-Number Summary refers to the five
descriptive measures: minimum, first quartile,
median, third quartile, and the maximum.
X min imum < Q1 < Median < Q3 < X max imum
60. Box-and-Whisker Plots
A Box-and-Whisker Plot is a graphical procedure that
uses the Five-Number summary.
A Box-and-Whisker Plot consists of
• an inner box that shows the numbers which span the
range from Q1 Box-and-Whisker Plot to Q3.
•a line drawn through the box at the median.
The “whiskers” are lines drawn from Q1 to the minimum
vale, and from Q3 to the maximum value.
62. Grouped Data Mean
For a population of N observations the mean is
K
∑fm i i
µ= i =1
N
For a sample of n observations, the mean is
K
∑fm i i
X= i =1
n
Where the data set contains observation values m1, m2, . . ., mk occurring with
frequencies f1, f2, . . . fK respectively
63. Grouped Data Variance
For a population of N observations the variance is
K K
∑f i (mi −µ) 2
∑ f i m i2
σ2 = i=1
= i=1
−µ2
N N
For a sample of n observations, the variance is
K K
∑ f i (mi − X ) 2 ∑ f i m i2 − nX 2
s2 = i =1
= i =1
n −1 n −1
Where the data set contains observation values m1, m2, . . ., mk occurring with
frequencies f1, f2, . . . fK respectively