This document provides information about the BUS 308 Statistics for Managers course, including discussion questions, assignments, and data sets used for analyzing gender pay equality. The course uses statistical techniques like descriptive statistics, hypothesis testing, ANOVA, correlation, and regression to analyze a sample employee salary data set. Students apply these methods to examine if gender is impacting pay when considering legal factors like grade, performance reviews, education, etc. The goal is for students to determine if their analyses provide evidence of gender pay inequality or consistency in the population from which the sample was drawn.
Psych 625 Effective Communication - tutorialrank.comBartholomew809
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Workshee
PSYCH 625 MENTOR Knowledge is divine--psych625mentor.comkarthik10037
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus
This document provides information and assignments for a PSYCH 625 class, including assignments for each week covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the class.
This document contains information about assignments for a PSYCH 625 course, including weekly worksheets and a multi-part statistics project. The worksheets cover topics like descriptive statistics, probability, statistical tests, and analyzing research studies. The statistics project involves analyzing a dataset using Excel to calculate descriptive statistics, form a hypothesis, and test it using appropriate statistical methods like t-tests. Completing the assignments will help students learn and apply statistical analysis skills.
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 MENTOR Education for Service-- psych625mentor.comKeatonJennings36
This document provides information and assignments for a PSYCH 625 class, including assignments for each week of the course covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the course.
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
Psych 625 Effective Communication - tutorialrank.comBartholomew809
For more course tutorials visit
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Workshee
PSYCH 625 MENTOR Knowledge is divine--psych625mentor.comkarthik10037
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus
This document provides information and assignments for a PSYCH 625 class, including assignments for each week covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the class.
This document contains information about assignments for a PSYCH 625 course, including weekly worksheets and a multi-part statistics project. The worksheets cover topics like descriptive statistics, probability, statistical tests, and analyzing research studies. The statistics project involves analyzing a dataset using Excel to calculate descriptive statistics, form a hypothesis, and test it using appropriate statistical methods like t-tests. Completing the assignments will help students learn and apply statistical analysis skills.
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 MENTOR Education for Service-- psych625mentor.comKeatonJennings36
This document provides information and assignments for a PSYCH 625 class, including assignments for each week of the course covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the course.
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
For more classes visit
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
For more course tutorials visit
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
PSYCH 625 Effective Communication - snaptutorial.comdonaldzs41
For more classes visit
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are also discussed, along with specific scales like Likert scales. The document stresses the importance of establishing the reliability and validity of measures to ensure the instruments accurately measure the intended constructs. Item analysis is presented as the first step, followed by assessing reliability and validity.
For more course tutorials visit
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
200 chapter 7 measurement :scaling by uma sekaran Irfan Sheikh
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. It also discusses developing rating scales and ranking scales to measure attitudes. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the constructs they are intended to measure.
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
BUS 308 Education Organization - snaptutorial.comdonaldzs179
For more classes visit
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BUS 308 Week 2 Problem Set
BUS 308 Week 3 Problem Set (Anova)
BUS 308 Week 4 Problem Set (Regression and Correlation)
BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers)
BUS 308 Week 1 DQ 1
BUS 308 Week 1 DQ 2
The document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale and the types of numerical operations that can be performed on data for each scale. Nominal scales involve simple sorting into categories while ratio scales allow for absolute comparisons between values. The document also covers various rating scale formats researchers can use to measure attributes, including Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are discussed as important aspects of ensuring measurement instruments accurately measure the intended constructs.
Bus 308 Effective Communication - snaptutorial.comHarrisGeorg10
BUS 308 Week 2 Problem Set
BUS 308 Week 3 Problem Set (Anova)
BUS 308 Week 4 Problem Set (Regression and Correlation)
BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers)
BUS 308 Week 1 DQ 1
BUS 308 Week 1 DQ 2
BUS 308 Week 2 DQ 1
BUS 308 Week 2 DQ 2
BUS 308 Week 3 DQ 1
BUS 308 Week 3 DQ 2
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Mgs. Orlando Lizaldes E.
Ciclo: Sexto
Bimestre: Segundo
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
PSYCH 625 Assignment Week 5 Correlation Worksheet (New Syllabus)
PSYCH 625 Assignment Week 6 Chi-Square Worksheet (New Syllabus)
PSYCH 625 Week 2 Statistics Project, Part 1 Opening Data In Microsoft®
1. The document discusses various types of attitude scales that can be used to measure attitudes, including Likert scales, semantic differential scales, numerical scales, constant sum scales, staple scales, and graphic rating scales.
2. It also covers Guttman scaling, which establishes a one-dimensional continuum to perfectly predict responses based on a total score. Guttman scaling involves having judges rate statements and arranging responses in a table to identify a cumulative scale.
3. Statistical techniques are used to examine the response table and test how well it fits a cumulative scale model, while also estimating a scale value for each item.
PSYCH 625 MENTOR Education Your Life / psych625mentor.comkopiko27
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PSYCH 625 Week 1 Individual Assignment Basic Concepts in Statistics Worksheet
PSYCH 625 Week 1 Individual Assignment Reliability and Validity Matrix
PSYCH 625 Week 1 Individual Assignment Time to Practice – Week One
PSYCH 625 MENTOR Become Exceptional--psych625mentor.comshanaabe77
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
This document outlines a protocol for unpacking standards into learning targets to improve common assessments. It explains how to analyze standards by underlining verbs, highlighting nouns, circling contexts, and identifying the type of learning target. Teachers will learn how to determine the depth of knowledge ceiling for each target and match assessment item types to target rigor. Sample essential outcomes are unpacked using the protocol steps. Guidelines are provided for constructing response questions that clearly communicate expectations and assessments are written for a sample standard.
Statistics Assignment Help from the Statistics Assignment Experts. Statistics assignment help is the most common assignment that students are mostly demand for. Statistics is the branch of mathematics, comprises the collection, summarizing, analysis, interpretation, and presentation of data.
This document discusses measurement in research and provides examples and guidelines. It covers topics such as selecting observable events, assigning numbers or symbols to represent aspects of events, applying mapping rules, and different levels of measurement including nominal, ordinal, interval and ratio scales. Reliability and validity are important criteria for good measurement. The document also discusses sampling methods like probability and non-probability designs as well as factors to consider for determining sample size.
Homework 1
Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
La literatura realista europea surgió a mediados del siglo XIX como una reacción al romanticismo. Se caracterizó por una visión objetiva y detallada de la sociedad a través de descripciones precisas y un lenguaje sobrio. Algunos de sus principales representantes fueron Balzac, Flaubert y Zola en Francia, Dickens en Inglaterra, Dostoievsky y Tolstoi en Rusia, Galdós en España y Twain en Estados Unidos.
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
For more course tutorials visit
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PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
PSYCH 625 Effective Communication - snaptutorial.comdonaldzs41
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are also discussed, along with specific scales like Likert scales. The document stresses the importance of establishing the reliability and validity of measures to ensure the instruments accurately measure the intended constructs. Item analysis is presented as the first step, followed by assessing reliability and validity.
For more course tutorials visit
www.newtonhelp.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
200 chapter 7 measurement :scaling by uma sekaran Irfan Sheikh
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. It also discusses developing rating scales and ranking scales to measure attitudes. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the constructs they are intended to measure.
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
BUS 308 Education Organization - snaptutorial.comdonaldzs179
For more classes visit
www.snaptutorial.com
BUS 308 Week 2 Problem Set
BUS 308 Week 3 Problem Set (Anova)
BUS 308 Week 4 Problem Set (Regression and Correlation)
BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers)
BUS 308 Week 1 DQ 1
BUS 308 Week 1 DQ 2
The document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale and the types of numerical operations that can be performed on data for each scale. Nominal scales involve simple sorting into categories while ratio scales allow for absolute comparisons between values. The document also covers various rating scale formats researchers can use to measure attributes, including Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are discussed as important aspects of ensuring measurement instruments accurately measure the intended constructs.
Bus 308 Effective Communication - snaptutorial.comHarrisGeorg10
BUS 308 Week 2 Problem Set
BUS 308 Week 3 Problem Set (Anova)
BUS 308 Week 4 Problem Set (Regression and Correlation)
BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers)
BUS 308 Week 1 DQ 1
BUS 308 Week 1 DQ 2
BUS 308 Week 2 DQ 1
BUS 308 Week 2 DQ 2
BUS 308 Week 3 DQ 1
BUS 308 Week 3 DQ 2
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Mgs. Orlando Lizaldes E.
Ciclo: Sexto
Bimestre: Segundo
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
PSYCH 625 Assignment Week 5 Correlation Worksheet (New Syllabus)
PSYCH 625 Assignment Week 6 Chi-Square Worksheet (New Syllabus)
PSYCH 625 Week 2 Statistics Project, Part 1 Opening Data In Microsoft®
1. The document discusses various types of attitude scales that can be used to measure attitudes, including Likert scales, semantic differential scales, numerical scales, constant sum scales, staple scales, and graphic rating scales.
2. It also covers Guttman scaling, which establishes a one-dimensional continuum to perfectly predict responses based on a total score. Guttman scaling involves having judges rate statements and arranging responses in a table to identify a cumulative scale.
3. Statistical techniques are used to examine the response table and test how well it fits a cumulative scale model, while also estimating a scale value for each item.
PSYCH 625 MENTOR Education Your Life / psych625mentor.comkopiko27
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Week 1 Individual Assignment Basic Concepts in Statistics Worksheet
PSYCH 625 Week 1 Individual Assignment Reliability and Validity Matrix
PSYCH 625 Week 1 Individual Assignment Time to Practice – Week One
PSYCH 625 MENTOR Become Exceptional--psych625mentor.comshanaabe77
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
This document outlines a protocol for unpacking standards into learning targets to improve common assessments. It explains how to analyze standards by underlining verbs, highlighting nouns, circling contexts, and identifying the type of learning target. Teachers will learn how to determine the depth of knowledge ceiling for each target and match assessment item types to target rigor. Sample essential outcomes are unpacked using the protocol steps. Guidelines are provided for constructing response questions that clearly communicate expectations and assessments are written for a sample standard.
Statistics Assignment Help from the Statistics Assignment Experts. Statistics assignment help is the most common assignment that students are mostly demand for. Statistics is the branch of mathematics, comprises the collection, summarizing, analysis, interpretation, and presentation of data.
This document discusses measurement in research and provides examples and guidelines. It covers topics such as selecting observable events, assigning numbers or symbols to represent aspects of events, applying mapping rules, and different levels of measurement including nominal, ordinal, interval and ratio scales. Reliability and validity are important criteria for good measurement. The document also discusses sampling methods like probability and non-probability designs as well as factors to consider for determining sample size.
Homework 1
Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
La literatura realista europea surgió a mediados del siglo XIX como una reacción al romanticismo. Se caracterizó por una visión objetiva y detallada de la sociedad a través de descripciones precisas y un lenguaje sobrio. Algunos de sus principales representantes fueron Balzac, Flaubert y Zola en Francia, Dickens en Inglaterra, Dostoievsky y Tolstoi en Rusia, Galdós en España y Twain en Estados Unidos.
This document provides the syllabus and assignments for a business law course (BUS 311). The course is divided into 5 weekly modules. Each module includes discussion questions and assignments related to topics in business law, such as contracts, torts, employment law, and intellectual property. For example, Week 1 asks students to analyze a hypothetical legal case and discuss jurisdiction. Week 2 focuses on contract elements and unconscionability. Assignments include analyzing examples of contracts and writing a critical analysis paper. Later modules address additional topics like property, ethics, and international business law.
La hoja de vida presenta los datos personales de Erika Tamara Chiliquinga Guato, nacida el 3 de mayo de 1994 en Tena, incluyendo su número de cédula, dirección, teléfonos y correo electrónico. Detalla su formación académica primaria y secundaria, así como su título universitario de Contador Bachiller en Comercio y Administración obtenido en la Universidad Técnica de Ambato.
The document discusses Talkbits' use of Apache Cassandra in Amazon EC2, including deploying Cassandra across 3 availability zones with a replication factor of 3 for strong consistency, as well as performing periodic full backups, incremental backups on SSTable changes, and continuous transaction log backups to Amazon S3 using tools like TableSnap. It also covers Cassandra consistency options and semantics for different read and write quorum settings.
El documento describe un proyecto que estudia las actitudes de los maestros hacia la integración de estudiantes con discapacidades en las aulas de primaria. El proyecto aplicó instrumentos a maestros y estudiantes para analizar cuantitativamente las actitudes y si los maestros afrontan adecuadamente la discapacidad. El objetivo es mejorar la integración mediante el fomento de actitudes positivas.
El documento resume la persecución de comunistas en la Zona Militar de Comodoro Rivadavia entre 1944 y 1957. Explica que el Partido Comunista ganó influencia en el movimiento obrero en las décadas de 1930 y 1940 a través de la organización de sindicatos. Sin embargo, fueron perseguidos durante las presidencias de Perón debido a su alineamiento con la Unión Democrática y al enfoque de Perón de "nacionalizar" el movimiento obrero. El documento analiza los archivos policiales y periódicos de la ép
La doctora María Angeles Porpatto presenta en el XX Congreso Argentino de Hipertensión Arterial sobre las combinaciones fijas de medicamentos antihipertensivos. Señala que aunque los estudios clínicos muestran beneficios de ciertas asociaciones fijas, su aplicación en la práctica habitual puede ser limitada debido a factores económicos y de accesibilidad para pacientes. Concluye que aunque las combinaciones fijas podrían mejorar la adherencia al tratamiento, en atención primaria es difícil disponer de todas las opciones
El documento presenta información sobre diferentes técnicas de marketing y publicidad no convencional como el marketing promocional, el marketing directo, la publicidad en el punto de venta, el merchandising, tipos de establecimientos comerciales, el patrocinio, la participación en eventos, la diferencia entre publicidad y publicity, y los formatos utilizados para incluir noticias en los medios de comunicación.
Este documento describe los primeros auxilios básicos, incluyendo cómo medir los signos vitales como el pulso, la respiración, la temperatura y la presión arterial. Explica cómo controlar el pulso en diferentes partes del cuerpo y los rangos normales para cada grupo de edad. También cubre la respiración, temperatura y presión arterial, así como la reanimación cardiopulmonar.
Posterior triangle of neck - Powerpoint lecture notes by Dr.N.Mugunthan.mgmcri1234
The posterior triangle of the neck is bounded by the sternocleidomastoid muscle anteriorly and the trapezius muscle posteriorly. It is subdivided into the occipital and subclavian triangles by the omohyoid muscle. The posterior triangle contains nerves like the accessory nerve and branches of the brachial plexus, blood vessels like the external jugular vein and subclavian artery, and lymph nodes. Knowledge of the anatomy of the posterior triangle is important for procedures like brachial plexus blocks and catheterization of the external jugular vein.
This document provides an overview of zero liquid discharge (ZLD) processes for the pulp and paper industry. It discusses how ZLD helps industries reduce wastewater generation and reuse water, outlines the key steps in a ZLD system including membrane filtration and crystallization, and examines a case study of a successful ZLD pilot plant for an Indian paper mill that recovered 93.7% of waste and reduced TDS levels by 96%. The document also notes challenges like high energy costs, and looks at incentives to promote wider ZLD adoption.
Este documento presenta el diagnóstico de un problema en la Escuela Básica Primaria El Molinete, donde muchos estudiantes tienen dificultades con la lecto-escritura. El autor propone desarrollar un software educativo para apoyar el proceso de enseñanza-aprendizaje en estos estudiantes. El documento describe la comunidad donde se encuentra la escuela, identifica las debilidades de los estudiantes, y justifica cómo el software podría facilitar el aprendizaje de manera atractiva e interactiva para mejorar los resultados educat
This document summarizes a study that identified and characterized Drosophila Snurportin (dSNUP), the fruit fly ortholog of human Snurportin1 (SPN1). The key findings are:
1) dSNUP lacks an importin-β binding (IBB) domain that is essential for SPN1's interaction with importin-β in vertebrates.
2) dSNUP does not physically interact with the Drosophila importin-β ortholog Ketel, and fruit fly snRNPs also fail to bind Ketel.
3) The importin-7 ortholog Moleskin (Msk) physically associates with both dS
IDEASVOICE is an international collaboration platform that helps entrepreneurs, founders, and cofounders connect to build successful business ventures. It allows entrepreneurs to find partners with complementary skills to join their founding team or help develop an existing company. It also enables those looking to join a startup to find exciting projects and teams to contribute to. Members can promote their profiles and projects, search for opportunities, build their professional networks, and interact with other entrepreneurs through the platform. The goal is to facilitate partnerships and business ventures on a global scale.
The document is a newsletter from the Evidence Based Practices for Improving Quality (EPIQ) organization. It discusses several initiatives at the hospital to improve outcomes for infants born before 29 weeks gestation. Specifically:
- There have been zero cases of skin breakdown in very preterm infants since implementing new guidelines for umbilical cord/catheter insertion.
- Prophylactic indomethacin administration for infants under 27 weeks has decreased rates of patent ductus arteriosus and severe intraventricular hemorrhage.
- Increased rates of complete antenatal steroid courses have led to fewer composite outcomes like death, bronchopulmonary dysplasia, necrotizing enterocolitis,
Technology-based assessments-special education
New technologies remain competitive in driving efforts to make learning more efficient. Technology-based assessment in special education has made quite some advancement (Goldsmith & LeBlanc, 2004). First applications of computer technology assessment were for the scoring student's test forms. Currently, features incorporate self-administration, software control in presentation, response evaluation based on algorithms, prescription based on expert knowledge and direct links in assessment and change in instructions. The technology-based assessment uses electronic and software systems to evaluate individual children in an educational setting. Traditional assessments employ approaches of the computer.
Video-based computer assisted test enabled learning of language for the student automatically increasing the validity of measurements. Video segments incorporated movie elements of moral dilemma in problem-solving tests. Students viewing the video segments respond by simply touching the screen. Innovative approaches have created relevance in testing procedures. Misplaced students result into poor results and get prompted to drop out. Teachers not well trained contribute to the misplacement due to poor management of certain behaviors and learning differences. For effect, teachers must be able to analyze data produced by the assessment and develop a due course of action.
In addressing students with physical limitations use of voice recognition, handwriting interpreters, stylus tools, and touchscreen enables communication without the use of keys (Gierach, 2009). New software features allow students to perform comfortable pace of video segments on preferred language options. Computers are linked to videodisc enabling students to learn according to individual needs and skills. Latest technological features concern evaluation. Technological advancements assess social competence among students. The evaluator views students in a variety of context. Limitation in technology infrastructure, seen as the key barrier in this sort of assessment. Many district schools lack adequate high-speed broadband access necessary for this evaluation. Moreover, obsolesce in technology-based assessment erodes the capacity to provide quality services technology-based systems have a relatively short functional life.
Holistic assessments are the best in technology-based assessments. They incorporate software control in presentation, conceptual models or algorithms, decision-making based rules and expert knowledge (Redecker, & Johannessen, 2013). Proliferation technology helps students in the inclusion of speech recognition, electronic communication, personal computers, robotics and artificial intelligence. Trends in technology-based assessments have impacted lives of students with a disability. They achieve school improvement goals as well as tracking student growth and progress. Current assessment norms have embedded current stan ...
Case Study 2 SCADA WormProtecting the nation’s critical infra.docxwendolynhalbert
Case Study 2: SCADA Worm
Protecting the nation’s critical infrastructure is a major security challenge within the U.S. Likewise, the responsibility for protecting the nation’s critical infrastructure encompasses all sectors of government, including private sector cooperation. Search on the Internet for information on the SCADA Worm, such as the article located athttp://www.theregister.co.uk/2010/09/22/stuxnet_worm_weapon/.
Write a three to five (3-5) page paper in which you:
1. Describe the impact and the vulnerability of the SCADA / Stuxnet Worm on the critical infrastructure of the United States.
2. Describe the methods to mitigate the vulnerabilities, as they relate to the seven (7) domains.
3. Assess the levels of responsibility between government agencies and the private sector for mitigating threats and vulnerabilities to our critical infrastructure.
4. Assess the elements of an effective IT Security Policy Framework, and how these elements, if properly implemented, could prevent or mitigate and attack similar to the SCADA / Stuxnet Worm.
5. Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
· Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
· Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
· Identify the role of an information systems security (ISS) policy framework in overcoming business challenges.
· Compare and contrast the different methods, roles, responsibilities, and accountabilities of personnel, along with the governance and compliance of security policy framework.
· Describe the different ISS policies associated with the user domain.
· Analyze the different ISS policies associated with the IT infrastructure.
· Use technology and information resources to research issues in security strategy and policy formation.
· Write clearly and concisely about Information Systems Security Policy topics using proper writing mechanics and technical style conventions.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.1601.053573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 226.80.866315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.71.120313075513.61FB457.91.01657 ...
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docxpotmanandrea
Score:
Week 1.
Measurement and Description - chapters 1 and 2
<1 point>
1
Measurement issues.
Data, even numerically coded variables, can be one of 4 levels -
nominal, ordinal, interval, or ratio.
It is important to identify which level a variable is, as
this impact the kind of analysis we can do with the data.
For example, descriptive statistics
such as means can only be done on interval or ratio level data.
Please list under each label, the variables in our data set that belong in each group.
Nominal
Ordinal
Interval
Ratio
b.
For each variable that you did not call ratio, why did you make that decision?
<1 point>
2
The first step in analyzing data sets is to find some summary descriptive statistics for key variables.
For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.
You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions.
(the range must be found using the difference between the =max and =min functions with Fx) functions.
Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.
Salary
Compa
Age
Perf. Rat.
Service
Overall
Mean
Standard Deviation
Range
Female
Mean
Standard Deviation
.
MARKETING MANAGEMENT PHILOSOPHIES
CHAPTER 1 - ASSIGNMENT
Question 1.
Considering the differences of the philosophies, in some cases slight differences, select a company (product or service) and describe the current philosophy they pose for the customer. Include in your comments the level of customer value delivered by the company’s actions.
In other words, measure the company’s interaction with their customers against the Market Concept Philosophy. Does the company operate under the Market Concept Philosophy or do they lean more toward one of the other Philosophies.
Be specific with your examples.
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variabl ...
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51METhe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.0475752952204.50ME1341.81.0444030100214.70FC14251.08523329012161FA1522.60.983233280814.91FA1648.51.213404490405.70MC1763.11.1075727553131FE1836.21.1673131801115.60FB1923.91.039233285104.61MA2035.51.1443144701614.80FB2178.91.1786743951306.31MF2257.61.199484865613.81FD2322.20.964233665613.30FA2453.41.112483075913.80FD2523.61.0282341704040MA2622.30.971232295216.20FA2746.21.156403580703.91MC2874.41.111674495914.40FF2975.61.129675295505.40MF3047.50.9894845901804.30MD3122.90.995232960413.91FA3228.10.906312595405.60MB3363.71.117573590905.51ME3426.90.869312680204.91MB3522.70.987232390415.30FA3624.41.059232775314.30FA3723.81.034232295216.20FA3864.61.1335745951104.50ME3937.31.202312790615.50FB4023.71.031232490206.30MA4140.31.008402580504.30MC4224.41.0592332100815.71FA4372.31.0796742952015.50FF4465.91.1565745901605.21ME4549.91.040483695815.21FD4657.41.0075739752003.91ME47560.982573795505.51ME4868.11.1955734901115.31FE4966.21.1615741952106.60ME5061.71.0835738801204.60ME
Week 1Week 1: Descriptive Statistics, including ProbabilityWhile the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus onexamining the issue using the salary measure.The purpose of this assignmnent is two fold:1. Demonstrate mastery with Excel tools.2. Develop descriptive statistics to help examine the question.3. Interpret descriptive outcomesThe first issue in examining salary data to determine if we - as a company - are paying males and females equally for doing equal work is to develop somedescriptive statistics to give us something to make a preliminary decision on whether we have an issue or not.1Descriptive Statistics: Develop basic descriptive statistics for SalaryThe first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab t.
Running head Organization behaviorOrganization behavior 2.docxtoltonkendal
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Chi-square tests are great to show if distributions differ or i.docxMARRY7
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
This document discusses measurement scales and how to establish the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating scales and ranking scales are presented as two categories for developing attitudinal scales. The document emphasizes the importance of establishing the goodness of measures through item analysis and testing the reliability and validity of instruments.
This document discusses different types of scales used to measure variables in research: nominal, ordinal, interval, and ratio scales. It provides examples of each scale and explains their properties. Nominal scales categorize subjects into groups without rank. Ordinal scales rank order categories. Interval scales measure the distance between scale points. Ratio scales have a true zero point. The document also discusses developing scales through rating scales like Likert scales, which use numbered categories to indicate agreement, and ranking scales, which compare preferences.
Chp9 - Research Methods for Business By Authors Uma Sekaran and Roger BougieHassan Usman
This document discusses measurement scales and establishing the reliability and validity of measures. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are introduced as ways to develop measures using these scales. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the concepts they are intended to. Item analysis, reliability testing, and validity assessment are presented as key ways to evaluate the quality of developed measures.
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
See comments at the right of the data set.
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8
0
F
A
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A
14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A
ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating – Appraisal rating (Employee evaluation score)
26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender: ...
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes163.21.108573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.10.873315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.335.31.138313075513.61FB461.41.07857421001605.51METhe column labels in the table mean:546.90.9784836901605.71MDID – Employee sample number Salary – Salary in thousands 674.61.1136736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)740.81.0194032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.81.035233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise974.21.108674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.41.017233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1122.30.97123411001914.81FA1264.61.1345752952204.50ME1340.61.0164030100214.70FC14230.99823329012161FA1525.21.094233280814.91FA1645.71.143404490405.70MC1770.21.2315727553131FE1834.71.1193131801115.60FB1923.91.039233285104.61MA2033.51.0813144701614.80FB21711.0606743951306.31MF2252.91.103484865613.81FD2322.10.960233665613.30FA2456.81.183483075913.80FD2524.31.0562341704040MA2624.61.071232295216.20FA2743.41.084403580703.91MC28771.149674495914.40FF2974.71.115675295505.40MF3047.80.9954845901804.30MD3120.70.898232960413.91FA3228.60.921312595405.60MB3359.21.038573590905.51ME3427.30.881312680204.91MB3522.90.996232390415.30FA3622.70.987232775314.30FA3723.91.037232295216.20FA3864.71.1355745951104.50ME39351.128312790615.50FB4023.61.024232490206.30MA4146.61.166402580504.30MC4223.31.0152332100815.71FA4376.41.1406742952015.50FF4461.21.0745745901605.21ME45511.062483695815.21FD4658.81.0315739752003.91ME4766.91.174573795505.51ME4870.71.2405734901115.31FE4963.51.1145741952106.60ME5064.51.1325738801204.60ME
Week 1Week 1: Descriptive Statistics, including ProbabilityWhile the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus onexamining the issue using the salary measure.The purpose of this assignmnent is two fold:1. Demonstrate mastery with Excel tools.2. Develop descriptive statistics to help examine the question.3. Interpret descriptive outcomesThe first issue in examining salary data to determine if we - as a company - are paying males and females equally for doing equal work is to develop somedescriptive statistics to give us something to make a preliminary decision on whether we have an issue or not.1Descriptive Statistics: Develop basic descriptive statistics for SalaryThe first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab to co.
Data AnalysisResearch Report AssessmentBSBOllieShoresna
Data Analysis
Research Report Assessment
BSB123 Data Analysis
BSB123 Data Analysis
Notes on the Assessment
Covers Topics 1 – 10 i.e. descriptive statistics to Multiple Regression
Assignment is based around the international student recruitment industry looking specifically at students interested in postgraduate studies in USA
All 500 observations on spreadsheet are for international students
Variables are all related to factors which affect chance of being admitted and your job is to analyse this so that the company (GES) can advise future students about what to do and what their chances are of being admitted.
Report is split so that in each section you look at different aspects
You will need to do a summary incorporating elements of all of the parts to make recommendations.
Marks reflect (generally) the amount of work you need to do.
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
What am I looking for?
Can you select the correct technique / analysis to solve the question
Is that technique correctly and FULLY applied with calculations done correctly
E.g. in a hypothesis test, did you:
Correctly identify the test statistic (Z, T, F, χ2)
Did you include accurate hypotheses and decision rule which are consistent with each other
Were the calculations correct
Did you check to see if the assumptions or conditions of the test held
OR for Descriptive Statistics did you:
Consider all aspects of how you describe data and use the appropriate statistics to do that
Choose correct graph(s) for the type of data
Summarise the results to actually describe what you found – not just quote the stats.
Can you interpret the results – not just make a decision or complete a calculation.
Can you express the result in terms of the question and in a way which is understandable to your audience
In other words you will not get full marks unless you can correctly select the right approach to take for the data given, accurately and fully apply that analysis in a way which logically leads to a conclusion, make the conclusion in terms of the problem presented and then communicate that solution concisely and clearly
BSB123 Data Analysis
BSB123 Data Analysis
Examples from THA 4
H0: ≤ 700
H1: > 700
What is wrong with this?
BSB123 Data Analysis
BSB123 Data Analysis
Include title of analysis – t-Test: Two Sample Assuming Unequal Variances
5
Examples from THA 4
BSB123 Data Analysis
BSB123 Data Analysis
Look at t stat – all wrong – copied from somewhere – multiple students all getting it wrong
P and t test – do one
Used population terminology not sample
P-value – what is it?
6
Hypothesis Test
State the Hypotheses in terms of the parameter (µ,σ,p)
Identify the correct probability distribution (t, z, F, χ2)
Identify level of significance
State decision rule clearly
Use either test statistic method (i.e. in terms of t or z etc) or in terms of p-value. Don’t need to do both.
Decision rule must be consistent wit ...
Ashford 2: - Week 1 - Instructor Guidance
Week Overview:
The following video series: Against All Odds Inside Statistics is helpful if you would like to watch it.
http://www.learner.org/resources/series65.html?pop=yes&pid=3138
For this week, we’ll learn that statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions.
In today’s world, numerical information is everywhere. Statistical techniques are used to make decisions that affect our daily lives. The knowledge of statistical methods will help you understand how decisions are made and give you a better understanding of how they affect you. No matter what line of work you select, you will find yourself faced with decisions where an understanding of data analysis is helpful.
The concepts introduced this week include levels of measurement, measurements of center, variations, etc. Normal distribution and calculations are introduced in this week.
Measurements
You should be able to distinguish among the nominal, ordinal, interval, and ratio levels of measurement.
Nominal level - data that is classified into categories and cannot be arranged in any particular order.
EXAMPLES: eye color, gender, religious affiliation.
Ordinal level – data arranged in some order, but the differences between data values cannot be determined or are meaningless.
EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4.
Interval level - similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.
EXAMPLE: Temperature on the Fahrenheit scale.
Ratio level - the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.
EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer’s representatives per month.
Why do you need to know the level of measurement of a data? This is because the level of measurement of the data dictates the calculations that can be done to summarize and present the data. It also determines the statistical tests that should be performed on the data.
Probability
PROBABILITY is a value between zero and one, inclusive, describing the relative possibility (chance or likelihood) an event will occur.
There are three ways of assigning probability:
1. Classical Probability
This is based on the assumption that the outcomes of an experiment are equally likely.
2. Empirical Probability
The probability of an event happening is the fraction of the time similar events happened in the past.
Example: On February 1, 2003, the Space Shuttle Columbia exploded. This was the second disaster in 113 space missions for NASA. On the basis of this information, what is the probability that a future mission is successfully completed?
Probability of successful flight ...
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docxSANSKAR20
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)SERvice – Years of serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
BUSI 230Discussion Board Forum 1Project 2 InstructionsSta.docxRAHUL126667
BUSI 230
Discussion Board Forum 1/Project 2 Instructions
Standard Deviation and Outliers
Thread:
For this assignment, you will use the Project 2 Excel Spreadsheet to answer the questions below. In each question, use the spreadsheet to create the graphs as described and then answer the question.
Put all of your answers into a thread posted in Discussion Board Forum 1/Project 2.
This course utilizes the Post-First feature in all Discussion Board Forums. This means you will only be able to read and interact with your classmates’ threads after you have submitted your thread in response to the provided prompt. For additional information on Post-First, click here for a tutorial. This is intentional. You must use your own work for answers to Questions 1–5. If something happens that leads you to want to make a second post for any of your answers to Questions 1–5, you must get permission from your instructor.
1. A. Create a set of 5 points that are very close together and record the standard deviation. Next, add a sixth point that is far away from the original 5 and record the new standard deviation.
What is the impact of the new point on the standard deviation? Do not just give a numerical value for the change. Explain in sentence form what happened to the standard deviation. (4 points)
B. Create a data set with 8 points in it that has a mean of approximately 10 and a standard deviation of approximately 1. Use the second chart to create a second data set with 8 points that has a mean of approximately 10 and a standard deviation of approximately 4. What did you do differently to create the data set with the larger standard deviation? (4 points)
2. Go back to the spreadsheet and clear the data values from Question 1 from the data column and then put values matching the following data set into the data column for the first graph. (8 points)
50, 50, 50, 50, 50.
Notice that the standard deviation is 0. Explain why the standard deviation for this one is zero. Do not show the calculation. Explain in words why the standard deviation is zero when all of the points are the same. If you don’t know why, try doing the calculation by hand to see what is happening. If that does not make it clear, try doing a little research on standard deviation and see what it is measuring and then look again at the data set for this question.
3. Go back to the spreadsheet one last time and put each of the following three data sets into one of the graphs. Record what the standard deviation is for each data set and answer the questions below.
Data set 1:
0, 0, 0, 100, 100, 100
Data set 2:
0, 20, 40, 60, 80, 100
Data set 3:
0, 40, 45, 55, 60, 100
Note that all three data sets have a median of 50. Notice how spread out the points are in each data set and compare this to the standard deviations for the data sets. Describe the relationship you see between the amount of spread and the size of the standard deviation and explain why this connection exists. Do not give your calcu ...
1Create a correlation table for the variables in our data set. (Us.docxjeanettehully
1
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)
a. Interpret the results.
What variables seem to be important in seeing if we pay males and females equally for equal work?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample
(Mid,
age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Ha: The regression coefficient for each variable is significant
Sal
The analysis used Sal as the y (dependent variable) and
SUMMARY OUTPUT
mid, age, ees, sr, g, raise, and deg as the dependent
variables (entered as a range).
Regression Statistics
Multiple R
0.99215498
R Square
0.9843715
Adjusted R Square
0.98176675
Standard Error
2.59277631
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
7
17783.7
2540.52
377.914
8.44043E-36
Residual
42
282.345
6.72249
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4.009
3.775
-1.062
0.294
-11.627
3.609
-11.627
3.609
Mid
1.220
0.030
40.674
0.000
1.159
1.280
1.159
1.280
Age
0.029
0.067
0.439
0.663
-0.105
0.164
-0.105
0.164
EES
-0.096
0.047
-2.020
0.050
-0.191
0.000
-0.191
0.000
SR
-0.074
0.084
-0.876
0.386
-0.244
0.096
-0.244
0.096
G
2.552
0.847
3.012
0.004
0.842
4.261
0.842
4.261
Raise
0.834
0.643
1.299
0.201
-0.462
2.131
-0.462
2.131
Deg
1.002
0.744
1.347
0.185
-0.500
2.504
-0.500
2.504
Interpretation:
Do you reject or not reject the regression null hypothesis?
Do you reject or not reject the null hypothesis for each variable?
What is the regression equation, using only significant variables if any exist?
What does result tell us about equal pay for equal work for males and females?
3
Perform a regression analysis using compa as the dependent variable and the same independent
variables as used in question 2.
Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
4
Based on all of your results to date, is gender a factor in the pay practices of this company?
Why or why not?
Which is the best variable to use in analyzing pay practices - salary or compa?
Why?
.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
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Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
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Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Bus 308
1. BUS 308
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BUS 308 STATISTICS FOR MANAGERS ENTIRE
COURSE ( UPDATE COURSE SEPTEMBER 2013)
BUS 308 Week1
DQ 1
Language.
Numbers and measurements are the language of business..Organizations look at results,expenses,quality levels,
efficiencies,time,costs, etc.What measures does y our department keep track of ? How are the measurescollected,
and how are they summarized/described? How are they used in making decisions? (Note: If y ou donot have a job
wheremeasures areavailable toy ou,ask someone you know for some examples or conduct outside research on an
interest of y ours.)
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoof y our classmates by providing
recommendations for the measures being discussed.
DQ 2
Lev els.
Managers and professionals often pay more attention tothe levels of their measures (means,sums,etc.)than tot he
v ariation in the data (thedispersion or the probability patterns/distributions that describethe data).For the
measures you identified in Discussion 1,why must dispersion be considered totruly understandwhat the data is
telling us about what we measure/track? How can we make decisions about outcomes and results if we donot
understand the consistency (variation) of the data? Does looking at the variation in the data give us a different
understanding of results?
2. Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates by commenting on the
situations that are being illustrated.
Week 1 Assignment
Problem Set WeekOne.Allstatisticalcalculations willuse the EmployeeSalary Data set (in Appendix
section).
1 . Using the Excel Analysis ToolPakfunction Descriptive Statistics,generate descriptive statistics for the salary data.
Which variables does this function not work properly for,even though we have some generated results?
2. Sort the data by either thevariable G or GEN1 (intomales and females) and find the mean and standard deviation
for each gender for the following variables: SAL, COMPA,AGE, SR, and RAISE. Use Descriptive for one gender and
the fx functions (AVERAGE and STDEV)for the other.
3. What is the probability distribution table for:
a. A randomly selected person being a male in a specificgrade?
b. A randomly selected person being in a specificgrade?
4. Find:
a. The zscore for each malesalary,based on the male salary distribution.
b. The zscore for each female salary, based on thefemale salary distribution.
5. Repeat question 4 for COMPA for each gender.
6. What conclusions can you makeabout theissue of male and femalepay equality? Are all of the results consistent?
If not, why not?
For additional assistance with these calculations reference the Recommended Resources for Week One.
Week 2
DQ 1
t-Tests.
In looking at your business,when and why would you want touse a one-sample mean test (either zor t) or a
twosamplet-test? Create a nulland alternate hypothesis for one of these issues. How would you use the results?
3. Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates by commenting on the
potential differences in the results andhow that might affect decision making.
DQ 2
Variation.
Variation exists in virtually allparts of our lives. We often see v ariation in results in what we spend (utility costs each
month, food costs, business supplies,etc.).Consider the measures and data you use (in either your personal or job
activities).When are differences (between one time period andanother,between different production lines, etc.)
between average or actualresults important? How can you or your department decidewhether or not the variation is
important? How could using a mean difference test help?
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates and comment on the
use of the test.
Week 2 Assignment
Problem Set WeekTwo.Complete the problems below and submit your workin an Excel document.Be
sure toshow all of y our workandclearly label allcalculations. All statisticalcalculations will use the Employee Salary
Data set (in Appendix section).
Problems
1 . Is either that male or female salary equaltothe overall mean salary? (Twohypotheses, one-sampletests needed.)
2. Are maleand female averagesalaries statistically equaltoeach other?
3. Are themale and female compa averagemeasures equal toeach other?
4. If the salary and compa mean tests in questions 2 and 3 provide different equality results, which would be more
appropriatetouse in answering the question about salary equity? Why?
5. What other information would you like toknow toanswer thequestion about salary equity between the genders?
Why ?
Week 3
DQ 1
ANOVA.
4. In many ways,comparing multiple sample means is simply an extension of what we coveredlast week.What
situations exist where a multiple (more than two) group comparison would be appropriate?(Note: Situationscould
relate toyour work, homelife,socialgroups, etc.).Create a nulland alternate hypothesis for one of these issues. What
would theresults tell you?
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates by commenting on
why you agree or disagree with the statisticaltest that your peers have described as appropriate in this scenario.
DQ 2
Effect Size
Sev eral statisticaltests havea way tomeasure effect size. What is this,and when might you want touse it in looking at
results from these tests on job related data?
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoof y our classmates and…
Week 3 Assignment
Problem Set WeekThree. Complete the problems below and submit your work in an Excel document.
Be sure toshow allof y our workand clearly labelallcalculations.Allstatistical calculations willuse the Employee
Salary Data set (in Appendix section).
1 . Is the average salary the samefor each of the grade levels? (Assume equal variance, and use the Analy sis ToolPak
function ANOVA.)Set up the data input table/range touse as follows:
Put all of the salary values for each grade under theappropriate gradelabel.
A B C D E F
2. The factorial ANOVA with only twovariables can be done with theAnalysis ToolPa kfunction two-way ANOVA with
replication.Set up a data input table like the following:
Grade
Gender A B C D E F
M
F
For each empty cell,randomly pick a maleor female salary from each grade.Interpret theresults.Are the average
salaries for each gender (listedas sample) equal?Are the average salaries for each grade (listed as column)equal?
5. 3. Repeat question 2 for the compa values.
Grade
Gender A B C D E F
M
For each empty cellrandomly picka male or female compa from each grade.Interpret the results. Aretheaverage
compas for each gender (listed as sample)equal? Are theaverage compas for each grade (listed as column)equal?
4. Pick any other variable you are interested in and doa simple two-way ANOVA without replication.Why did you
pick this variable, and what dothe results show?
5. What are your conclusions about salary equity now?
Week 4
DQ 1
Confidence Intervals.
Earlier we discussed issues with looking at only a singlemeasure toassess job-related results.Looking backat the
data examples you have provided in the previous discussion questions on this issue,how might adding confidence
intervals help managers understand results better?
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates by commenting on
whether or not you think changing the confidence intervals willresult in a different outcome.Explain if you agree or
disagree with the role of a confidence intervalin the interpretation of the answer.
DQ 2
Chi-Square Tests.
Chi-square tests aregreat toshow if distributions differ or if twovariables interact in producing outcomes. What are
some examples of v ariables that you might want tocheck using the chi-square tests? What would these results tell
y ou?
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates by commenting on
how this information might be used tomakebusiness decisions.
Week 4 Assignment
Problem Set WeekFour.Let’s look at some other factors that might influencepay.Complete the
6. problems below and submit your work in an Excel document.Be suretoshow allof y our workand clearly labelall
calculations.Allstatisticalcalculations willuse the EmployeeSalary Data set (in Appendix section).
1 . Is the probability of having a graduate degree independent of the grade the employee is in?
2. Construct a 95% confidence interval on the mean service for each gender.Dothey intersect?
3. Are males andfemales distributed across grades in a similar pattern?
4. Do95% confidence intervals on the mean length of service for each gender intersect?
5. How do y ou interpret these results in light of our equity question?
Week 5
DQ 1
Correlation.
What results in your departments seem tobe correlated or related toother activities? How could you verify this?
Createa nulland alternatehypothesis for one of these issues. What are the managerial implications of a
correlation between these variables?
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates by
explaining whether or not you thinkthat there is a relationship between the variables discussed.
DQ 2
Regression.
At times we can generate a regression equation toexplain outcomes. For example,an employee’s salary can often be
explained by their pay grade,appraisal rating, education level, etc.What variables might explain or predict an
outcome in your department or life? If y ou generated a regression equation,how would you interpret it and the
residuals from it?
Guided Response: Rev iew several of y our classmates’ posts. Respond toat least twoclassmates by commenting on
how this information might be used tomakebusiness decisions.
Week 5
Final Paper
7. The finalassignment for this course is a FinalPaper.The purpose of the FinalPaper is for y ou toculminate the
learning achieved in the course by creating a sales report. The FinalPaper represents 25% of the ov erallcourse grade.
Writing the FinalPaper
Identify an issuein your life (work place,home,socialorganization,etc.)where a statisticalanalysis coul d be used to
help make a managerialdecision.Develop a sampling plan,an appropriate set of hypotheses,and an inferential
statistical procedure totest them.You donot need tocollect any data on this issue, but you will discuss what a
significant statisticaltest would mean and how you would relate this result tothe real-world issue you identified. Your
paper should be three tofive pages in length (excluding the cover and reference pages). In addition tothe text,utilize
at least three sources toto support your points. Noabstract is required.Use the following research plan format to
structurethe paper:
Step 1 : Identification of the problem
Describe what is known about the situation,why it is a concern,and what we donot know.
Step 2: Research Question
What exactly dowe want our study tofindout? This shouldnot be phrased as a yes/noquestion.
Step 3: Data collection
What data is needed toanswer the question,how willwe collect it, and how will wedecide how much we need?
Step 4: Data Analysis
Describe how you would analyze the data.Provide at least one hypothesis test (nulland alternate) and an associated
statistical test.
Step 5: Results andConclusions
Describe how you would interpret the results. For example,what wouldyou recommend if your nullhypothesis was
rejected and what would you doif the nullwas not rejected?
A quick example: Concern if gender is impacting employee’s pay. H0: Gender is not related topay.H1: Gender is
related topay. Approach: Multiple regression equation to see if gender impacts pay after considering thelegalfactors
of grade,appraisal,education,etc.If regression coefficient for gender is significant, will need tocreate residual list t o
see which employees show excessivevariation from predicted salaries when gender is not considered
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course-update-course-september-2013
BUS 308 WEEK 1 PROBLEM SET WEEK ONE
All statistical calculations will use the Employee Salary Data set (in Appendix section).
1 . Using the Excel Analysis ToolPakor StatPlus:macLE function descriptive statistics,generate and show the
descriptive statistics for each appropriate variable in the sample data set.
a. For which variables in thedata set does this function not workcorrectly for? Why?
2. Sort the data by Gen or Gen 1 (intomales and females) andfind the mean and standard deviation for each gender
for the following variables:
a. sal, compa, age, sr and raise.Use either the descriptive stats function or theFx functions (average and stdev).
3. What is the probability for a:
a. Randomly selectedperson being a male in grade E?
b. Randomly selected male being in grade E?
c. Why are the results different?
4. Find:
a. The zscore for each malesalary,based on only the male salaries.
b. The zscore for each female salary, based on only the female salaries.
c. The zscore for each female compa,based on only the female compa values
d. The zscore for each malecompa,basedon only the male compa values.
e. What dothe distributions and spread suggest about male and female salaries?
f. Why might wewant touse compa tomeasure salaries between males and females?
5. Based on this sample,what conclusions can you make about the issue of male and female pay equality?
6. Are all of the results consistent with your conclusion?If not, why not?
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BUS 308 WEEK 2 PROBLEM SET WEEK TWO
Problem Set WeekTwo.Complete the problems below and submit your workin an Excel document.Be sure toshow
all of y our work and clearly labelall calculations.Allstatistical calculations willuse the Employee Salary Data Set.
9. Included in theWeek Twotab of the EmployeeSalary Data Set are 2 one-sample t-tests comparing male and female
av erage salaries tothe overallsample mean.
1 . Based on our sample,how doy ou interpret theresults and what dothese results suggest about the population
means for male and female salaries?
2. Based on our sample results, perform a 2 -sample t-test tosee if the population male and female salaries could be
equal toeach other.
3. Based on our sample results, can the male and female compas in the population be equal toeach other? (Another 2-
sample t-test.)
4. What other information would you liketoknow toanswer the question about salary equity between the genders?
Why ?
5. If the salary and compa mean tests in questions 3 and 4 provide different results about male andfemale salary
equality, which would be more appropriate touse in answering thequestion about salary equity?Why? What are your
conclusions about equalpay at this point?
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BUS 308 WEEK 3 FINAL OUTLINE DRAFT
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BUS 308 WEEK 3 PROBLEM SET WEEK THREE
Problem Set WeekThree. Complete the problems below and submit your work in an Excel document.Be suretoshow
all of y our work and clearly labelall calculations.All statistical calculations willuse the Employee Salary Data set (in
Appendix section).
1. Based on the sample data,can theaverage(mean) salary in thepopulation be thesame for each of the grade
lev els? (Assumeequalvariance,and use the analysis toolpak or StatPlus:mac LE function ANOVA.)Set up
the input table/range touse as follows: Put allof the salary values for each grade under the appropriate
grade label.Be sure toinclude the null and alternate hypothesis along with the statisticaltest and result.
2. The table and analysis below demonstrate a 2 -way ANOVA with replication.Please interpret the results.
3. Using our sample results,can we say that the compa values in thepopulation are equal by grade and/or
gender,and areindependent of each factor?
4. Pick any other variable you are interested in and doa simple 2 -way ANOVA without replication. Why did
y ou pickthis variable andwhat dothe results show?
10. 5. Using the results for this week, What are your conclusions about gender equalpay for equal workat this
point
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BUS 308 WEEK 4 PROBLEM SET WEEK FOUR
Problem Set WeekFour. Let’s look at some other factors that might influencepay.Complete the
problems below and submit your work in an Excel document.Be suretoshow allof y our workand clearly labelall
calculations.Allstatisticalcalculations willuse the EmployeeSalary Data set (in Appendix section).
1. How do y ou interpret these results in light of our equity question?One question wemight haveis if the
distribution of graduate and undergraduatedegrees independent of the grade theemployee? (Note:this is
the sameas asking if the degrees are distributed thesame way.)Based on theanalysis of our sample data
(shown below),what is your answer?
2. Using our sample data,we can construct a 95% confidence interval for the population’s mean salary for
each gender.Interpret the results. How dothey compare with the findings in the week2 one sample t -test
outcomes (Question 1 )?
3. Based on our sample data,can weconclude that males and females are distributed across grades in a
similar pattern within the population?
4. Using our sample data,construct a 95% confidence interval for the population’s mean servicedifference
for each gender.Dothey intersect or ov erlap? How dothese results comparetothe findings in week2,
question 2?
5. How do y ou interpret these results in light of our question about equalpay for equalwork?
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BUS 308 WEEK 5 FINAL PAPER
The finalassignment for this course is a FinalPaper.The purpose of the FinalPaper is for y ou toculminate the
learning achieved in the course by creating a sales report. The FinalPaper represents 25% of the ov erallcourse grade.
Writing the FinalPaper
Identify an issuein your life (work place,home,socialorganization,etc.)where a statisticalanalysis could be used to
help make a managerialdecision.Develop a sampling plan,an appropriate set of hypotheses,and an inferential
statistical procedure totest them.You donot need tocollect any data on this issue, but you will discuss what a
significant statisticaltest would mean and how you would relate this result tothe real-world issue you identified. Your
paper should be three tofive pages in length (excluding the cover and reference pages). In addition tothe text,utilize
11. at least three sources totosupport your points. Noabstract is required.Use the following research plan format to
structurethe paper:
Step 1 : Identification of the problem
Describe what is known about the situation,why it is a concern,and what we donot know.
Step 2: Research Question
What exactly dowe want our study tofindout? This shouldnot be phrased as a yes/noquestion.
Step 3: Data collection
What data is needed toanswer the question,how willwe collect it, and how will wedecide how much we need?
Step 4: Data Analysis
Describe how you would analyze the data.Provide at least one hypothesis test (nulland alternate) and an associated
statistical test.
Step 5: Results andConclusions
Describe how you would interpret the results. For example,what wouldyou recommend if your nullhypothesis was
rejected and what would you doif the nullwas not rejected?
A quick example: Concern if gender is impacting employee’s pay. H0: Gender is not related topay.H1: Gender is
related topay. Approach: Multiple regression equation tosee if gender impacts pay after considering thelegalfactors
of grade,appraisal,education,etc.If regression coefficient for gender is significant, will need tocreate residual list to
see which employees show excessivevariation from predicted salaries when gender is not considered
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