This document provides an overview of descriptive statistics and different types of measurement data. It discusses nominal, ordinal, interval, and ratio data and how each type is measured. It also defines and provides examples of key descriptive statistics like mean, median, mode, variability, standard deviation, and different ways to visually represent data through graphs and charts. The goal is to familiarize readers with descriptive statistics concepts before more advanced statistical analysis is introduced.
Statistical Processes
Can descriptive statistical processes be used in determining relationships, differences, or effects in your research question and testable null hypothesis? Why or why not? Also, address the value of descriptive statistics for the forensic psychology research problem that you have identified for your course project. read an article for additional information on descriptive statistics and pictorial data presentations.
300 words APA rules for attributing sources.
Computing Descriptive Statistics
Computing Descriptive Statistics: “Ever Wonder What Secrets They Hold?” The Mean, Mode, Median, Variability, and Standard Deviation
Introduction
Before gaining an appreciation for the value of descriptive statistics in behavioral science environments, one must first become familiar with the type of measurement data these statistical processes use. Knowing the types of measurement data will aid the decision maker in making sure that the chosen statistical method will, indeed, produce the results needed and expected. Using the wrong type of measurement data with a selected statistic tool will result in erroneous results, errors, and ineffective decision making.
Measurement, or numerical, data is divided into four types: nominal, ordinal, interval, and ratio. The businessperson, because of administering questionnaires, taking polls, conducting surveys, administering tests, and counting events, products, and a host of other numerical data instrumentations, garners all the numerical values associated with these four types.
Nominal Data
Nominal data is the simplest of all four forms of numerical data. The mathematical values are assigned to that which is being assessed simply by arbitrarily assigning numerical values to a characteristic, event, occasion, or phenomenon. For example, a human resources (HR) manager wishes to determine the differences in leadership styles between managers who are at different geographical regions. To compute the differences, the HR manager might assign the following values: 1 = West, 2 = Midwest, 3 = North, and so on. The numerical values are not descriptive of anything other than the location and are not indicative of quantity.
Ordinal Data
In terms of ordinal data, the variables contained within the measurement instrument are ranked in order of importance. For example, a product-marketing specialist might be interested in how a consumer group would respond to a new product. To garner the information, the questionnaire administered to a group of consumers would include questions scaled as follows: 1 = Not Likely, 2 = Somewhat Likely, 3 = Likely, 4 = More Than Likely, and 5 = Most Likely. This creates a scale rank order from Not Likely to Most Likely with respect to acceptance of the new consumer product.
Interval Data
Oftentimes, in addition to being ordered, the differences (or intervals) between two adjacent measurement values on a measurement scale are identical. For example, the di ...
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
This document provides information about descriptive statistics and how to calculate various descriptive statistics measures. It defines four types of measurement data: nominal, ordinal, interval, and ratio data. It then explains how to calculate and interpret the mean, median, mode, variability measures including range, variance and standard deviation. Examples are provided to demonstrate calculating these descriptive statistics on sets of sample data. The document emphasizes that descriptive statistics alone cannot be used to draw conclusions, but rather just describe patterns in the data.
Executive Program Practical Connection Assignment - 100 poinBetseyCalderon89
This document discusses descriptive statistics and how to calculate and interpret various descriptive statistics, including mean, median, mode, range, variance, and standard deviation. It provides examples and formulas for computing each statistic using data on employee productivity. The key points are:
- Descriptive statistics are used to summarize and describe data through measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation).
- The appropriate statistic to use depends on the level of measurement of the data (nominal, ordinal, interval, ratio).
- Examples are provided to demonstrate how to calculate and interpret the mean, median, mode, range, variance, and standard deviation using data on the number of items employees produced.
In research, one of the important aspects is analyzing the data appropriately in order to derive the findings. Statistics is majorly applied in quantitative research to do the analysis of the research. Data collection, analysis, interpretation, presentation, and organization are all part of the mathematical field of statistics. Making sense of the data, making judgments, and coming to trustworthy findings are its main goals. Numerous disciplines, including science, economics, business, medicine, and social sciences, heavily rely on statistics. Statistical assignment help Canada emphasizes that statistics offers helpful methods and instruments for interpreting data, coming to trustworthy judgments, and bolstering evidence-based decision-making.
ANALYSIS ANDINTERPRETATION OF DATA Analysis and Interpr.docxcullenrjzsme
ANALYSIS AND
INTERPRETATION
OF DATA
Analysis and Interpretation of Data
https://my.visme.co/render/1454658672/www.erau.edu
Slide 1 Transcript
In a qualitative design, the information gathered and studied often is nominal or narrative in form. Finding trends, patterns, and relationships is discovered inductively and upon
reflection. Some describe this as an intuitive process. In Module 4, qualitative research designs were explained along with the process of how information gained shape the inquiry as it
progresses. For the most part, qualitative designs do not use numerical data, unless a mixed approach is adopted. So, in this module the focus is on how numerical data collected in either
a qualitative mixed design or a quantitative research design are evaluated. In quantitative studies, typically there is a hypothesis or particular research question. Measures used to assess
the value of the hypothesis involve numerical data, usually organized in sets and analyzed using various statistical approaches. Which statistical applications are appropriate for the data of
interest will be the focus for this module.
Data and Statistics
Match the data with an
appropriate statistic
Approaches based on data
characteristics
Collected for single or multiple
groups
Involve continuous or discrete
variables
Data are nominal, ordinal,
interval, or ratio
Normal or non-normal distribution
Statistics serve two
functions
Descriptive: Describe what
data look like
Inferential: Use samples
to estimate population
characteristics
Slide 3 Transcript
There are, of course, far too many statistical concepts to consider than time allows for us here. So, we will limit ourselves to just a few basic ones and a brief overview of the more
common applications in use. It is vitally important to select the proper statistical tool for analysis, otherwise, interpretation of the data is incomplete or inaccurate. Since different
statistics are suitable for different kinds of data, we can begin sorting out which approach to use by considering four characteristics:
1. Have data been collected for a single group or multiple groups
2. Do the data involve continuous or discrete variables
3. Are the data nominal, ordinal, interval, or ratio, and
4. Do the data represent a normal or non-normal distribution.
We will address each of these approaches in the slides that follow. Statistics can serve two main functions – one is to describe what the data look like, which is called descriptive statistics.
The other is known as inferential statistics which typically uses a small sample to estimate characteristics of the larger population. Let’s begin with descriptive statistics and the measures
of central tendency.
Descriptive Statistics and Central Measures
Descriptive statistics
organize and present data
Mode
The number occurring most
frequently; nominal data
Quickest or rough estimate
Most typical value
Measures of central
tendenc.
Research and Statistics Report- Estonio, Ryan.pptxRyanEstonio
Statistical tools and treatments can help researchers manage large datasets and better interpret results. Common statistical tools include measures of central tendency like the mean and measures of variability like standard deviation. Regression, hypothesis testing, and statistical software packages are also used. Determining the appropriate tools and treatments for research requires conducting a literature review, consulting experts, considering the study design, and pilot testing options.
Statistical analysis involves investigating trends, patterns, and relationships using quantitative data. It requires careful planning from the start, including specifying hypotheses and designing the study. After collecting sample data, descriptive statistics summarize and organize the data, while inferential statistics are used to test hypotheses and make estimates about populations. Key steps in statistical analysis include planning hypotheses and research design, collecting a sufficient sample, summarizing data with measures of central tendency and variability, and testing hypotheses or estimating parameters with techniques like regression, comparison tests, and confidence intervals. The results must be interpreted carefully in terms of statistical significance, effect sizes, and potential decision errors.
The document summarizes key concepts from Chapter 1 of the textbook "Elementary Statistics" including:
- The difference between a population and a sample, and how statistics uses samples to make inferences about populations.
- The different types of data: quantitative, categorical, discrete vs. continuous data.
- The different levels of measurement for data: nominal, ordinal, interval, and ratio.
- The importance of critical thinking when analyzing data and statistics, including considering context, sources, sampling methods, and avoiding misleading graphs, samples, conclusions, or survey questions.
Statistical Processes
Can descriptive statistical processes be used in determining relationships, differences, or effects in your research question and testable null hypothesis? Why or why not? Also, address the value of descriptive statistics for the forensic psychology research problem that you have identified for your course project. read an article for additional information on descriptive statistics and pictorial data presentations.
300 words APA rules for attributing sources.
Computing Descriptive Statistics
Computing Descriptive Statistics: “Ever Wonder What Secrets They Hold?” The Mean, Mode, Median, Variability, and Standard Deviation
Introduction
Before gaining an appreciation for the value of descriptive statistics in behavioral science environments, one must first become familiar with the type of measurement data these statistical processes use. Knowing the types of measurement data will aid the decision maker in making sure that the chosen statistical method will, indeed, produce the results needed and expected. Using the wrong type of measurement data with a selected statistic tool will result in erroneous results, errors, and ineffective decision making.
Measurement, or numerical, data is divided into four types: nominal, ordinal, interval, and ratio. The businessperson, because of administering questionnaires, taking polls, conducting surveys, administering tests, and counting events, products, and a host of other numerical data instrumentations, garners all the numerical values associated with these four types.
Nominal Data
Nominal data is the simplest of all four forms of numerical data. The mathematical values are assigned to that which is being assessed simply by arbitrarily assigning numerical values to a characteristic, event, occasion, or phenomenon. For example, a human resources (HR) manager wishes to determine the differences in leadership styles between managers who are at different geographical regions. To compute the differences, the HR manager might assign the following values: 1 = West, 2 = Midwest, 3 = North, and so on. The numerical values are not descriptive of anything other than the location and are not indicative of quantity.
Ordinal Data
In terms of ordinal data, the variables contained within the measurement instrument are ranked in order of importance. For example, a product-marketing specialist might be interested in how a consumer group would respond to a new product. To garner the information, the questionnaire administered to a group of consumers would include questions scaled as follows: 1 = Not Likely, 2 = Somewhat Likely, 3 = Likely, 4 = More Than Likely, and 5 = Most Likely. This creates a scale rank order from Not Likely to Most Likely with respect to acceptance of the new consumer product.
Interval Data
Oftentimes, in addition to being ordered, the differences (or intervals) between two adjacent measurement values on a measurement scale are identical. For example, the di ...
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
This document provides information about descriptive statistics and how to calculate various descriptive statistics measures. It defines four types of measurement data: nominal, ordinal, interval, and ratio data. It then explains how to calculate and interpret the mean, median, mode, variability measures including range, variance and standard deviation. Examples are provided to demonstrate calculating these descriptive statistics on sets of sample data. The document emphasizes that descriptive statistics alone cannot be used to draw conclusions, but rather just describe patterns in the data.
Executive Program Practical Connection Assignment - 100 poinBetseyCalderon89
This document discusses descriptive statistics and how to calculate and interpret various descriptive statistics, including mean, median, mode, range, variance, and standard deviation. It provides examples and formulas for computing each statistic using data on employee productivity. The key points are:
- Descriptive statistics are used to summarize and describe data through measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation).
- The appropriate statistic to use depends on the level of measurement of the data (nominal, ordinal, interval, ratio).
- Examples are provided to demonstrate how to calculate and interpret the mean, median, mode, range, variance, and standard deviation using data on the number of items employees produced.
In research, one of the important aspects is analyzing the data appropriately in order to derive the findings. Statistics is majorly applied in quantitative research to do the analysis of the research. Data collection, analysis, interpretation, presentation, and organization are all part of the mathematical field of statistics. Making sense of the data, making judgments, and coming to trustworthy findings are its main goals. Numerous disciplines, including science, economics, business, medicine, and social sciences, heavily rely on statistics. Statistical assignment help Canada emphasizes that statistics offers helpful methods and instruments for interpreting data, coming to trustworthy judgments, and bolstering evidence-based decision-making.
ANALYSIS ANDINTERPRETATION OF DATA Analysis and Interpr.docxcullenrjzsme
ANALYSIS AND
INTERPRETATION
OF DATA
Analysis and Interpretation of Data
https://my.visme.co/render/1454658672/www.erau.edu
Slide 1 Transcript
In a qualitative design, the information gathered and studied often is nominal or narrative in form. Finding trends, patterns, and relationships is discovered inductively and upon
reflection. Some describe this as an intuitive process. In Module 4, qualitative research designs were explained along with the process of how information gained shape the inquiry as it
progresses. For the most part, qualitative designs do not use numerical data, unless a mixed approach is adopted. So, in this module the focus is on how numerical data collected in either
a qualitative mixed design or a quantitative research design are evaluated. In quantitative studies, typically there is a hypothesis or particular research question. Measures used to assess
the value of the hypothesis involve numerical data, usually organized in sets and analyzed using various statistical approaches. Which statistical applications are appropriate for the data of
interest will be the focus for this module.
Data and Statistics
Match the data with an
appropriate statistic
Approaches based on data
characteristics
Collected for single or multiple
groups
Involve continuous or discrete
variables
Data are nominal, ordinal,
interval, or ratio
Normal or non-normal distribution
Statistics serve two
functions
Descriptive: Describe what
data look like
Inferential: Use samples
to estimate population
characteristics
Slide 3 Transcript
There are, of course, far too many statistical concepts to consider than time allows for us here. So, we will limit ourselves to just a few basic ones and a brief overview of the more
common applications in use. It is vitally important to select the proper statistical tool for analysis, otherwise, interpretation of the data is incomplete or inaccurate. Since different
statistics are suitable for different kinds of data, we can begin sorting out which approach to use by considering four characteristics:
1. Have data been collected for a single group or multiple groups
2. Do the data involve continuous or discrete variables
3. Are the data nominal, ordinal, interval, or ratio, and
4. Do the data represent a normal or non-normal distribution.
We will address each of these approaches in the slides that follow. Statistics can serve two main functions – one is to describe what the data look like, which is called descriptive statistics.
The other is known as inferential statistics which typically uses a small sample to estimate characteristics of the larger population. Let’s begin with descriptive statistics and the measures
of central tendency.
Descriptive Statistics and Central Measures
Descriptive statistics
organize and present data
Mode
The number occurring most
frequently; nominal data
Quickest or rough estimate
Most typical value
Measures of central
tendenc.
Research and Statistics Report- Estonio, Ryan.pptxRyanEstonio
Statistical tools and treatments can help researchers manage large datasets and better interpret results. Common statistical tools include measures of central tendency like the mean and measures of variability like standard deviation. Regression, hypothesis testing, and statistical software packages are also used. Determining the appropriate tools and treatments for research requires conducting a literature review, consulting experts, considering the study design, and pilot testing options.
Statistical analysis involves investigating trends, patterns, and relationships using quantitative data. It requires careful planning from the start, including specifying hypotheses and designing the study. After collecting sample data, descriptive statistics summarize and organize the data, while inferential statistics are used to test hypotheses and make estimates about populations. Key steps in statistical analysis include planning hypotheses and research design, collecting a sufficient sample, summarizing data with measures of central tendency and variability, and testing hypotheses or estimating parameters with techniques like regression, comparison tests, and confidence intervals. The results must be interpreted carefully in terms of statistical significance, effect sizes, and potential decision errors.
The document summarizes key concepts from Chapter 1 of the textbook "Elementary Statistics" including:
- The difference between a population and a sample, and how statistics uses samples to make inferences about populations.
- The different types of data: quantitative, categorical, discrete vs. continuous data.
- The different levels of measurement for data: nominal, ordinal, interval, and ratio.
- The importance of critical thinking when analyzing data and statistics, including considering context, sources, sampling methods, and avoiding misleading graphs, samples, conclusions, or survey questions.
This document provides an overview of key concepts from Chapter 1 of the textbook "Elementary Statistics". It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between different types of data and levels of measurement. Additionally, it discusses the importance of collecting sample data through appropriate random sampling methods. Critical thinking in statistics is emphasized, highlighting factors like the context, source, and sampling method of data when evaluating statistical claims. Different ways of collecting data through studies and experiments are also introduced.
Need a nonplagiarised paper and a form completed by 1006015 before.docxlea6nklmattu
Need a nonplagiarised paper and a form completed by 10/06/015 before 7:00pm. I have attached the documents along the rubics that must be followed.
Coyne and Messina Articles, Part 2 Statistical Assessment
Details:
1) Write a paper of 1,000-1,250 words regarding the statistical significance of outcomes as presented in Messina's, et al. article "The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals."
2) Assess the appropriateness of the statistics used by referring to the chart presented in the Module 4 lecture and the resource "Statistical Assessment."
3) Discuss the value of statistical significance vs. pragmatic usefulness.
4) Prepare this assignment according to the APA guidelines found in the APA Style Guide located in the Student Success Center. An abstract is not required.
5) This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
·
The research question itself
·
The sample size
·
The type of data you have collected
·
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generaliza.
This document provides an overview of quantitative data analysis methods for medical education research. It discusses summary measures, hypothesis testing, statistical methodologies, sample size determination, and additional resources for statistical support. Key points covered include choosing appropriate statistical tests based on study design, translating research questions into testable hypotheses, interpreting p-values and making conclusions, and factors that influence required sample size such as effect size and variability.
This document provides an overview and objectives for Chapter 3 of the textbook "Statistical Techniques in Business and Economics" by Lind. The chapter covers describing data through numerical measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). It includes examples of computing various measures like the weighted mean, median, mode, and interpreting their relationships. The document also lists learning activities for students such as reading the chapter, watching video lectures, completing practice problems in the book, and participating in an online discussion forum.
This document summarizes a study that used machine learning algorithms to predict happiness based on survey data from over 4,600 users. The researchers analyzed data from 100+ survey questions along with demographics to predict if users identified as happy or unhappy. They tested multiple algorithms including naive Bayes, decision trees, random forests, gradient boosting, and support vector machines. Gradient boosting achieved the highest AUC score of 0.68 on test data, outperforming other algorithms. The researchers concluded machine learning can effectively predict happiness from subjective survey responses, with ensemble methods like gradient boosting and random forests performing best.
This document provides an overview of key concepts in data management and statistics. It defines statistics as the study of collecting, organizing, and interpreting data to make inferences about populations. The main branches are descriptive statistics, which summarizes data, and inferential statistics, which generalizes from samples to populations. It also defines key terms like population, sample, parameter, statistic, variable, data, levels of measurement, and measures of central tendency and dispersion. Measures of central tendency like mean, median, and mode are used to describe the center of data, while measures of dispersion like range and standard deviation describe how spread out data are.
- Descriptive statistics describe the properties of sample and population data through metrics like mean, median, mode, variance, and standard deviation. Inferential statistics use those properties to test hypotheses and draw conclusions about large groups.
- Descriptive statistics focus on central tendency, variability, and distribution of data. Inferential statistics allow statisticians to draw conclusions about populations based on samples and determine the reliability of those conclusions.
- Statistics rely on variables, which are characteristics or attributes that can be measured and analyzed. Variables can be qualitative like gender or quantitative like mileage, and quantitative variables can be discrete like test scores or continuous like height.
Here are the steps to find the variance and standard deviation of the given sample data:
1) Find the mean (x-bar) of the data:
(5 + 17 + 12) / 3 = 34 / 3 = 11.33
2) Find the deviations from the mean:
5 - 11.33 = -6.33
17 - 11.33 = 5.67
12 - 11.33 = 0.67
3) Square the deviations:
(-6.33)^2 = 40.11
(5.67)^2 = 32.17
(0.67)^2 = 0.45
4) Sum the squared deviations:
40.11
Introduction to statistics & data analysisAsmaUmar4
This document provides an introduction to basic statistical concepts. It defines statistics as a tool for extracting information from data. Key concepts discussed include:
- Population and sample - A population is the whole group being studied, a sample is a subset of the population.
- Parameter and statistic - Parameters describe populations, statistics describe samples.
- Descriptive and inferential statistics - Descriptive statistics summarize and organize data, inferential statistics make inferences about populations from samples.
- Measures of central tendency (mean, median, mode) and how to determine which to use based on the data.
Statistics and types of statistics .docxHwre Idrees
This document discusses different types of statistics. It defines descriptive statistics as summarizing and describing data, while inferential statistics use samples to make inferences about populations. Measures of central tendency like mean, median and mode are described as well as measures of variability such as range, standard deviation and variance. Specific types of each are defined and explained, such as weighted mean, interquartile range, and harmonic mean. Tables and figures are included to illustrate the differences between descriptive and inferential statistics and examples of various statistical measures.
Please acknowledge my work and I hope you like it. This is not boring like other ppts you see, I have tried my best to make it extremely informative with lots of pictures and images, I am sure if you choose this as your presentation for statistics topic in your office or school, you are surely going to appreciated by all including your teachers, friends, your interviewer or your manager.
This document discusses using statistics to analyze bottlenecks in a manufacturing process. It begins by introducing the purpose of using statistics to understand relationships between variables like population and country size. Later sections discuss using hypothesis testing to analyze sample data and determine if it represents the overall population parameters. Descriptive statistics are calculated on the sample data and a regression analysis is run to determine the relationship between population and sample size.
This document outlines the syllabus for a statistics and probabilities course, which covers topics such as descriptive statistics like measures of central tendency and dispersion, probability distributions, hypothesis testing, regression, and experimental design. It provides definitions and examples of key statistical concepts like populations, samples, variables, measures of central tendency including mean, median and mode, and measures of dispersion like range, mean deviation, variance and standard deviation. The course aims to teach students how to make informed judgments and decisions using statistical methods.
This document provides an overview of basic concepts in inferential statistics. It defines descriptive statistics as describing and summarizing data through measures like mean, median, variance and standard deviation. Inferential statistics is defined as using sample data and statistics to draw conclusions about populations through hypothesis testing and estimates. Key concepts explained include parameters, statistics, sampling distributions, null and alternative hypotheses, and the hypothesis testing process. Examples of descriptive and inferential analyses are also provided.
This document provides an overview of data analysis and graphical representation. It discusses data analytics, statistics, quantitative and qualitative data, different types of graphical representations including line graphs, bar graphs and histograms. It also covers sampling design, types of sampling including probability and non-probability sampling, and measures of central tendency such as mean, median and mode.
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B409 W11 Sas Collaborative Stats Guide V4.2marshalkalra
This document provides an overview of numerical summaries and variation within data. It defines key terms like mean, median, mode, range, standard deviation, and variance. It also discusses sources of variation within data like process inputs and conditions versus random temporary events. The document demonstrates how to use SAS software to analyze a cars dataset and create reports and bar charts to describe the data and identify trends and variation.
Data science notes for ASDS calicut 2.pptxswapnaraghav
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
The document provides an overview of a business statistics course, including topics covered, applications in different business fields, and examples of descriptive statistics. The course covers topics such as data collection, descriptive statistics, statistical inference, and the use of computers for analysis. Descriptive statistics are used to summarize parts cost data from 50 car tune-ups, finding an average cost of $79. Inferential statistics are used to estimate population characteristics based on sample data.
(APA 6th Edition Formatting and Style Guide)
Office of Graduate Studies
Alcorn State University
Engaging Possibilities, Pursuing Excellence
REVISED May 23, 2018
THESIS MANUAL
Graduates
2
COPYRIGHT PRIVILEGES
BELONG TO
OFFICE OF GRADUATE STUDIES
ALCORN STATE UNIVERSITY, LORMAN, MS
Reproduction for distribution of this THESIS MANUAL requires the written permission of the
Provost and Executive Vice President for Academic Affairs or Graduate Studies Administrator.
FOREWORD
Alcorn State University Office of Graduate Studies requires that all students comply with the
specifications given in this document in the publication of a thesis or non-thesis research project.
Graduate students, under faculty guidance, are expected to produce scholarly work either in the
form of a thesis or a scholarly research project.
The thesis (master or specialist) should document the student's research study and maintain a
degree of intensity.
The purpose of this manual is to assist the graduate student and the graduate thesis advisory
committee in each department with the instructions contained herein. This is the official
approved manual by the Graduate Division.
Formatting questions not addressed in these guidelines should be directed to the Graduate School
staff in the Walter Washington Administration Building, Suite 519 or by phone at
601.877.6122 or via email: [email protected] or in person.
The Graduate Studies
Thesis Advisory Committee
(Revised Spring 2018)
mailto:[email protected]
TABLE OF CONTENTS
Page
INTRODUCTION ............................................................................................................................ 3
SELECTION AND APPOINTMENT OF THESIS ADVISORY COMMITTEE ......................... 4
1. Early Topic Selection ......................................................................................................... 4
2. Selection of Thesis Chair ......................................................................................................... 4
3. Selection of Thesis Committee Members .......................................................................... 4
4. Appointment of Thesis Advisory Committee Form .......................................................... 4
5. Invitation to Prospective Committee Members ................................................................. 5
6. TAC Committee Selection ................................................................................................. 5
CHOICE OF SUBJECT .................................................................................................................... 5
PROPOSAL DEFENSE AND SUBMISSION OF PROPOSAL TO IRB ..................................... 5
PARTS OF THE MANUSCRIPT: PRELIMINARY PAGES ..................................................... 8
1. Title Page .
(a) Thrasymachus’ (the sophist’s) definition of Justice or Right o.docxAASTHA76
(a) Thrasymachus’ (the sophist’s) definition of Justice or Right or Right Doing/Living is “The Interest of the Stronger (Might makes Right).” How does Socrates refute this definition? (cite just
one
of his arguments) [cf:
The Republic
, 30-40, Unit 1 Lecture Video]
(b) According to Socrates, what is the true definition of Justice or Right? [cf:
The Republic
, 141-42, Unit 2 Lecture Video]
(c) And why therefore is the Just life far preferable to the Unjust life (142-43)?
(a) The Allegory of the CAVE (the main metaphor of western philosophy) is an illustration of the Divided LINE.
Characterize
the Two Worlds, and the move/ascent from one to the other (exiting the CAVE, crossing the Divided LINE)—which is alone the true meaning of Education and the only way to become Just, Right, and Immortal. [cf:
The Republic
, 227-232, Unit 3 Lecture Video]
(b) How do the philosophical Studies of
Arithmetic
(number) and
Dialectic
take you above the Divided Line and out of the changing sense-world of illusion (the CAVE) into Reality and make you use your Reason (pure thought) instead of your senses? [cf:
The Republic
, 235-37, 240-42, 250-55. Unit 4 Lecture Video (transcript)]
Give a summary of the
Proof of the Force
(Why there is the “Universe,” “Man,” “God,” “History,” etc)? Start with, “Can there be
nothing
?” [cf: TJH 78-95, Unit 2 Lecture Video]
NIETZSCHE is the crucial Jedi philosopher who provides the “bridge” between negative and positive Postmodernity by focusing on a certain “Problem” and the “
Solution
” to it.
(a) Discuss
2
of the following items (
1
pertaining to the Problem,
1
pertaining to the
.
This document provides an overview of key concepts from Chapter 1 of the textbook "Elementary Statistics". It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between different types of data and levels of measurement. Additionally, it discusses the importance of collecting sample data through appropriate random sampling methods. Critical thinking in statistics is emphasized, highlighting factors like the context, source, and sampling method of data when evaluating statistical claims. Different ways of collecting data through studies and experiments are also introduced.
Need a nonplagiarised paper and a form completed by 1006015 before.docxlea6nklmattu
Need a nonplagiarised paper and a form completed by 10/06/015 before 7:00pm. I have attached the documents along the rubics that must be followed.
Coyne and Messina Articles, Part 2 Statistical Assessment
Details:
1) Write a paper of 1,000-1,250 words regarding the statistical significance of outcomes as presented in Messina's, et al. article "The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals."
2) Assess the appropriateness of the statistics used by referring to the chart presented in the Module 4 lecture and the resource "Statistical Assessment."
3) Discuss the value of statistical significance vs. pragmatic usefulness.
4) Prepare this assignment according to the APA guidelines found in the APA Style Guide located in the Student Success Center. An abstract is not required.
5) This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
·
The research question itself
·
The sample size
·
The type of data you have collected
·
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generaliza.
This document provides an overview of quantitative data analysis methods for medical education research. It discusses summary measures, hypothesis testing, statistical methodologies, sample size determination, and additional resources for statistical support. Key points covered include choosing appropriate statistical tests based on study design, translating research questions into testable hypotheses, interpreting p-values and making conclusions, and factors that influence required sample size such as effect size and variability.
This document provides an overview and objectives for Chapter 3 of the textbook "Statistical Techniques in Business and Economics" by Lind. The chapter covers describing data through numerical measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). It includes examples of computing various measures like the weighted mean, median, mode, and interpreting their relationships. The document also lists learning activities for students such as reading the chapter, watching video lectures, completing practice problems in the book, and participating in an online discussion forum.
This document summarizes a study that used machine learning algorithms to predict happiness based on survey data from over 4,600 users. The researchers analyzed data from 100+ survey questions along with demographics to predict if users identified as happy or unhappy. They tested multiple algorithms including naive Bayes, decision trees, random forests, gradient boosting, and support vector machines. Gradient boosting achieved the highest AUC score of 0.68 on test data, outperforming other algorithms. The researchers concluded machine learning can effectively predict happiness from subjective survey responses, with ensemble methods like gradient boosting and random forests performing best.
This document provides an overview of key concepts in data management and statistics. It defines statistics as the study of collecting, organizing, and interpreting data to make inferences about populations. The main branches are descriptive statistics, which summarizes data, and inferential statistics, which generalizes from samples to populations. It also defines key terms like population, sample, parameter, statistic, variable, data, levels of measurement, and measures of central tendency and dispersion. Measures of central tendency like mean, median, and mode are used to describe the center of data, while measures of dispersion like range and standard deviation describe how spread out data are.
- Descriptive statistics describe the properties of sample and population data through metrics like mean, median, mode, variance, and standard deviation. Inferential statistics use those properties to test hypotheses and draw conclusions about large groups.
- Descriptive statistics focus on central tendency, variability, and distribution of data. Inferential statistics allow statisticians to draw conclusions about populations based on samples and determine the reliability of those conclusions.
- Statistics rely on variables, which are characteristics or attributes that can be measured and analyzed. Variables can be qualitative like gender or quantitative like mileage, and quantitative variables can be discrete like test scores or continuous like height.
Here are the steps to find the variance and standard deviation of the given sample data:
1) Find the mean (x-bar) of the data:
(5 + 17 + 12) / 3 = 34 / 3 = 11.33
2) Find the deviations from the mean:
5 - 11.33 = -6.33
17 - 11.33 = 5.67
12 - 11.33 = 0.67
3) Square the deviations:
(-6.33)^2 = 40.11
(5.67)^2 = 32.17
(0.67)^2 = 0.45
4) Sum the squared deviations:
40.11
Introduction to statistics & data analysisAsmaUmar4
This document provides an introduction to basic statistical concepts. It defines statistics as a tool for extracting information from data. Key concepts discussed include:
- Population and sample - A population is the whole group being studied, a sample is a subset of the population.
- Parameter and statistic - Parameters describe populations, statistics describe samples.
- Descriptive and inferential statistics - Descriptive statistics summarize and organize data, inferential statistics make inferences about populations from samples.
- Measures of central tendency (mean, median, mode) and how to determine which to use based on the data.
Statistics and types of statistics .docxHwre Idrees
This document discusses different types of statistics. It defines descriptive statistics as summarizing and describing data, while inferential statistics use samples to make inferences about populations. Measures of central tendency like mean, median and mode are described as well as measures of variability such as range, standard deviation and variance. Specific types of each are defined and explained, such as weighted mean, interquartile range, and harmonic mean. Tables and figures are included to illustrate the differences between descriptive and inferential statistics and examples of various statistical measures.
Please acknowledge my work and I hope you like it. This is not boring like other ppts you see, I have tried my best to make it extremely informative with lots of pictures and images, I am sure if you choose this as your presentation for statistics topic in your office or school, you are surely going to appreciated by all including your teachers, friends, your interviewer or your manager.
This document discusses using statistics to analyze bottlenecks in a manufacturing process. It begins by introducing the purpose of using statistics to understand relationships between variables like population and country size. Later sections discuss using hypothesis testing to analyze sample data and determine if it represents the overall population parameters. Descriptive statistics are calculated on the sample data and a regression analysis is run to determine the relationship between population and sample size.
This document outlines the syllabus for a statistics and probabilities course, which covers topics such as descriptive statistics like measures of central tendency and dispersion, probability distributions, hypothesis testing, regression, and experimental design. It provides definitions and examples of key statistical concepts like populations, samples, variables, measures of central tendency including mean, median and mode, and measures of dispersion like range, mean deviation, variance and standard deviation. The course aims to teach students how to make informed judgments and decisions using statistical methods.
This document provides an overview of basic concepts in inferential statistics. It defines descriptive statistics as describing and summarizing data through measures like mean, median, variance and standard deviation. Inferential statistics is defined as using sample data and statistics to draw conclusions about populations through hypothesis testing and estimates. Key concepts explained include parameters, statistics, sampling distributions, null and alternative hypotheses, and the hypothesis testing process. Examples of descriptive and inferential analyses are also provided.
This document provides an overview of data analysis and graphical representation. It discusses data analytics, statistics, quantitative and qualitative data, different types of graphical representations including line graphs, bar graphs and histograms. It also covers sampling design, types of sampling including probability and non-probability sampling, and measures of central tendency such as mean, median and mode.
data science course with placement in hyderabadmaneesha2312
360DigiTMG delivers data science course with placement in hyderabad, where you can gain practical experience in key methods and tools through real-world projects. Study under skilled trainers and transform into a skilled Data Scientist. Enroll today!
B409 W11 Sas Collaborative Stats Guide V4.2marshalkalra
This document provides an overview of numerical summaries and variation within data. It defines key terms like mean, median, mode, range, standard deviation, and variance. It also discusses sources of variation within data like process inputs and conditions versus random temporary events. The document demonstrates how to use SAS software to analyze a cars dataset and create reports and bar charts to describe the data and identify trends and variation.
Data science notes for ASDS calicut 2.pptxswapnaraghav
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
The document provides an overview of a business statistics course, including topics covered, applications in different business fields, and examples of descriptive statistics. The course covers topics such as data collection, descriptive statistics, statistical inference, and the use of computers for analysis. Descriptive statistics are used to summarize parts cost data from 50 car tune-ups, finding an average cost of $79. Inferential statistics are used to estimate population characteristics based on sample data.
(APA 6th Edition Formatting and Style Guide)
Office of Graduate Studies
Alcorn State University
Engaging Possibilities, Pursuing Excellence
REVISED May 23, 2018
THESIS MANUAL
Graduates
2
COPYRIGHT PRIVILEGES
BELONG TO
OFFICE OF GRADUATE STUDIES
ALCORN STATE UNIVERSITY, LORMAN, MS
Reproduction for distribution of this THESIS MANUAL requires the written permission of the
Provost and Executive Vice President for Academic Affairs or Graduate Studies Administrator.
FOREWORD
Alcorn State University Office of Graduate Studies requires that all students comply with the
specifications given in this document in the publication of a thesis or non-thesis research project.
Graduate students, under faculty guidance, are expected to produce scholarly work either in the
form of a thesis or a scholarly research project.
The thesis (master or specialist) should document the student's research study and maintain a
degree of intensity.
The purpose of this manual is to assist the graduate student and the graduate thesis advisory
committee in each department with the instructions contained herein. This is the official
approved manual by the Graduate Division.
Formatting questions not addressed in these guidelines should be directed to the Graduate School
staff in the Walter Washington Administration Building, Suite 519 or by phone at
601.877.6122 or via email: [email protected] or in person.
The Graduate Studies
Thesis Advisory Committee
(Revised Spring 2018)
mailto:[email protected]
TABLE OF CONTENTS
Page
INTRODUCTION ............................................................................................................................ 3
SELECTION AND APPOINTMENT OF THESIS ADVISORY COMMITTEE ......................... 4
1. Early Topic Selection ......................................................................................................... 4
2. Selection of Thesis Chair ......................................................................................................... 4
3. Selection of Thesis Committee Members .......................................................................... 4
4. Appointment of Thesis Advisory Committee Form .......................................................... 4
5. Invitation to Prospective Committee Members ................................................................. 5
6. TAC Committee Selection ................................................................................................. 5
CHOICE OF SUBJECT .................................................................................................................... 5
PROPOSAL DEFENSE AND SUBMISSION OF PROPOSAL TO IRB ..................................... 5
PARTS OF THE MANUSCRIPT: PRELIMINARY PAGES ..................................................... 8
1. Title Page .
(a) Thrasymachus’ (the sophist’s) definition of Justice or Right o.docxAASTHA76
(a) Thrasymachus’ (the sophist’s) definition of Justice or Right or Right Doing/Living is “The Interest of the Stronger (Might makes Right).” How does Socrates refute this definition? (cite just
one
of his arguments) [cf:
The Republic
, 30-40, Unit 1 Lecture Video]
(b) According to Socrates, what is the true definition of Justice or Right? [cf:
The Republic
, 141-42, Unit 2 Lecture Video]
(c) And why therefore is the Just life far preferable to the Unjust life (142-43)?
(a) The Allegory of the CAVE (the main metaphor of western philosophy) is an illustration of the Divided LINE.
Characterize
the Two Worlds, and the move/ascent from one to the other (exiting the CAVE, crossing the Divided LINE)—which is alone the true meaning of Education and the only way to become Just, Right, and Immortal. [cf:
The Republic
, 227-232, Unit 3 Lecture Video]
(b) How do the philosophical Studies of
Arithmetic
(number) and
Dialectic
take you above the Divided Line and out of the changing sense-world of illusion (the CAVE) into Reality and make you use your Reason (pure thought) instead of your senses? [cf:
The Republic
, 235-37, 240-42, 250-55. Unit 4 Lecture Video (transcript)]
Give a summary of the
Proof of the Force
(Why there is the “Universe,” “Man,” “God,” “History,” etc)? Start with, “Can there be
nothing
?” [cf: TJH 78-95, Unit 2 Lecture Video]
NIETZSCHE is the crucial Jedi philosopher who provides the “bridge” between negative and positive Postmodernity by focusing on a certain “Problem” and the “
Solution
” to it.
(a) Discuss
2
of the following items (
1
pertaining to the Problem,
1
pertaining to the
.
(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docxAASTHA76
(Glossary of Telemedicine and eHealth)
· Teleconsultation: Consultation between a provider and specialist at distance using either store and forward telemedicine or real time videoconferencing.
· Telehealth and Telemedicine: Telemedicine is the use of medical information exchanged from one site to another via electronic communications to improve patients' health status. Closely associated with telemedicine is the term "telehealth," which is often used to encompass a broader definition of remote healthcare that does not always involve clinical services. Videoconferencing, transmission of still images, e-health including patient portals, remote monitoring of vital signs, continuing medical education and nursing call centers are all considered part of telemedicine and telehealth. Telemedicine is not a separate medical specialty. Products and services related to telemedicine are often part of a larger investment by health care institutions in either information technology or the delivery of clinical care. Even in the reimbursement fee structure, there is usually no distinction made between services provided on site and those provided through telemedicine and often no separate coding required for billing of remote services. Telemedicine encompasses different types of programs and services provided for the patient. Each component involves different providers and consumers.
· TeleICU: TeleICU is a collaborative, interprofessional model focusing on the care of critically ill patients using telehealth technologies.
· Telemonitoring: The process of using audio, video, and other telecommunications and electronic information processing technologies to monitor the health status of a patient from a distance.
· Telemonitoring: The process of using audio, video, and other telecommunications and electronic information processing technologies to monitor the health status of a patient from a distance.
· Clinical Decision Support System (CCDS): Systems (usually electronically based and interactive) that provide clinicians, staff, patients, and other individuals with knowledge and person-specific information, intelligently filtered and presented at appropriate times, to enhance health and health care. (http://healthit.ahrq.gov/images/jun09cdsreview/09_0069_ef.html)
· e-Prescribing: The electronic generation, transmission and filling of a medical prescription, as opposed to traditional paper and faxed prescriptions. E-prescribing allows for qualified healthcare personnel to transmit a new prescription or renewal authorization to a community or mail-order pharmacy.
· Home Health Care and Remote Monitoring Systems: Care provided to individuals and families in their place of residence for promoting, maintaining, or restoring health or for minimizing the effects of disability and illness, including terminal illness. In the Medicare Current Beneficiary Survey and Medicare claims and enrollment data, home health care refers to home visits by professionals including nu.
(Assmt 1; Week 3 paper) Using ecree Doing the paper and s.docxAASTHA76
The document provides instructions for students on completing Assignment 1 for an online history course. It explains how to access and submit the assignment through the ecree online platform. Students are instructed to write a 2-page paper in 4 parts addressing how diversity was dealt with in America from 1865 to the 1920s. The document provides a sample paper format and emphasizes including an introduction with thesis, 3 examples supporting the thesis, consideration of an opposing view, and conclusion relating the topic to modern times. Sources must be cited within the paper and listed at the end using the SWS format.
(Image retrieved at httpswww.google.comsearchhl=en&biw=122.docxAASTHA76
(Image retrieved at https://www.google.com/search?hl=en&biw=1229&bih=568&tbm=isch&sa=1&ei=fmYIW9W3G6jH5gLn7IHYAQ&q=analysis&oq=analysis&gs_l=img.3..0i67k1l2j0l5j0i67k1l2j0.967865.968569.0.969181.7.4.0.0.0.0.457.682.1j1j4-1.3.0....0...1c.1.64.img..5.2.622...0i7i30k1.0.rL9KcsvXM1U#imgrc=LU1vXlB6e2doDM: / )
ESOL 052 (Essay #__)
Steps:
1. Discuss the readings, videos, and photographs in the Truth and Lies module on Bb.
2. Select a significant/controversial photograph to analyze. (The photograph does not have to be from Bb.)
3. Choose one of the following essay questions:
a. What truth does this photograph reveal?
b. What lie does this photograph promote?
c. Why/How did people deliberately misuse this photograph and distort its true meaning?
d. Why was this photograph misinterpreted by so many people?
e. Why do so many people have different reactions to this photograph?
f. ___________________________________________________________________________?
(Students may create their own visual analysis essay question as long as it is pre-approved by the instructor.)
4. Use the OPTIC chart to brainstorm and take notes on your photograph.
5. Use a pre-writing strategy (outline, graphic organizer, etc.) to organize your ideas.
6. Using correct MLA format, write a 3-5 page essay.
7. Type a Works Cited page. (Use citationmachine.net, easybib.com, etc. to format your info.)
8. Peer and self-edit during the writing process (Bb Wiki, in/outside class).
9. Get feedback from your peers and an instructor during the writing process.
(Note: Students who visit the Writing Center and show me proof get 2 additional days to work on the assignment.)
10. Proofread/edit/revise during the writing process.
11. Put your pre-writing, essay, and Works Cited page in 1 Word document and upload it on Bb by midnight on ______. (If a student submits an essay without pre-writing or without a Works Cited page, he/she will receive a zero. If a student submits an assignment late, he/she will receive a zero. If a student plagiarizes, he/she will receive a zero.)
Purpose: Students will be able to use their reading, writing, critical thinking, and research skills to conduct a visual analysis that explores the theme of Truth and Lies.
Tone: The tone of this assignment should be formal and academic.
Language: The diction and syntax of this assignment should be formal and academic. Students should not use second person pronouns (you/your), contractions, abbreviations, slang, or any type of casual language. Students should refer to the diction and syntax guidelines in the writing packet.
Audience: The audience of this assignment is the student’s peers and instructor.
Format: MLA style (double spaced, 1 in. margins, Times New Roman 12 font, pagination, heading, title, tab for each paragraph, in-text citations, Works Cited page, hanging indents, etc.)
Requirements:
In order for a student to earn a minimum passing grade of 70% on this assignment, h.
(Dis) Placing Culture and Cultural Space Chapter 4.docxAASTHA76
(Dis) Placing Culture and Cultural Space
Chapter 4
+
Chapter Objectives
Describe the relationships among culture, place, cultural space, and identity in the context of globalization.
Explain how people use communicative practices to construct, maintain, negotiate, and hybridize cultural spaces.
Explain how cultures are simultaneously placed and displaced in the global context leading to segregated, contested and hybrid cultural spaces.
Describe the practice of bifocal vision to highlight the linkages between “here” and “there” as well as the connections between present and past.
+
Introduction
Explore the cultural and intercultural communication dimensions of place, space and location. We will examine:
The dynamic process of placing and displacing cultural space in the context of globalization.
How people use communicative practices to construct, maintain, negotiate, and hybridize cultural spaces
How segregated, contested, and hybrid cultural spaces are both shaped by the legacy of colonialism and the context of globalization.
How Hip hop culture illustrates the cultural and intercultural dimensions of place, space, and location in the context of globalization
+
Placing Culture and Cultural Space
Culture, by definition, is rooted in place with a reciprocal relationship between people and place
Culture:
“Place tilled” in Middle English
Colere : “to inhabit, care for, till, worship” in Latin
In the context of globalization, what is the relationship between culture and place?
Culture is both placed and displaced
+
Cultural Space
The communicative practices that construct meanings in, through and about particular places
Cultural space shapes verbal and nonverbal communicative practices
i.e. Classrooms, dance club, library.
Cultural spaces are constructed through the communicative practices developed and lived by people in particular places
Communicative practices include:
The languages, accents, slang, dress, artifacts, architectural design, the behaviors and patterns of interaction, the stories, the discourses and histories
How is the cultural space of your home, neighborhood, city, and state constructed through communicative practices?
+
Place, Cultural Space and Identity
Place, Culture, Identity and Difference
What’s the relationship between place and identity?
Avowed identity:
The way we see, label and make meaning about ourselves and
Ascribed identity:
The way others view, name and describe us and our group
Examples of how avowed and ascribed identities may conflict?
How is place related to standpoint and power?
Locations of enunciation:
Sites or positions from which to speak.
A platform from which to voice a perspective and be heard and/or silenced.
+
Displacing Culture and Cultural Space
(Dis) placed culture and cultural space:
A notion that captures the complex, contradictory and contested nature of cultural space and the relationship between culture and place that has emerged in the context o.
(1) Define the time value of money. Do you believe that the ave.docxAASTHA76
(1) Define the time value of money. Do you believe that the average person considers the time value of money when they make investment decisions? Please explain.
(2) Distinguish between ordinary annuities and annuities due. Also, distinguish between the future value of an annuity and the present value of an annuity.
.
(chapter taken from Learning Power)From Social Class and t.docxAASTHA76
This document summarizes Jean Anyon's observations of 5 elementary schools that served different socioeconomic classes. In working-class schools, classroom activities focused on rote memorization and following procedures without explanation of underlying concepts. Work involved copying steps and notes from the board. In contrast, more affluent schools emphasized conceptual learning, creativity, and preparing students for professional careers through activities like experiments and projects. Anyon concluded schools were preparing students for different roles in the economy and society based on their social class.
(Accessible at httpswww.hatchforgood.orgexplore102nonpro.docxAASTHA76
(Accessible at https://www.hatchforgood.org/explore/102/nonprofit-photography-ethics-and-approaches)
Nonprofit Photography: Ethics
and Approaches
Best practices and tips on ethics and approaches in
humanitarian photography for social impact.
The first moon landing. The Vietnamese ‘napalm girl’, running naked and in agony. The World
Trade Centers falling.
As we know, photography carries the power to inspire, educate, horrify and compel its viewers to
take action. Images evoke strong and often public emotions, as people frequently formulate their
opinions, judgments and behaviors in response to visual stimuli. Because of this, photography
can wield substantial control over public perception and discourse.
Moreover, photography in our digital age permits us to deliver complex information about
remote conditions which can be rapidly distributed and effortlessly processed by the viewer.
Recently, we’ve witnessed the profound impact of photography coupled with social media:
together, they have fueled political movements and brought down a corrupt government.
Photography can - and has - changed the course of history.
Ethical Considerations
Those who commission and create photography of marginalized populations to further an
organizations’ mission possess a tremendous responsibility. Careful ethical consideration should
be given to all aspects of the photography supply chain: its planning, creation, and distribution.
When planning a photography campaign, it is important to examine the motives for creating
particular images and their potential impact. Not only must a faithful, comprehensive visual
depiction of the subjects be created to avoid causing misconception, but more importantly, the
subjects’ dignity must be preserved. Words and images that elicit an emotional response by their
sheer shock value (e.g. starving, skeletal children covered in flies) are harmful because they
exploit the subjects’ condition in order to generate sympathy for increasing charitable donations
or support for a given cause. In addition to violating privacy and human rights, this so-called
'poverty porn’ is harmful to those it is trying to aid because it evokes the idea that the
marginalized are helpless and incapable of helping themselves, thereby cultivating a culture of
paternalism. Poverty porn is also detrimental because it is degrading, dishonoring and robs
people of their dignity. While it is important to illustrate the challenges of a population, one must
always strive to tell stories in a way that honors the subjects’ circumstances, and (ideally)
illustrates hope for their plight.
Legal issues
Legal issues are more clear cut when images are created or used in stable countries where legal
precedent for photography use has been established. Image use and creation becomes far more
murky and problematic in countries in which law and order is vague or even nonexistent.
Even though images created for no.
(a) The current ratio of a company is 61 and its acid-test ratio .docxAASTHA76
(a) The current ratio of a company is 6:1 and its acid-test ratio is 1:1. If the inventories and prepaid items amount to $445,500, what is the amount of current liabilities?
Current Liabilities
$
89100
(b) A company had an average inventory last year of $113,000 and its inventory turnover was 6. If sales volume and unit cost remain the same this year as last and inventory turnover is 7 this year, what will average inventory have to be during the current year? (Round answer to 0 decimal places, e.g. 125.)
Average Inventory
$
96857
(c) A company has current assets of $88,800 (of which $35,960 is inventory and prepaid items) and current liabilities of $35,960. What is the current ratio? What is the acid-test ratio? If the company borrows $12,970 cash from a bank on a 120-day loan, what will its current ratio be? What will the acid-test ratio be? (Round answers to 2 decimal places, e.g. 2.50.)
Current Ratio
2.47
:1
Acid Test Ratio
:1
New Current Ratio
:1
New Acid Test Ratio
:1
(d) A company has current assets of $586,700 and current liabilities of $200,100. The board of directors declares a cash dividend of $173,700. What is the current ratio after the declaration but before payment? What is the current ratio after the payment of the dividend? (Round answers to 2 decimal places, e.g. 2.50.)
Current ratio after the declaration but before payment
:1
Current ratio after the payment of the dividend
:1
The following data is given:
December 31,
2015
2014
Cash
$66,000
$52,000
Accounts receivable (net)
90,000
60,000
Inventories
90,000
105,000
Plant assets (net)
380,500
320,000
Accounts payable
54,500
41,500
Salaries and wages payable
11,500
5,000
Bonds payable
70,500
70,000
8% Preferred stock, $40 par
100,000
100,000
Common stock, $10 par
120,000
90,000
Paid-in capital in excess of par
80,000
70,000
Retained earnings
190,000
160,500
Net credit sales
930,000
Cost of goods sold
735,000
Net income
81,000
Compute the following ratios: (Round answers to 2 decimal places e.g. 15.25.)
(a)
Acid-test ratio at 12/31/15
: 1
(b)
Accounts receivable turnover in 2015
times
(c)
Inventory turnover in 2015
times
(d)
Profit margin on sales in 2015
%
(e)
Return on common stock equity in 2015
%
(f)
Book value per share of common stock at 12/31/15
$
Exercise 24-4
As loan analyst for Utrillo Bank, you have been presented the following information.
Toulouse Co.
Lautrec Co.
Assets
Cash
$113,900
$311,200
Receivables
227,200
302,700
Inventories
571,200
510,700
Total current assets
912,300
1,124,600
Other assets
506,000
619,800
Total assets
$1,418,300
$1,744,400
Liabilities and Stockholders’ Equity
Current liabilities
$291,300
$350,400
Long-term liabilities
390,800
506,000
Capital stock and retained earnings
736,200
888,000
Total liabilities and stockholders’ equity
$1.
(1) How does quantum cryptography eliminate the problem of eaves.docxAASTHA76
Quantum cryptography eliminates eavesdropping by using the principles of quantum mechanics, where any interception of encrypted information can be detected. However, quantum cryptography has limitations in the distance over which it can be effectively implemented and requires specialized equipment. Developments in both theoretical and applied cryptography will be influenced by advances in computing power, communication technologies, user needs for security and privacy, and socioeconomic or geopolitical factors.
#transformation
10
Event
Trends
for 2019
10 Event Trends for 2019
C O P Y R I G H T
All rights reserved. No part of this report may be
reproduced or transmitted in any form or by any
means whatsoever (including presentations, short
summaries, blog posts, printed magazines, use
of images in social media posts) without express
written permission from the author, except in the
case of brief quotations (50 words maximum and
for a maximum of 2 quotations) embodied in critical
articles and reviews, and with clear reference to
the original source, including a link to the original
source at https://www.eventmanagerblog.com/10-
event-trends/. Please refer all pertinent questions
to the publisher.
page 2
https://www.eventmanagerblog.com/10-event-trends/
https://www.eventmanagerblog.com/10-event-trends/
10 Event Trends for 2019
CONTENTS
INTRODUCTION page 5
TRANSFORMATION 8
10. PASSIVE ENGAGEMENT 10
9. CONTENT DESIGN 13
8. SEATING MATTERS 16
7. JOMO - THE JOY OF MISSING OUT 19
6. BETTER SAFE THAN SORRY 21
5. CAT SPONSORSHIP 23
4. SLOW TICKETING 25
3. READY TO BLOCKCHAIN 27
2. MARKETING BUDGETS SHIFTING MORE TO EVENTS 28
1. MORE THAN PLANNERS 30
ABOUT THE AUTHOR 31
CMP CREDITS 32
CREDITS AND THANKS 32
DISCLAIMER 32
page 3
INTERACTIVITY
AT THE HEART OF YOUR MEETINGS
Liven up your presentations!
EVENIUM
ConnexMe
San Francisco/Paris [email protected]
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10 Event Trends for 2019
I am very glad to welcome you to the 8th edition of our annual
event trends. This is going to be a different one.
One element that made our event trends stand out from
the thousands of reports and articles on the topic is that we
don’t care about pleasing companies, pundits, suppliers, star
planners and the likes. Our only focus is you, the reader, to
help you navigate through very uncertain times.
This is why I decided to bring back this report, by far the most
popular in the industry, to its roots. 10 trends that will actually
materialize between now and November 2019, when we will
publish edition number nine.
I feel you have a lot going on, with your events I mean.
F&B, room blocks, sponsorship, marketing security, technology.
I think I failed you in previous editions. I think I gave you too
much. This report will be the most concise and strategic piece
of content you will need for next year.
If you don’t read anything else this year, it’s fine. As long as you
read the next few words.
INTRODUCTION
INTRODUCTION -
Julius Solaris
EventMB Editor
page 5
https://www.eventmanagerblog.com
10 Event Trends for 2019
How did I come up with these trends?
~ As part of this report, we reviewed 350 events. Some of the most successful
worldwide.
~ Last year we started a community with a year-long trend watch. That helped
us to constantly research new things happening in the industry.
~ We have reviewed north of 300 event technology solutions for our repor.
$10 now and $10 when complete Use resources from the required .docxAASTHA76
$10 now and $10 when complete
Use resources from the required readings or the GCU Library to create a 10‐15 slide digital presentation to be shown to your colleagues informing them of specific cultural norms and sociocultural influences affecting student learning at your school.
Choose a culture to research. State the country or countries of origin of your chosen culture and your reason for selecting it.
Include sociocultural influences on learning such as:
Religion
Dress
Cultural Norms
Food
Socialization
Gender Differences
Home Discipline
Education
Native Language
Include presenter’s notes, a title slide, in‐text citations, and a reference slide that contains three to five sources from the required readings or the GCU Library.
.
#include <string.h>
#include <stdlib.h>
#include <sys/types.h>
#include <sys/wait.h>
#include <stdio.h>
#include <unistd.h>
#include <string.h>
// Function: void parse(char *line, char **argv)
// Purpose : This function takes in a null terminated string pointed to by
// <line>. It also takes in an array of pointers to char <argv>.
// When the function returns, the string pointed to by the
// pointer <line> has ALL of its whitespace characters (space,
// tab, and newline) turned into null characters ('\0'). The
// array of pointers to chars will be modified so that the zeroth
// slot will point to the first non-null character in the string
// pointed to by <line>, the oneth slot will point to the second
// non-null character in the string pointed to by <line>, and so
// on. In other words, each subsequent pointer in argv will point
// to each subsequent "token" (characters separated by white space)
// IN the block of memory stored at the pointer <line>. Since all
// the white space is replaced by '\0', every one of these "tokens"
// pointed to by subsequent entires of argv will be a valid string
// The "last" entry in the argv array will be set to NULL. This
// will mark the end of the tokens in the string.
//
void parse(char *line, char **argv)
{
// We will assume that the input string is NULL terminated. If it
// is not, this code WILL break. The rewriting of whitespace characters
// and the updating of pointers in argv are interleaved. Basically
// we do a while loop that will go until we run out of characters in
// the string (the outer while loop that goes until '\0'). Inside
// that loop, we interleave between rewriting white space (space, tab,
// and newline) with nulls ('\0') AND just skipping over non-whitespace.
// Note that whenever we encounter a non-whitespace character, we record
// that address in the array of address at argv and increment it. When
// we run out of tokens in the string, we make the last entry in the array
// at argv NULL. This marks the end of pointers to tokens. Easy, right?
while (*line != '\0') // outer loop. keep going until the whole string is read
{ // keep moving forward the pointer into the input string until
// we encounter a non-whitespace character. While we're at it,
// turn all those whitespace characters we're seeing into null chars.
while (*line == ' ' || *line == '\t' || *line == '\n' || *line == '\r')
{ *line = '\0';
line++;
}
// If I got this far, I MUST be looking at a non-whitespace character,
// or, the beginning of a token. So, let's record the address of this
// beginning of token to the address I'm pointing at now. (Put it in *argv)
.
$ stated in thousands)Net Assets, Controlling Interest.docxAASTHA76
$ stated in thousands)
Net Assets, Controlling Interest
–
–
Net Assets, Noncontrolling Interest
AUDIT COMMITTEE
of the
Executive Board of the Boy Scouts of America
Francis R. McAllister, Chairman
David Biegler Ronald K. Migita
Dennis H. Chookaszian David Moody
Report of Independent Auditors
To the Executive Board of the National Council of the Boy Scouts of America
We have audited the accompanying consolidated financial statements of the National Council of the Boy Scouts
of America and its affiliates (the National Council), which comprise the consolidated statement of financial position
as of December 31, 2016, and the related consolidated statements of revenues, expenses, and other changes in net
assets, of functional expenses and of cash flows for the year then ended.
Management’s Responsibility for the Consolidated Financial Statements
Management is responsible for the preparation and fair presentation of the consolidated financial statements
in accordance with accounting principles generally accepted in the United States of America; this includes the
design, implementation and maintenance of internal control relevant to the preparation and fair presentation of
consolidated financial statements that are free from material misstatement, whether due to fraud or error.
Auditors’ Responsibility
Our responsibility is to express an opinion on the consolidated financial statements based on our audit. We
conducted our audit in accordance with auditing standards generally accepted in the United States of America.
Those standards require that we plan and perform the audit to obtain reasonable assurance about whether the
consolidated financial statements are free from material misstatement.
An audit involves performing procedures to obtain audit evidence about the amounts and disclosures in the
consolidated financial statements. The procedures selected depend on our judgment, including the assessment of
the risks of material misstatement of the consolidated financial statements, whether due to fraud or error. In making
those risk assessments, we consider internal control relevant to the National Council’s preparation and fair
presentation of the consolidated financial statements in order to design audit procedures that are appropriate in the
circumstances, but not for the purpose of expressing an opinion on the effectiveness of the National Council’s
internal control. Accordingly, we express no such opinion. An audit also includes evaluating the appropriateness of
accounting policies used and the reasonableness of significant accounting estimates made by management, as well as
evaluating the overall presentation of the consolidated financial sta.
#include <stdio.h>
#include <stdlib.h>
#include <pthread.h>
#include <time.h>
#include <unistd.h>
// Change the constant below to change the number of philosophers
// coming to lunch...
// This is a known GOOD solution based on the Arbitrator
// solution
#define PHILOSOPHER_COUNT 20
// Each philosopher is represented by one thread. Each thread independenly
// runs the same "think/start eating/finish eating" program.
pthread_t philosopher[PHILOSOPHER_COUNT];
// Each chopstick gets one mutex. If there are N philosophers, there are
// N chopsticks. That's the whole problem. There's not enough chopsticks
// for all of them to be eating at the same time. If they all cooperate,
// everyone can eat. If they don't... or don't know how.... well....
// philosophers are going to starve.
pthread_mutex_t chopstick[PHILOSOPHER_COUNT];
// The arbitrator solution adds a "waiter" that ensures that only pairs of
// chopsticks are grabbed. Here is the mutex for the waiter ;)
pthread_mutex_t waiter;
void *philosopher_program(int philosopher_number)
{ // In this version of the "philosopher program", the philosopher
// will think and eat forever.
while (1)
{ // Philosophers always think before they eat. They need to
// build up a bit of hunger....
//printf ("Philosopher %d is thinking\n", philosopher_number);
usleep(1);
// That was a lot of thinking.... now hungry... this
// philosopher (who knows his own number) grabs the chopsticks
// to her/his right and left. The chopstick to the left of
// philosopher N is chopstick N. The chopstick to the right
// of philosopher N is chopstick N+1
//printf ("Philosopher %d wants chopsticks\n",philosopher_number);
pthread_mutex_lock(&waiter);
pthread_mutex_lock(&chopstick[philosopher_number]);
pthread_mutex_lock(&chopstick[(philosopher_number+1)%PHILOSOPHER_COUNT]);
pthread_mutex_unlock(&waiter);
// Hurray, if I got this far I'm eating
printf ("Philosopher %d is eating\n",philosopher_number);
//usleep(1); // I spend twice as much time eating as thinking...
// typical....
// I'm done eating. Now put the chopsticks back on the table
//printf ("Philosopher %d finished eating\n",philosopher_number);
pthread_mutex_unlock(&chopstick[philosopher_number]);
pthread_mutex_unlock(&chopstick[(philosopher_number+1)%PHILOSOPHER_COUNT]);
//printf("Philosopher %d has placed chopsticks on the table\n", philosopher_number);
}
return(NULL);
}
int main()
{ int i;
srand(time(NULL));
for(i=0;i<PHILOSOPHER_COUNT;i++)
pthread_mutex_init(&chopstick[i],NULL);
pthread_mutex_init(&waiter,NULL);
for(i=0;i<PH.
#Assessment BriefDiploma of Business Eco.docxAASTHA76
#
Assessment BriefDiploma of Business Economics for Business
Credit points : 6 Prerequisites : None Co-requisites :
Subject Coordinator : Harriet Scott
Deadline : Sunday at the end of week 10 (Turnitin via CANVAS submission). Reflection due week 11 in tutorials.
ASSESSMENT TASK #3: FINAL CASE STUDY REPORT 25%
TASK DESCRIPTION
This assessment is a formal business report on a case study. Case studies will be assigned to students in the Academic and Business Communication subject. Readings on the case study are available on Canvas, in the Economics for Business subject. Students will also write a reflection on learning in tutorial classes in week 11.
LEARNING OUTCOMES
· Demonstrates understanding of microeconomic and macroeconomic concepts
· Applies economic concepts to contemporary issues and events
· Evaluates possible solutions for contemporary economic and business problems
· Communicates economic information in a business report format
INSEARCH CRICOS provider code: 00859D I UTS CRICOS provider code: 00099F INSEARCH Limited is a controlled entity of the University of Technology, Sydney (UTS), a registered non-self accrediting higher education institution and a pathway provider to UTS.
1. Refer to the case study you are working on for your presentation in Academic and Business Communication. Read the news stories for your case study, found on Canvas.
2. Individually, write a business report that includes the following information:
· Description of the main issue/problem and causes
· Description of the impact on stakeholders
· Analysis of economic concepts relevant to the case study (3-5 concepts)
· Recommendations for alternate solutions to the issue/problem
3. In your week 11 tutorial, write your responses to the reflection questions provided by your tutor, describing your learning experience in this assessment.
Other Requirements Format: Business Report
· Use the Business Report format as taught in BABC001 (refer to CANVAS Help for more information)
· Write TEEL paragraphs (refer to CANVAS Help for more information)
· All work submitted must be written in your own words, using paraphrasing techniques taught in BABC001
· Check Canvas — BECO — Assessments — Final Report page and ‘Writing a report' flyer for more information
Report Presentation: You need to include:
· Cover page as taught in BABC001
· Table of contents - list headings, subheadings and page numbers
· Reference list - all paraphrased/summarised/quoted evidence should include citations; all citations should be detailed in the Reference List
Please ensure your assignment is presented professionally. Suggested structure:
· Cover page
· Table of contents (bold, font size 18)
· Executive summary (bold, font size 18)
· 1.0 Introduction (bold, font size 16)
· 2.0 Main issue (bold, font size 16)
o 2.1 Causes (italics, font size 14)
· 3.0 Stakeholders (bold, font size 16)
o 3.1 Stakeholder 1 (italics, font size 14) o 3.2 Stakeholder 2 (italics, font size 14) o 3.3 Stakeholde.
#include <stdio.h>
#include <stdint.h>
#include <stdbool.h>
// Prototype of FOUR functions, each for a STATE.
// The func in State 1 performs addition of "unsigned numbers" x0 and x1.
int s1_add_uintN(int x0, int x1, bool *c_flg);
// The func in State 2 performs addition of "signed numbers" x0 and x1.
int s2_add_intN(int x0, int x1, bool *v_flg);
// The func in State 3 performs subtraction of "unsigned numbers" x0 and x1.
int s3_sub_uintN(int x0, int x1, bool *c_flg);
// The func in State 3 performs subtraction of "signed numbers" x0 and x1.
int s4_sub_intN(int x0, int x1, bool *v_flg);
// We define the number of bits and the related limits of unsigned and
// and signed numbers.
#define N 5 // number of bits
#define MIN_U 0 // minimum value of unsigned N-bit number
#define MAX_U ((1 << N) - 1) // maximum value of unsigned N-bit number
#define MIN_I (-(1 << (N-1)) ) // minimum value of signed N-bit number
#define MAX_I ((1 << (N-1)) - 1) // maximum value of signed N-bit number
// We use the following three pointers to access data, which can be changed
// when the program pauses. We need to make sure to have the RAM set up
// for these addresses.
int *pIn = (int *)0x20010000U; // the value of In should be -1, 0, or 1.
int *pX0 = (int *)0x20010004U; // X0 and X1 should be N-bit integers.
int *pX1 = (int *)0x20010008U;
int main(void) {
enum progState{State1 = 1, State2, State3, State4};
enum progState cState = State1; // Current State
bool dataReady = false;
bool cFlg, vFlg;
int result;
while (1) {
dataReady = false;
// Check if the data are legitimate
while (!dataReady) {
printf("Halt program here to provide correct update of data\n");
printf("In should be -1, 0, and 1 and ");
printf("X0 and X1 should be N-bit SIGNED integers\n");
if (((-1 <= *pIn) && (*pIn <= 1)) &&
((MIN_I <= *pX0) && (*pX0 <= MAX_I)) &&
((MIN_I <= *pX1) && (*pX1 <= MAX_I))) {
dataReady = true;
}
}
printf("Your input: In = %d, X0 = %d, X1 = %d \n", *pIn, *pX0, *pX1);
switch (cState) {
case State1:
result = s1_add_uintN(*pX0, *pX1, &cFlg);
printf("State = %d, rslt = %d, Cflg = %d\n", cState, result, cFlg);
cState += *pIn;
if (cState < State1) cState += State4;
break;
case State2:
result = s2_add_intN(*pX0, *pX1, &vFlg);
printf("State = %d, rslt = %d, Vflg = %d\n", cState, result, vFlg);
cState += *pIn;
break;
case State3:
case State4:
default:
printf("Error with the program state\n");
}
}
}
int s1_add_uintN(int x0, int x1, bool *c_flg) {
if (x0 < 0) x0 = x0 + MAX_U + 1;
if.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
2. the decision maker in making
sure that the chosen statistical method will, indeed, produce the
results needed and expected.
Using the wrong type of measurement data with a selected
statistic tool will result in erroneous
results, errors, and ineffective decision making.
Measurement, or numerical, data is divided into four types:
nominal, ordinal, interval, and ratio.
The businessperson, because of administering questionnaires,
taking polls, conducting surveys,
administering tests, and counting events, products, and a host of
other numerical data
instrumentations, garners all the numerical values associated
with these four types.
Nominal Data
Nominal data is the simplest of all four forms of numerical data.
The mathematical values are
assigned to that which is being assessed simply by arbitrarily
assigning numerical values to a
characteristic, event, occasion, or phenomenon. For example, a
human resources (HR) manager
wishes to determine the differences in leadership styles between
managers who are at different
geographical regions. To compute the differences, the HR
manager might assign the following
values: 1 = West, 2 = Midwest, 3 = North, and so on. The
numerical values are not descriptive of
anything other than the location and are not indicative of
quantity.
Ordinal Data
In terms of ordinal data, the variables contained within the
4. 3 Computing Descriptive Statistics
Ratio Data
Some measurements, in addition to having an equal interval
value, also have an absolute zero
value. In this instance, zero represents the absence of the
variable being measured. With an
absolute zero value, for example, either you have some money
or you do not have any money. In
the money scenario, adding the interval quality to ratio data
would mean that you have no money,
$1.00 to $5.00, $6.00 to $10.00, and so on. What is being stated
here is that ratio data is
quantitative as it tells us the amount of the variable being
measured. Consider other examples of
ration data such as the percentage of votes received by a
candidate, the gross national product
per capita, the current American crime rate, and the number of
finished consumer products
manufactured per day per person—all of these are examples of
ratio data.
Why Measurement Data Matters
To the behavioral scientist, when conducting research, the level
of measurement is important
because the higher the level of measurement of a variable, the
more powerful the statistical
techniques that can be employed to analyze the data. Take, for
example, the voters’ choice
wherein the nominal variables in the 2004 presidential race
were Bush, Kerry, Nader, etc. One
can count the number of votes each candidate received as well
as calculate the percentage each
5. candidate received. One can also calculate joint frequencies and
percentages by region and by
gender. One can also calculate the relationship between region
and vote and whether the
relationship occurred by chance. Unfortunately, using nominal
measurement data does not permit
one to use advanced methods of statistics. In the example
presented above, even when we
assign numbers to each candidate, we cannot very well
determine that Bush plus Kerry equals
Nader or that Nader divided by Kerry is half way between Bush
and Kerry, and so on. In an
attempt to calculate the situation, only the mentioned higher
level statistical techniques are
required.
If one uses a technique that assumes a higher level of
measurement than is appropriate for the
data, there is a risk of getting meaningless results and answers.
At the same time, if one uses a
technique that fails to take advantage of a higher level of
measurement, important things are often
overlooked about the data collected.
Computing and Using the Mean, Median, Mode, Frequency
Distribution, and Standard
Deviation
Regardless of the type of measurement data garnered from a
data collection set, the mean,
mode, median, frequency distribution, and standard deviation
can be calculated. However, simply
because mathematical calculations can be made does not imply
the legitimacy of their use. The
remainder of this section will deal with how one computes and
uses measures of central tendency
8. Second shift total number of employees (n) = 11
Mean = 72/11 = 6.55 units produced
First shift units (ΣΧ) = 69
First shift total number of employees (n) = 11
Mean = 69/11 = 6.27 units produced
Conclusion
What appears to be happening is that the second shift of line
employees produces more baby
strollers than the first shift of line employees.
Caution: No other conclusions can be drawn from the mean. To
determine whether production
differences are significant, a higher level statistical process
must be used. We can only “describe”
what has happened here and cannot draw conclusions or make
inferences as to why or how
much.
9. Median
Median is broadly defined as the middle value of a set of
measurement values. Just like medians
divide roads down the middle, so does the median in statistics
in that the median is simply the
middle number For highly skewed distributions, the median is a
better measure of central
tendency than the mean as extreme outliners or measurement
values do not affect it.
Formula:
No statistical formula is needed. To calculate the median of a
group of measurement scores,
simply find the
midpoint of the distribution by arranging the scores in
ascending order—from low to high.
Example
Second-shift stroller production 5, 7, 8, 5, 7, 7, 9, 9, 5, 5, 5
Ordered Values –5, 5, 5, 5, 5, 7, 7,
7, 8, 9, 9
11. Mode is defined as the most frequent value in a measurement
data set and is of limited value.
Example
Second-shift stroller production: 5, 7, 8, 5, 7, 7, 9, 9, 5, 5, 5
Mode = 5
First-shift stroller production: 7, 7, 7, 7, 6, 6, 9, 2, 4, 6, 8
Mode = 7
Variability
Furthermore, the central tendency is a summary measure of the
overall quantity of a
measurement data set. Variability (or dispersion) measures the
amount of spread in a
measurement data set. Variability is generally measured using
three criteria: range, variance, and
standard deviation.
Example
13. Second-shift stroller production: 5, 7, 8, 5, 7, 7, 9, 9, 5, 5, 5
Range = 9 – 5 = 4
First-shift stroller production: 7, 7, 7, 7, 6, 6, 9, 2, 4, 6, 8
Range = 9 – 2 = 7
Variance
Variance is a deviation. It is a measure of by how much each
point frequency distribution lies
above or below the mean for the entire data set:
Note: If you add all the deviation scores for a measurement data
set together, you will
automatically get the mean for that data set.
In order to define the amount of deviation of a data set from the
mean, calculate the mean of all
the deviation scores, i.e., the variance.
Formula:
14. Standard Deviation
In statistics, the standard deviation represents the measure of
the spread of a set of
measurement values from the mean of the data set. Putting it
another way, the standard deviation
can be defined as the average amount by which scores in a
distribution differ from the mean while
ignoring the sign of the difference, i.e., the plus or minus value.
Further, standard deviations are
only good when referring to single data or measurement values,
i.e., finding out when a single
score falls with reference to being above or below the mean.
To find out the standard deviation of a data set, you must
perform the following steps:
1. Calculate the mean of all the scores.
2. Find the deviation of each score from the mean.
3. Square each deviation.
4. Calculate the average of each deviation.
5. Calculate the square root of the average deviation.
16. accomplish per month.
The monthly closings are as follows: 8, 25, 7, 5, 8, 3, 10, 12, 9
1. First, calculate the mean and determine N.
2. Remember, the mean is the sum of scores divided by N,
where N is the number of scores.
3. Therefore, the mean = (8+25+7+5+8+3+10+12+9) / 9 or 9.67
4. Then, calculate the standard deviation, n, as illustrated
below.
Squared
Score Mean Deviation* Deviation
8 9.67 –1.67 2.79
25 9.67 +15.33 235.01
7 9.67 –2.67 7.13
5 9.67 –4.67 21.81
8 9.67 –1.67 2.79
18. = Square root (320.01/ (9 – 1))
= Square root (40)
= 6.32
Conclusion
Real estate closings for the month vary ±6.32 closings above or
below the mean of a 9.67 closing
average. Caution must be exercised here as no conclusion can
be drawn as to whether this is an
acceptable range of real estate closing activity. In other words,
you cannot draw a conclusion
about whether the closings are profitable, not profitable, or
represent an industry average. Further
statistical data analysis would have to be conducted to draw any
such conclusion.
Page 10 of 5
Research and Evaluation Design
20. also be considered the road map to what has happened or lies
ahead. Further, visually reported
data are not only colorful but also easy to construct, and one
does not have to be a statistician to
create them.
Unfortunately, visually reported data results in the behavioral
science arena have two primary
drawbacks, namely, the presenter and the lack of data
sophistication. For the most part, visually
presented data are based on percentages, raw numeric data
scores, and frequencies. Rarely is
visually presented material based on true value statistics based
on inferential statistical findings.
The reason is that the values for inferential statistics are not
amenable to graphs and charts, and
their values speak mathematically for themselves. That is to
say, inferential statistical values are
mathematical values that must be interpreted and are not
subjected to graphic presentation.
A Prelude to Statistical Data Analysis: Raw Data’s Wardrobe in
the Form of Bar Graphs,
Pie Charts, Line Graphs, Stem and Leaf Displays, and Box and
Whisper Plots
21. Introduction
Regardless of the type of chart or graph you use to illuminate or
express a concept or idea, they
all have one thing in common, namely, to communicate via
picture what is being studied or what
is happening. A behavioral scientist, or any other person who
wants to show what is taking place,
makes use of bar graphs, pie charts, line graphs, leaf displays,
and box and whisper plots.
Unfortunately, however, those who are not well-grounded and
informed about statistical
processes oftentimes rely on these pictorial presentations to
draw conclusions or make inferences
about what is being studied and evaluated. When this happens in
the behavioral science arena,
wrong decisions are often made about important matters.
Nonetheless, graphs and charts are an
important step in achieving what statistical processes will
eventually resolve.
Bar Graph
Bar graphs allow and encourage a great deal of poetic license to
those who design or make use
22. of them. The reason is that the one designing the bar graph
determines what scale is to be used.
This means that the information can be presented in a
misleading way. For example, by using a
smaller scale (for example, having each half inch of the height
of a bar represent 10 widgets
versus 50 widgets produced), one can exaggerate the truth,
make production differences look
more dramatic, or even exaggerate values. On the other hand, by
using a larger scale (for
example having each half inch of a bar represent 50 widgets
versus 10 widgets produced), a
person can downplay differences, make the end results look less
dramatic than they actually are
or even make small differences appear to be nonexistent. When
evaluating a bar graph, one
should do the following:
Page 11 of 5
Research and Evaluation Design
24. course) and determine how much money each participant spends
on transportation in a year. The
second step is to define income level. The third step is to
determine the horizontal axis and the
vertical axis. When you are seeking the “how” of something,
always remember that it becomes
the vertical axis and the “category” becomes the horizontal axis.
For this particular example, the
bar graph might possibly look like the following:
To construct a bar graph, go to your Windows task bar, click on
Insert, click on Chart, and enter
your information when prompted.
Pie Charts
Similar to bar graphs, pie charts, or circle graphs, are a pictorial
means using which the individual
can present information in a simple, nonstatistical form.
Further, like bar graphs, pie charts are
usually employed to compare percentages of the same whole.
Unlike bar graphs, pie charts do
not use a set of axes to plot information or data points. Pie
26. Example
Take, for example, a human resource manager who is interested
in finding out how three different
departments in a business situation waste time on the Internet
on a given day when they should
be engaged in company business. The human resource person
would collect data through a time
study process and determine the number of times each employee
in each department logged on
and off the Internet for personal business. The times would be
collected and added together, and
each department’s time would be converted to percentages.
Going further, the human resource
manager reported that, cumulatively, the employees of
Department 1 spent a total of five hours a
day on the Internet, those of Department 2 spent two hours a
day, and those of Department 3
spent six hours. The pie chart would look like the following: