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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 .
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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 ...
MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx
MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx
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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: ...
Chi-square tests are great to show if distributions differ or i.docx
Chi-square tests are great to show if distributions differ or i.docx
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Similar to Lecture7b Applied Econometrics and Economic Modeling
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 .
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
simonithomas47935
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 ...
MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx
MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx
infantsuk
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: ...
Chi-square tests are great to show if distributions differ or i.docx
Chi-square tests are great to show if distributions differ or i.docx
MARRY7
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 ...
Running head Organization behaviorOrganization behavior 2.docx
Running head Organization behaviorOrganization behavior 2.docx
toltonkendal
BUSI 620 Questions for Critical Thinking 3 Salvatore’s Chapter 6: a. Discussion Questions: 1, 7, and 15. b. Problems: 7 and appendix problems 1 and 3 (pp. 256–257). Note: 1. Revised P7: Just construct the diffusion index from month 2 to 3. In this problem, we have three leading indicators. The diffusion index from month 1 to 2 is 66.7 (=2/3) because two indicators move up and move down (see p. 236). 2. Appendix problem 1: Delete “Eliminating the data for 2000.” You need to calculate the moving average forecasts and RMSEs for year 2000, not the whole data period. 3. Appendix problem 3: Compare RMSEs for moving average and exponential forecasts to answer “Is this a better forecast than the moving average” (see also p. 234)? Use 166.63, the mean of all 36 months, as the initial forecast for Jan. 1998 for both exponential smoothing forecasts. Salvatore’s Chapter 7: a. Discussion Questions: 3, 11, and 12. b. Problems: 4, 12, and 13. Note: 1. P4: Ms. Smith should hire workers as long as their marginal revenue product (MRP) exceeds their marginal resource cost (MRC) and until MRP=MRC. MRP=MR x MP = P x MP = $10 x MP (use information in the problem to calculate MP). MRC=wages=$40. 2. P12(a): Calculate Q when L=1and K=1, and L=2 and K=2. Then compare and answer the question about the returns to scale. 3. P12(b): Given K=1, show the change in Q if L changes from 1 to 2 and 2 to 3. Answer the question about diminishing returns. 4. P13(a): See figure (7-4) on page 276. 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 midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91 ...
BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
humphrieskalyn
ww w. te x- ce te ra .co m CHAPTER 9 Compensation Management 337 Figure 9-5 Point Manual Description of “Responsibility: Equipment and Materials” 1. Responsibility … b. Equipment and Materials. Each employee is responsible for conserving the company’s equipment and materials. This includes reporting malfunctioning equipment or defective materials, keeping equipment and materials cleaned or in proper order, and maintaining, repairing, or modifying equipment and materials according to individual job duties. The company recognizes that the degree of responsibility for equipment and materials varies widely throughout the organization. Level I. Employee reports malfunctioning equipment or defective materials to immediate superior. Level II. Employee maintains the appearance of equipment or order of materials and has responsibility for the security of such equipment or materials. Level III. Employee performs preventive maintenance and repairs on equipment or corrects defects in materials. Level IV. Employee makes replacement and purchasing decisions and is in control of the “equipment and materials” factor. Wage and Salary Surveys All job evaluation techniques result in a ranking of jobs based upon their perceived relative worth. This assures internal equity; that is, jobs that are worth more will be paid more. But how much should be paid? What constitutes external equity? To determine a fair rate of compensation, most firms rely on wage and salary surveys, which discover what other employers in the same labour market are paying for specific key jobs. The labour market—the area from which the employer recruits—is generally the local community; how- ever, firms may have to compete for some workers in a wider market. Consider how the president of one large university viewed the market: Our labour market depends on the type of position we are trying to fill. For the hourly paid jobs such as janitor, data entry clerk, and secretary, the labour market is the surrounding metropolitan community. LO2 internal equity Perceived equity of a pay system in an organization. external equity Perceived fairness in pay relative to what other employers are paying for the same type of work. wage and salary surveys Studies made of wages and salaries paid by other organizations within the employer’s labour market. SpOTlIGHT On ETHICS Job evaluation You are the human resource director of a large grocery chain. As part of a restructuring of its compensation system, and to comply with pay equity legislation, the company has recently switched from the job ranking system to the point system. You are chairing the Job Evaluation Committee, which is ready to allocate points to the cashier job category, the largest category in the company. The discussion so far has focused on how many points to allocate to the responsibility factor, and the committee is essentially split 50–50 on the numbers. As it so happened, there .
www.tex-cetera.com CHAPTER 9 Compe.docx
www.tex-cetera.com CHAPTER 9 Compe.docx
ericbrooks84875
Final Exam Due Friday, Week Eight Instructions: Each response is worth a maximum of 50 points. Number and state the question. Space and then give your response. Each response will be a minimum 175 and maximum 225 words. Utilize the book, at least one resource beyond the course book and your personal examples in each response. This means that beyond the course text you should have a minimum of 4 references for this midterm exam. Create appropriate headings/subheading for each response and then give your detailed answer to the question. Use Word doc. or docx only. Times New Roman, 12 font and double-space everything. Include a cover page and reference page. Return exam via email attachment NLT. Follow APA 6th ed. formatting requirements. NO LATE EXAMS WILL BE ACCEPTED! 1. In determining rates of pay, how are the decisions made as to who gets paid $7.00 per hour and who will receive $1 million for a years work? 2. How can team effort result in improved organizational profitability? 3. What is reverse discrimination and how does it influence the design of an executive compensation plan? 4. With benefits consuming approximately 40 percent of the compensation dollar of most organizations, among many managers the “fringe benefit” concept still exists. Develop a detailed outline of the various components of a typically benefits program, indicating their importance and scope. Develop the outline so that it can be used for a presentation. (This response length may vary, so ensure you meet the requirements as stated.) 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 midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.87 ...
Final Exam Due Friday, Week EightInstructions Each response is.docx
Final Exam Due Friday, Week EightInstructions Each response is.docx
mydrynan
Math 009 Final Examination Spring, 2015 1 Answer Sheet Math 009 Introductory Algebra Name______________________________ Final Examination: Spring, 2015 Instructor __________________________ Answer Sheet Instructions: This is an open-book exam. You may refer to your text and other course materials as you work on the exam and you may use a calculator. Record your answers and show your work on this document. You must show your work to receive credit: answers given with no work shown will not receive credit. You may type your work using plain-text formatting or an equation editor, or you may hand-write your work and scan it. If you choose to scan your work, note that most scanners have a setting that will allow you to create one PDF document from all of the pages of your Answer Sheet – please make use of this option if it is available on your scanner. Whether you type your work or write it by hand, show work neatly and correctly, following standard mathematical conventions. Each step should follow clearly and completely from the previous step. If necessary, you may attach extra pages. You must complete the exam individually. Neither collaboration nor consultation with others is allowed. Your exam will receive a zero grade unless you complete the following honor statement. Please sign (or type) your name below the following honor statement: I have completed this final examination myself, working independently and not consulting anyone except the instructor. I have neither given nor received help on this final examination. Name _______________ Date___________________ Math 009 Final Examination Spring, 2015 2 Answer Sheet Problem Number Solution 1 WORK: ANSWER: 2 WORK: ANSWER: 3 WORK: ANSWER: 4 WORK: ANSWER: Math 009 Final Examination Spring, 2015 3 Answer Sheet 5 WORK: ANSWER: 6 WORK: ANSWER: 7 WORK: ANSWER: 8 WORK: ANSWER: Math 009 Final Examination Spring, 2015 4 Answer Sheet 9 WORK: ANSWER: 10 WORK: ANSWER: 11 WORK: ANSWER: 12 WORK: ANSWER: Math 009 Final Examination Spring, 2015 5 Answer Sheet 13 WORK: ANSWER: 14 WORK: ANSWER: 15 WORK: ANSWER: 16 WORK: ANSWER: Math 009 Final Examination Spring, 2015 6 Answer Sheet 17 WORK: ANSWER: 18 WORK: ANSWER: 19 WORK: ANSWER: 20 WORK: ANSWER: Math 009 Final Examination Spring, 2015 7 Answer Sheet 21 WORK: ANSWER: 22 WORK: ANSWE ...
Math 009 Final Examination Spring, 2015 1 Answer Sheet M.docx
Math 009 Final Examination Spring, 2015 1 Answer Sheet M.docx
andreecapon
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 ...
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
SANSKAR20
Week 3 Lecture 11 Regression Analysis Regression analysis is the development of an equation that shows the impact of the independent variables (the inputs we can generally control) on the output result. While the mathematical language may sound strange, most of you are quite familiar with regression like instructions and use them quite regularly. To make a cake, we take 1 box mix, add 1¼ cups of water, ½ cup of oil, and 3 eggs. All of this is combined and cooked. The recipe is an example of a regression equation. The output (or result or dependent variable) is the cake, the inputs (or independent variables) are the inputs used. Each input is accompanied by a coefficient (AKA weight or amount) that tells us how “much” of the variable is “used” or weighted into the outcome. So, in an equation format, this cake recipe might look like: Y = 1X1 + 1.25X2 + .5X3 + 3X4 where: Y = cake X1 = box mix X2 = cups of water X3 = cups of oil X4 = an egg. Of course, for the cake, the recipe needs to go through the cooking process; while for other regression equations the outputs need to go through whatever “process” turns the inputs into the output – this is often called “life.” Example With a regression analysis, we can identify what factors influence an outcome. So, with our Salary issue, the natural question to help us answer our research question of do males and females get equal pay for equal work would be: what factors influence or explain an individual’s pay? This is a perfect question for a multi-variate regression. Multi-variate simply means we have multiple input variables with a single output variable (Lind, Marchel, & Wathen, 2008). Variables. A regression analysis uses two distinct types of data. The first are variables that are at least interval level or better (the same as the other techniques we have used so far). The other is called a dummy variable, a variable that can be coded 0 or 1 indicating the presence of some characteristic. In our data set, we have two variables that can be used as dummy coded variables in a regression, Degree and Gender; both coded 0 or 1. In the case of Degree, the 0 stands for having a bachelor’s degree and the 1 stands for having an advanced degree. For Gender, 0 means a male and 1 means a female. How these are interpreted in a regression output will be discussed below. For now, the significance of dummy coding is that it allows us to include nominal or ordinal data in our analysis. Excel Approach. For our question of what factors influence pay, we will use Excel’s Regression function found in the Data Analysis section. This function will produce two output tables of interest. The first table tests to see if the entire regression equation is statistically significant; that is, do the input variables significantly impact the output variable. If so, we would then examine the second table – the coefficients used in a regression equation for e.
Week 3 Lecture 11 Regression Analysis Regression analy.docx
Week 3 Lecture 11 Regression Analysis Regression analy.docx
cockekeshia
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? .
1Create a correlation table for the variables in our data set. (Us.docx
1Create a correlation table for the variables in our data set. (Us.docx
jeanettehully
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe 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)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME 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, .
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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Bus 308 http://www.homeworkmotivator.com/products/bus-308?pagesize=12
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1 Running Head: NURSING PROFESSIONALISM 2 NURSING PROFESSIONALISM Nursing Professionalism Name Institution Introduction Generally, professionalism can be defined as the standard of behavior members of a certain profession are expected to display and this conduct is driven by the profession’s goals and qualities. Nurses and other health care providers portray professionalism by making sure they adhere to the set regulations, principles and standards of clinical practices in their attitudes, knowledge and behavior (Michiko et al., 2014, 579). Studies involving professionalism in the field of nursing have revealed that patients under the care of a nurse displaying professionalism have a higher chance of surviving than those under the care of a less professional nurse which serves as evidence for the importance of professionalism in nursing. Professionalism and Education Various surveys regarding nursing professionalism have been done and in a recent one, nurses in Japan scored a mean of 6.74 while those in Turkey had a much higher score of 16.7. The main variable in the set of nurses involved in the study was that only 43.5% of the Japanese nurses had a baccalaureate or a higher degree education level as compared to Turkey’s 79.5%. This validates previous findings that have concluded that professionalism in nursing or any other discipline increases with the level of education of the practitioner of the specific profession. As mentioned above, professionalism is positively correlated with education level but the importance of this has been quantified by studies. It has been shown that increasing by 10% the number of nurses holding a bachelors degree or higher in a hospital causes a 5% decrease in the chance that an admitted patient will die within one month of admission (Michiko et al., 2014, 584). It also significantly decreases the likelihood of a patient dying due to sudden life threatening complications. High education levels combined with nursing experience are therefore vital in increasing professionalism. Improving Professionalism One of the ways to improve professionalism in the nursing practice is by improving working conditions. With Japan as an example, nurses work for long hours and have mandatory night shifts without receiving adequate compensation which has led to a high turnover rate. The low retention rate implies that nurses do not gain professionalism resulting from experience. Hence, it is imperative that health care institutions provide nurses with a work environment that is conducive for professional growth. The importance of education for professionalism has been clearly es ...
1Running Head NURSING PROFESSIONALISM2NURSING PROFESSIONALI.docx
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Score: Week 5 Correlation and Regression <1 point> 1. Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.) a. Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)? b. Place table here (C8): c. Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are significantly related to Salary? To compa? d. Looking at the above correlations - both significant or not - are there any surprises -by that I mean any relationships you expected to be meaningful and are not and vice-versa? e. Does this help us answer our equal pay for equal work question? <1 point> 2 Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, age, performance rating, service, gender, and degree 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.) Plase interpret the findings. Ho: The regression equation is not significant. Ha: The regression equation is significant. Ho: The regression coefficient for each variable is not significant Note: technically we have one for each input variable. Ha: The regression coefficient for each variable is significant Listing it this way to save space. Sal SUMMARY OUTPUT Regression Statistics Multiple R 0.9915591 R Square 0.9831894 Adjusted R Square 0.9808437 Standard Error 2.6575926 Observations 50 ANOVA df SS MS F Significance F Regression 6 17762.3 2960.38 419.1516 1.812E-36 Residual 43 303.7003 7.0628 Total 49 18066 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -1.749621 3.618368 -0.4835 0.631166 -9.046755 5.5475126 -9.04675504 5.54751262 Midpoint 1.2167011 0.031902 38.1383 8.66E-35 1.1523638 1.2810383 1.152363828 1.28103827 Age -0.004628 0.065197 -0.071 0.943739 -0.136111 0.1268547 -0.13611072 0.1268547 Performace Rating -0.056596 0.034495 -1.6407 0.108153 -0.126162 0.0129695 -0.12616237 0.01296949 Service -0.0425 0.084337 -0.5039 0.616879 -0.212582 0.1275814 -0.2125.
ScoreWeek 5 Correlation and Regressio.docx
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BUS 308 Week 2 Lecture 2 Statistical Testing for Differences – Part 1 After reading this lecture, the student should know: 1. How statistical distributions are used in hypothesis testing. 2. How to interpret the F test (both options) produced by Excel 3. How to interpret the T-test produced by Excel Overview Lecture 1 introduced the logic of statistical testing using the hypothesis testing procedure. It also mentioned that we will be looking at two different tests this week. The t-test is used to determine if means differ, from either a standard or claim or from another group. The F-test is used to examine variance differences between groups. This lecture starts by looking at statistical distributions – they underline the entire statistical testing approach. They are kind of like the detective’s base belief that crimes are committed for only a couple of reasons – money, vengeance, or love. The statistical distribution that underlies each test assumes that statistical measures (such as the F value when comparing variances and the t value when looking at means) follow a particular pattern, and this can be used to make decisions. While the underlying distributions differ for the different tests we will be looking at throughout the course, they all have some basic similarities that allow us to examine the t distribution and extrapolate from it to interpreting results based on other distributions. Distributions. The basic logic for all statistical tests: If the null hypothesis claim is correct, then the distribution of the statistical outcome will be distributed around a central value, and larger and smaller values will be increasingly rare. At some point (and we define this as our alpha value), we can say that the likelihood of getting a difference this large is extremely unlikely and we will say that our results do not seem to come from a population that matches the claims of the null hypothesis. Note that this logic has several key elements: 1. The test is based on an assumption that the null hypothesis is correct. This gives us a starting point, even if later proven wrong. 2. All sample results are turned into a statistic that matches the test selected (for example, the F statistic when using the F-test, or the t-statistic when using the T-test.) 3. The calculated statistic is compared to a related statistical distribution to see how likely an outcome we have. 4. The larger the test statistic, the more unlikely it is that the result matches or comes from the population described by the null hypothesis claim. We will demonstrate these ideas by looking at the questions being asked in this week’s homework. We will show results of the related Excel tests, and discuss how to interpret the output. We need to remember that seeing different value (mean, variance, etc.) from different samples does not tell us that the population parameters we are estimating are, in fact, different. The ...
BUS 308 Week 2 Lecture 2 Statistical Testing for Differenc.docx
BUS 308 Week 2 Lecture 2 Statistical Testing for Differenc.docx
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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 ...
Case Study 2 SCADA WormProtecting the nation’s critical infra.docx
Case Study 2 SCADA WormProtecting the nation’s critical infra.docx
wendolynhalbert
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe 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)? 2270.870315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB4661.15757421001605.51METhe column labels in the table mean:5470.9794836901605.71MDID – Employee sample number Sal – Salary in thousands 6761.1346736701204.51MFAge – Age in yearsEES – Appraisal rating (Employee evaluation score)7411.0254032100815.71FCSER – Years of serviceG – Gender (0 = male, 1 = female) 8231.000233290915.81FAMid – salary grade midpoint Raise – percent of last raise9771.149674910010041MFGrade – job/pay gradeDeg (0= BS\BA 1 = MS)10220.956233080714.71FAGen1 (Male or Female)Compa - salary divided by midpoint, a measure of salary that removes the impact of grade11231.00023411001914.81FA12601.0525752952204.50METhis data should be treated as a sample of employees taken from a company that has about 1,000 13421.0504030100214.70FCemployees using a random sampling approach.14241.04323329012161FA15241.043233280814.91FA16471.175404490405.70MCMac Users: The homework in this course assumes students have Windows Excel, and17691.2105727553131FEcan load the Analysis ToolPak into their version of Excel.18361.1613131801115.60FBThe analysis tool pak has been removed from Excel for Windows, but a free third-party 19241.043233285104.61MAtool that can be used (found on an answers Microsoft site) is:20341.0963144701614.80FBhttp://www.analystsoft.com/en/products/statplusmacle21761.1346743951306.31MFLike the Microsoft site, I make cannot guarantee the program, but do know that 22571.187484865613.81FDStatplus is a respected statistical package.You may use other approaches or tools23231.000233665613.30FAas desired to complete the assignments.24501.041483075913.80FD25241.0432341704040MA26241.043232295216.20FA27401.000403580703.91MC28751.119674495914.40FF29721.074675295505.40MF30491.0204845901804.30MD31241.043232960413.91FA32280.903312595405.60MB33641.122573590905.51ME34280.903312680204.91MB35241.043232390415.30FA36231.000232775314.30FA37220.956232295216.20FA38560.9825745951104.50ME39351.129312790615.50FB40251.086232490206.30MA41431.075402580504.30MC42241.0432332100815.71FA43771.1496742952015.50FF44601.0525745901605.21ME45551.145483695815.21FD46651.1405739752003.91ME47621.087573795505.51ME48651.1405734901115.31FE49601.0525741952106.60ME50661.1575738801204.60MEhttp://www.analystsoft.com/en/products/statplusmacle Week 1Week 1.Describing the data.<Use right click on the row numbers at the left to insert rows below each question for your results and comments.>1Using the Excel Analysis ToolPak function descriptive statistics, generate and show the descriptive statistics for each appropriate variable in the sample data set.a. For which variables in the data set does this function not work correctly for? W.
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Lecture7b Applied Econometrics and Economic Modeling
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Modeling Possibilities
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Nonparallel Female and
Male Salary Lines
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Modeling Possibilities
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Regression Output with
Log_Salary as Response Variable
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Modeling Possibilities
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Data for Electric
Power
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Residuals from a
Straight-Line Fit
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Regression Output with
Squared Term Included
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Quadratic Fit Scatterplot
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Logarithmic Fit Scatterplot
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Modeling Possibilities
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Car Demand Data
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Regression Output for
Multiplicative Relationship
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Modeling Possibilities
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Scatterplot of Log
Variables with Linear Trend Superimposed
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Using the Learning
Curve Model for Predications
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