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]]>For each of the 5 questions, provide the following:
List the research question
Identify the variables presented in the question and describe each variable’s scale of measurement (nominal, ordinal, interval, or ratio) and characteristics (i.e., discrete vs. continuous, qualitative vs. categorical, etc.)
Provide an operational definition for each variable
The paper should be APA formatted but can include listings, though full sentences should be used within these listings. Be sure to appropriately identify each component for each question.
Description: This written assignment is based on the work conducted in the “Creating Research Questions” discussion forum. Based on this initial work, feedback received, and additional research, students should submit a final version of at least 5 unique research questions. Students are encouraged to use a wide range of variables, in terms of number and types, and to make sure the questions cover a range of focus, such as exploring relationships between variables, making predictions for one variable using one or more other variables, and determining differences between groups across one or two variables. For each of the 5 questions, provide the following: • List the research question • Identify the variables presented in the question and describe each variable’s scale of measurement (nominal, ordinal, interval, or ratio) and characteristics (i.e., discrete vs. continuous, qualitative vs. categorical, etc.) • Provide an operational definition for each variable The paper should be APA formatted but can include listings, though full sentences should be used within these listings. Be sure to appropriately identify each component for each question. Students should submit their paper to the Dropbox by Day 7.
Total Possible Score: 7.00
5 appropriate and correctly formatted research questions are presented.
Total: 2.00
Clearly and accurately describes 5 appropriate research questions that are specific, measurable, and correctly formatted.
Clearly and accurately describes 5 appropriate research questions that are mostly specific, measurable, and correctly formatted.
Adequately describes 5 appropriate research questions that are mostly specific, measurable, and correctly formatted.
Minimally describes 5 research questions that are lacking in appropriateness, specificity, measurability, or correct format.
Does not describe 5 research questions that are appropriate, specific, measureable, and correctly formatted.
All variables evident in each research question are correctly identified, with each variable’s scale of measurement and characteristics correctly identified.
Total: 2.00
Clearly and accurately describes all variables in each research question, including each variable’s correct characteristics and scale of measurement.
Clearly and accurately describes all variables in each research question, including most of the variables’ correct characteristics and scale of measurement.
Adequately describes all variables in each research question, including adequate characteristics and scale of measurement for each variable.
Minimally describes some of the variables in each research question, with adequate descriptions of the characteristics and scale of measurement for each variable.
Minimally describes some of the variables in each research question, with adequate descriptions of the characteristics and scale of measurement for each variable.
Appropriate operational definitions are provided for all variables for each research question.
Total: 1.00
Clearly and accurately describes the operational definition for each variable in all of the research questions.
Accurately describes the operational definition for each variable in all of the research questions.
Adequately describes the operational definition for each variable in all of the research questions.
Minimally describes the operational definition for each variable in all of the research questions.
Does not describe the operational definition for each variable in all of the research questions.
Writing and Organization
Total: 1.00
Demonstrates exemplary clarity and organization. The paper contains a wellarticulated thesis statement, flawless mechanics, and precise APA formatting.
Demonstrates effective clarity and organization. The paper contains a wellarticulated thesis statement, proper mechanics, and correct APA formatting.
Demonstrates adequate clarity and organization. The paper contains a clear thesis statement, adequate mechanics, and mostly correct APA formatting.
Demonstrates some clarity and organization. The paper contains an unclear thesis statement, poor mechanics, and improper APA formatting.
Lacking clarity and organization. The paper lacks a thesis statement, effective mechanics, and proper APA formatting.
Research
Total: 1.00
Demonstrates exemplary critical analysis of the research materials. The student comprehensively uses varied, scholarly, relevant, and current resources to inform analysis, evaluation, problemsolving, and decisionmaking.
Demonstrates effective critical analysis of the research materials. The student fully uses varied, scholarly, relevant, and current resources to inform analysis, evaluation, problemsolving, and decisionmaking.
Demonstrates adequate critical analysis of the research materials. The student adequately uses varied, scholarly, relevant, and current resources to inform analysis, evaluation, problemsolving, and decisionmaking.
Demonstrates some critical analysis of the research materials. The student partially uses varied, scholarly, relevant, and current resources to inform analysis, evaluation, problemsolving, and decisionmaking.
Lacking critical analysis of the research materials. The student fails to use varied, scholarly, relevant, and current resources to inform analysis, evaluation, problemsolving, and decisionmaking.
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]]>The U.S governors are selected at random without replacement
Let R represent republican and D represent democratic
P (R1&D1) =P(R1)× (D2/R1)
P (R1) =30/50
P (D2&R1) = 20/49
P (R1 &D1) = P(R1)×(D2&R1)= 30/50×20/49= 0.244
Therefore, the probability that first is republican and the second is a democrat is o.244.
P (R1&R2) = P(R1)× (R2/R1)
P (R1) =30/50
P (R2&R1) = 29/49
P (R1 &R2) = P(R1)×(R2&R1)= 30/50×29/49= 0.355
The probability that both are republican is 0.355
29/49 R2(R1&R2)= 30/50×29/40= 0.355
R1 20/49 D2 (R1&D2)30/50×20/49= 0.244
30/50
30/49 R2(D1&R2)= 20/50 ×30/49=0.244
20/50
D1 19/49 R2 D1&D2)=20/50×19/49=0.244
P(R1&R2)+ P(D1&D2)= 0.355+0.155=0.510
P(R1&D2)+ P(D1&D2)= 0.244+0.244=0.490
4.186
a. Find P(C1)
P(C1)= 9.3/61.4= 0.151
b. Find P(C1/S2)
P(C1/S2)= 13/25.8= 0.050
c. Are events C1 and S2 independent? Explain your answer
If P(C1/S2)= P(C1) then events are independent, because 0.050≠0.151 events are dependent
d. Is the events that an injured person is male independent of the event that an injured person was hurt at home? Explain your answer.
Male injured person= 35.6
Home injured person= 21.4
P(C2)= 21.4/61.4= 0.348
P(S1)=35.6/61.4=0.579
If P(C2&S2)= P(C2) the events are independent, but because 0.348×0.579≠ 0.348 events are independent.
4.188
a. find the probability P(G1), P(F1), AND P(G1&F1).
P(G1)= 0.419/1= 0.419
P(F1)=0.582/1= 0.582
P(G1&F1)=0.300/1=0.300
b. Use the special multiplication rule to determine whether event G1 and F1 are independent
If P(G1&F1)=P(G1)×P(F1) when the events are independent, but substituting the values in a. into the equation we see that 0.300±0.419×0.582, so events are depend.
Reference
Delucchi, M. (2006). The efficacy of collaborative learning groups in an understanding statistics course. College teaching, 54, 244248.
Muth, J. E. (2006). Basic Statistics and Pharmaceutical Statistical Application (Vol. 2). New York: Chapman & Hall. CRC Press.
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]]>01: The paper makes use of statistical methods in identification of a solution for the NYke Shoe Company. The problem is to choose a shoe size regardless of the gender and height. The paper considers the appropriate statistical methods to solve the problem and is giving the recommendations based on the results.
Contents
Table 1: Descriptive statistics for Shoe Size and Height 5
Table 2: Descriptive statistics for Gender. 5
Table 3: Regression analysis considering Shoe Size as dependent variable. 6
Table 4: 95% Confidence interval for Shoe Size 6
Table 5: Counts for Shoe Size 7
Graph 1: Histogram and Box plot for Shoe Size 7
Graph 2: Histogram and Box plot for Height 7
Graph 3: Scatter plot of Height vs Shoe Size and Sex vs Shoe Size 8
Statistics and data analysis are topics of high importance and are required to be applied in different areas of real life for appropriate solutions to existing problems to be found. Business information systems are performing statistical analysis and related operations for the purposes of advanced data analysis and making decisions which are both efficient and significant. Companies are using these techniques for purposes of maximization of revenue and profit. Therefore, statistical methods and analysis are important in analysis of existing data and the results from the data can be used in making recommendations on appropriate courses of action to take for better positioning within the target markets. Statistics is also dependent on the existing data, therefore, it cannot be dismissed when used in forecasting.
Nyke Shoe Company are undergoing financial difficulties and are looking into making a single size of shoe regardless of demographics of customers such as gender, or height. This decision is intended to reduce the costs associated with production since in manufacturing different sizes of shoes, there are processes undergone such as measurement of and cutting of products into the required lengths and them being fitted onto different soles and this increases costs of production and the time spent in creation of products. However, if the company is able to find a single size that works for a larger group, then this can help them recover from their financial downturn and they can minimize the costs associated with production without a loss in sales as well as profits increasing. This solution will help in the overall financial problem being eliminated. There is need for statistical analysis to be done on the data to determine the shoe size they can focus on to help in solving their current problem.
The dataset given from the company has 3 variables and 35 data points as the points for carrying out the analysis. The variables are the ‘Shoe Size’, ‘Height’ and ‘Gender’ of the customers and the sizes and height are given in a discreet form in terms of them being integers, however, for advanced analysis, the values need to be continuous. The target of the statistical processes being applied is to discover the shoe size which meets the interests of the company and from the analysis being carried out, conclusions can be made. The dependent variables in this case is the shoe size while the independent variables are the gender and height. Additionally, gender is a variable which is qualitative and in order for the statistical analysis to be carried out, it needs to be quantitative in nature. Therefore, the following dummy variable will be created to solve the problem.
Sex = 1 if Gender = Female
= 0 if Gender = Male
The above variables have been used to derive the analysis that follows using the dummy variable newly defined.
Prior to working on a detailed analysis, there is need for exploration of the data. From my perspective, the best solution would be analysis of the descriptive demographics of the data. The tables which were obtained from this statistical analysis are in the Appendix and labelled as tables Table 1 and Table 2 respectively. The two tables give insights on the basic demographics of the data and from the data set, there is ability to derive the mean, mode and median for the different variables. The variability of the values gives insights that it is more present in the ‘Shoe Size’ variable as compared to the ‘Height’ variable with these being achieved through comparison of the respective mean values.
The mean mode and median for ‘Shoe Size’ respectively are 9.1429, 9, and 7.
The mean mode and median for ‘Height’ respectively are 68.9429, 70, and 70.
The second table, Table 2, shows that shoes made for females are almost 50% of production and the other 50% for males. The deviation of 51.43% from 50% is insignificantly small therefore the need for consideration of the genders for a better analysis of the results to be carried out. To better interpret and understand the data, there is need for an analysis f the Histogram and BoxPlots which are corresponding. The output obtained from this analysis is presented in the Appendix as Graphs 1 and 2. These graphs give insights on the variables ‘Shoe Size’ and ‘Height’ and conclusions can be made that their distributions are not normal. From the box plot, we see that there is a slight positive skew on the distribution of ‘Shoe Size’ with this being further supported by the coefficient of skewness in Table 1. The distribution of ‘Height’ is notably negatively skewed with evidences of this conclusion being drawn from both the table and the chart.
The next step to take is to draw the scatter plots and these resulted in the graphs Graph 3 and Graph 4 in the appendix. In the construction of these, the ‘Shoe Size’ was used as the dependent variable. These graphs show that the shoe size is variant across genders and heights. The graphs show the variations in height and sex of the customers and conclusions are that generally, people who are larger need a larger shoe size and also that males are in need of larger shoe sizes. From these statistics, there are expectations of relationships to exist between the independent variables. Running a regression analysis on this data returns a result of 3.
The regression analysis validates the existing doubts such as the shoe size being affected by variables such as gender and height. This, therefore, proofs that a standardized shoe size which would fit all is impossible. However, the company in focus cannot be able to cater for production costs of all sizes, therefore, there is need for them to find a central point to work on for maximum sales and profits. The best solution would be applying a point estimate alongside a confidence interval. The results are displayed in table 4.1 and the confidence interval is 95%.
The confidence interval is (8.256, 10.030)
This value, however, fails in giving proper results since Table 5 shows that only 5 from the range of 35 is included in this interval. The maximum shoe size is 7 with reference to table 5. Therefore, the test fails in giving best results since 7 becomes the usable size for the best outcome.
The initial outcome shows that both gender and height impact the size of the shoe significantly, therefore, there is no standard single shoe size which can be accepted by all genders and heights. This, therefore, makes it impossible to get a perfect result and maintain revenue. However, the best recommendation after carrying out the tests is 7 since this offers maximum efficiency. Therefore, size 7 shoes are recommended for production.
SUMMARY OUTPUT 



ANOVA 


df 
SS 
MS 
F 
Significance F 

Regression 
2 
203.1724 
101.5862 
137.6663 
0.0000 

Residual 
32 
23.6133 
0.7379 

Total 
34 
226.7857 





Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 

Intercept 
15.3534 
3.2377 
4.7421 
0.0000 
21.9484 
8.7584 

Height 
0.3735 
0.0453 
8.2475 
0.0000 
0.2812 
0.4657 

Sex 
2.4329 
0.3598 
6.7623 
0.0000 
3.1657 
1.7000 
OneSample T: Shoe Size
Variable N Mean StDev SE Mean 95% CI
Shoe Size 35 9.143 2.583 0.437 (8.256, 10.030)
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]]>For each column, identify whether the data is qualitative or quantitative
I would qualify data in columns A, B and C is quantitative in nature. Quantitative data is any data that can be quantified or measured and expressed as a number. In this data set, values of annual food spending, annual household income and non mortgage household debt are quantitative data. On the other hand, qualitative data cannot be expressed as a number. Data in column D and E is thus qualitative in nature because region and direction cannot be quantified.
Identify the level of measurement for the data in each column.
The level of measurement of data is used to determine the type of statistics which are meaningful. Data in column D and E is nominal in nature. This is because these are categorical data only can only be categorized using names or labels. For direction and region, all mathematical operations are meaningless. On the other hand, annual food spending, annual household income and non mortgage household debt are ratio level data.
For each column containing quantitative data:
◦Evaluate the mean and median
◦Interpret the mean and median in plain nontechnical terms
The mean for annual food spending, annual household income and non mortgage household debt is 8966, 55552 and 15604 respectively. On the other hand, the median for annual food spending, annual household income and non mortgage household debt is 8932, 54957 and 16100 respectively. When all the values are added and divided by the number of the values, the average is the mean. When the values are arranged from the least to the largest, the values in the middle will be the median.
For each column containing quantitative data:
◦Evaluate the standard deviation and range
◦Interpret the standard deviation and range in plain nontechnical terms
The standard deviation for annual food spending, annual household income and non mortgage household debt is 3125.007986, 14661.36006 and 8583.539127 respectively. On the other hand, the range for annual food spending, annual household income and non mortgage household debt is 15153, 74486 and 36374 respectively. The standard deviation is the measure of how the individual values spread out from the average. On the other hand, range is the difference between the maximum and minimum values of any given column.
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]]>Assignment Steps
This assignment has an Excel? dataset spreadsheet attached. You will be required to use the Consumer Food dataset.
Resource: Microsoft Excel?, Statistics Concepts and Descriptive Measures Data Set
Download the Statistics Concepts and Descriptive Measures Data Set.
Use the Consumer Food dataset to complete this assignment: Do not use the Financial or Hospital data sets.
Answer each of the following in a total of 90 words:
?For each column, identify whether the data is qualitative or quantitative.
?Identify the level of measurement for the data in each column.
?For each column containing quantitative data:
◦Evaluate the mean and median
◦Interpret the mean and median in plain nontechnical terms
◦Use the Excel =AVERAGE function to find the mean
◦Use the Excel =MEDIAN function to find the median
?For each column containing quantitative data:
◦Evaluate the standard deviation and range
◦Interpret the standard deviation and range in plain nontechnical terms
◦Use the Excel =STDEV.S function to find the standard deviation
◦For range (maximum value minus the minimum value), find the maximum value using the Excel =MAX function and find the minimum value using the Excel’s =MIN function
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]]>Introduction
The proliferation of statistics into business has been nothing but phenomenal over the last half century. Business statistics has become integral with businesses able to access more information that before. It has been used in converting the raw data into meaningful output used in decision making. The wholesome embrace of business statistics has, however, come with certain ethical issues. The ethical issues raised by Ostapski and Superville (2001) in examining business statistics mainly touch on the scenarios of nonowner use of data, and significance and outliers search.
Ethical issues ought to be addressed honestly and directly rather than ignoring them all together (Geertsema, 1987).While the application of historical contexts, social contexts, and scientific contexts has taken hold, Christian contexts have also been applied to address the ethical issues in business statistics. This paper looks at biblical perspectives and supporting ethical theories which are essential in ensuring ethical performance in business statistics.
Ethical Issues in Business Statistics
The process of using statistics to refine numbers is complicated thus the need for guidelines which probe the integrity of the process, participants, outcome, and interpretation (Kault, 2003). According to Ostapski and Superville (2001), there are various guidelines which are useful in applying critical thought in evaluating the ethical nature of statistics. The ethical guidelines which are most applicable to me include probing the data, its sources and procedure, interrogating the interest behind numbers, and investigating the interpretation.
There are certain key principles which offer guidance on ethical business statistics. These include maintaining integrity, protecting information confidentiality, fulfilling all the inquiry’s commitments, application of statistical procedures without leaning on a particular side, and seeking client’s consent when using their information (Ostapski and Superville, 2001). These guidelines are vital in addressing the ethical issues around nonowner data use, and significance and outlier search. These guidelines are also relevant in protecting the source of information, avoiding discriminate use of findings, and avoiding misinterpretation.
The raised ethical issues can be addressed from a Christian worldview with support from all relevant ethical theories. Honesty, fairness, and mutual respect are some of the biblical perspectives which are echoed in the business ethics guidelines. Christian viewpoint encompasses biblical doctrines and any supportive theories such as utilitarianism and distributive justice. Christian faith teaches these principles to be applied in daily life and they can be extended to fields such as statistics (Ripley &Dwiwardani, 2014).
Honesty and integrity are themes widely discussed in the bible and can be applied in maintaining ethical practice in business statistics. The book of Proverbs 2:2021 states, “So you will walk in the way of the good and keep to the paths of the righteous. For the upright will inhabit the land, and those with integrity will remain init”. Proverbs 28:18 also states, “Whoever walks in integrity will be delivered, but he who is crooked in his ways will suddenly fall”. These two biblical excerpts emphasize the need for Christians to uphold integrity in their transactions. Statistics should not be any different when it comes to integrity.
Integrity in statistics involves seeking the approval of clients before using their information, maintaining confidentiality of sources, and divulging all information without favoring a specific side. The ethical guidelines of the American Statistical Association (ASA) emphasizes on maintaining integrity and honesty as the first principle (Ostapski and Superville, 2001). Statisticians who follow the Christian principle of integrity are also able to follow other guidelines such as protecting confidentiality, maintaining impartiality, and seeking client’s consent before divulging their information.
One of the core Christian principles commonly emphasized is fair treatment. Luke 6:31 states “Do to others as you would have them do to you”. Do to others as you expect them to do to you is one of the Christian viewpoints which can be extrapolated to business ethics. According to Ostapski and Superville (2001), one of the bases of ethical inquiry is to understand if one’s decision towards another would be acceptable to the performer if they are subjected to similar decisions.
Nonowner use of data is one of the ethical dilemmas which have faced business statistics. Ownership and property rights are also expressed from a Christian perspective in Luke 16:12 when it states “And if you are not faithful with other people’s things, why should you be trusted with things of your own”. This Christian viewpoint should help the statistician to respect the ownership rights of their clients and honor their agreement. Those who get the client’s consent are able to use the data without aggravating their client. They are also supposed to use the data responsibly because they remain the property of the clients despite the permission.
Outliers is a controversial ethical area where the statistician is faced with the choice of removing or including certain findings. They might be faced with the choice of siding with the client at the expense of other users or divulging all findings. The book of Leviticus 19:15 provides a Christian viewpoint on dealing in an impartial manner when it states “You shall do no injustice in court. You shall not be partial to the poor or defer to the great, but in righteousness shall you judge your neighbor”. Christian principle requires them to favor no side but offer a fair chance for the end user to apply all relevant information, however unpleasant, to make interpretations and decisions.
Ostapski and Superville (2001) emphasize the need for considering the number of people affected by an issue as ethical inquiry basis. The authors suggest that the consequences of a proposed action should benefit most people most of the time. This is in line with the basic principle of the utilitarianism theory. Utilitarianism’s greatest happiness principle also insists on maximizing positive consequences to the greatest number of population while minimizing negative outcomes to the least population (Iman, 1995). This principle should also be applicable in the Christian viewpoint when making ethical decisions in business statistics.
The use of Christian perspectives does not undermine the scientific or social contexts which have become integral parts of ethical business statistics. Instead, these perspectives complement the other contexts and work to collectively ensure ethical practice (Ripley&Dwiwardani, 2014). For instance, ASA’s integrity principle is in line with the bible’s position on the same. According to Geertsema (1987), the Christian viewpoint of examining all things as interconnected plays a huge role in the integration of Christian approaches with scientific and social contexts. Christians in statistics have an obligation to perform in line with their religious beliefs while embracing a contextual approach which considers other viewpoints.
The article affects my personal decision making with respect to ethical analysis, information handling and disclosure, and application of statistical procedures. The unique challenges faced by statisticians leads to ethical issues which come with their work. The article has played a huge role in convincing me of the ultimate objective of maintaining ethical performance. The ability of a statistician to maintain ethical standards in business statistics influences the interpretation of the output and the relationships with clients (Hunter, 1994). When the statistician is unethical and omits certain aspects of the research to favor their client, the users of the information will unknowingly misinterpret the results. Statisticians who use their client’s information without consent also jeopardize their relationship and might bear certain legal consequences.
Conclusion
In conclusion, Christian approaches can be integrated into statistics to ensure ethical performance without interfering with the existing scientific and social assumptions. The ethical issues in business statistics have clearly presented room for considering Christian perspectives in handling the problems. Christian philosophies have room in viewing statistics and addressing the ethical issues in business statistics. The greatest happiness principle is applicable when addressing ethical issues because it maximizes the positives to a large number of people and minimizes the negative outcomes to a small proportion of the population. Integrity is a Christian philosophy which can be applied in maintaining ethical standards in business statistics.
The bible doctrines and ASA’s Ethical Guidelines for Statistical Practice agree on the need for integrity. This will ensure that confidentiality is maintained, owner’s consent is acquired before using their information, and the statistician remains impartial at all times. Ultimately, the application of these Christian viewpoints will ensure that the statistical process proceeded unhindered, clients are protected, and the final users interpret the correct information.
References
Geertsema, J.C. (1987). A Christian View of the Foundations of Statistics. Perspectives on Science and Christian Faith. 39(3), 158164.
Hunter, J. S. (1994). Statistics as a Profession. Journal of the American Statistical Association, 89(425), 16.
Iman, R. L. (1995). New Paradigms for the Statistics Profession.Journal of the American Statistical Association, 90(429), 19.
Kault, D. (2003). Statistics with Common Sense. Westport, CT: Greenwood Press.
Ripley, J. S., &Dwiwardani, C. (2014). Integration of Christianity in Research and Statistics Courses. Journal of Psychology and Theology, 42(2), 220227.
Ostapski , A. Superville , C. (2001). Reflection before action: The statistical consultant confronts ethical issues. Business Quest. Retrieved from http://www.westga.edu/~bquest/2001/consultant.htm
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]]>Read the following:
“Ethical Issues” sections in chapters 2.8, 3.6, 4.5, 7.2, 8.5, and 9.5 in the textbook.
Chapters 7.1 and 7.2 in the textbook.
Refer to “Reflection Before Action: The Statistical Consultant Confronts Ethical Issues.” In this article, the authors cite the American Statistical Association’s Ethical Guidelines for Statistical Practice (see the section “Ethics Provides A Defensible Response” in the article).
“A Christian View of the Foundations of Statistics”
Write a paper that considers ethical issues in business statistics and discuss how your personal values can be applied to them. This assignment is primarily introspective in nature and students will be given significant latitude in addressing specific questions.
Directions
Include a brief introduction that outlines your paper.
Address the following question: Which ethical guideline(s) from the articles is most applicable to you and why? How does this article (or articles) affect your personal decision making as it related to statistics and ethics?
How can the ethical issue(s) raised be addressed from a Christian worldview? In other words, what guidance, using principles from a Christian worldview perspective, could be applied to understand and address these ethical issues? The following GCU websites may be helpful:
http://www.gcu.edu/AboutUs/DoctrinalStatement.php
http://www.gcu.edu/AboutUs/MissionandVision.php
In addition to addressing questions for item 4 above, the student may also optionally frame the issue using ethical theories (Utilitarianism, Kantian ethics, Distributive Justice, Virtue ethics and Covenantal ethics). Note, however, that the questions in item 3 must still be addressed.
Provide a brief conclusion that summarizes your perspectives.
Your paper should have at least six external citations (in addition to any Biblical citations) to help frame the issue. No Wikipedia citations are allowed
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]]>Research job boards for project manager positions. Take note of the various industries that hire project managers. Then, research statistics from the project management institute on the rate of success of projects in different industries including IT.
Write a two to three (23) page paper in which you:
1. Explain project management as a discipline.
2. Describe the industries in which project managers are in high demand. Provide evidence to support your response.
3. Describe the general role of a project manager, and explain the primary ways in which it differs across different industries.
4. Compare the rate of success of projects in IT and other industries. Explain the discrepancy or lack thereof.
5. Use at least two (2) 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 oneinch margins on all sides; citations and references must follow APA or schoolspecific 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 how project management improves the success of information technology projects.
· Use technology and information resources to research issues in IT project management.
· Write clearly and concisely about issues in IT project management using proper writing mechanics and technical style conventions.
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]]>Format your summary consistent with APA guidelines.
Statistics in Business – Sample Paper
Statistics in Business
Statistics is a science of decision making to eliminate uncertainty. Statistics is a widely used method in business. Statistics is quantitative in nature and deals with getting the most precise data to use for decisionmaking purposes. Statistics is also described as “the science of learning from data”.
There are different types of statistics. Inferential and descriptive statistics are the main types used. Inferential statistics also called inductive statistics is a type that draws its conclusion from data that possess random variations and is subject to change. The variations could be sampling variations or observational errors. The data changes in an unpredictable manner.
The other type of statistics is descriptive statistics. This kind of statistics summarizes data from samples using defined indexes such as mean, mode, and standard deviation. Descriptive statistics usually deal with two sets of properties in a sample or population distribution.
There are four different levels of statistics, nominal, ordinal, interval and ratio. The first level is nominal and uses numbers to classify data. Words and letters can also be used. The ordinal level is the second one, and here data has order, but the interval between measurements is not equal. Interval level classifies and orders data, has meaningful intervals between data, but there is no true starting point. The fourth level is ratio, data has ratios between measurements, has meaningful intervals and a starting point (Kundu, n.d.).
In business, statistics is used in predicting sales, controlling quality and for market research in decision making. Statistics could be applied in different situations. A manager can try to determine the productivity of workers by observing and recording the amount of work they deliver. From the data, they can analyze the data and understand the performance of each employee. Another example of how statistics can be used is when a trader wants to know what will happen in business. The trader can look at past statistics and predict how the business will be.
References
Buseco.monash.edu.au,. (2013). What is Business Statistics? – Department of Econometrics and Business Statistics. Retrieved 9 July 2015, from http://www.buseco.monash.edu.au/ebs/about/busstats.php
Kundu, S.(n.d.) An Introduction To Business Statistics, 1506.
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