LABS

LABS

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title: “Regression, Mediation, Moderation”
author: “Enter Your Name”
date: “`r Sys.Date()`”
output: word_document

“`{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
“`
*Title*: The influence of cognitive and affective based job satisfaction measures on the relationship between satisfaction and organizational citizenship behavior
*Abstract*: One of the most widely believed maxims of management is that a happy worker is a productive worker. However, most research on the nature of the relationship between job satisfaction and job performance has not yielded convincing evidence that such a relationship exists to the degree most managers believe. One reason for this might lie in the way in which job performance is measured. Numerous studies have been published that showed that using Organizational Citizenship Behavior to supplant more traditional measures of job performance has resulted in a more robust relationship between job satisfaction and job performance. Yet, recent work has suggested that the relationship between job satisfaction and citizenship may be more complex than originally reported. This study investigated whether the relationship between job satisfaction and citizenship could depend upon the nature of the job satisfaction measure used. Specifically, it was hypothesized that job satisfaction measures which reflect a cognitive basis would be more strongly related to OCB than measures of job satisfaction, which reflect an affective basis. Results from data collected in two midwestern companies show support for the relative importance of cognition based satisfaction over affect based satisfaction. Implications for research on the causes of citizenship are discussed.
# Dataset:

– Dependent variable (Y): OCB – Organizational citizenship behavior measure
– Independent variables (X)
– Affective – job satisfaction measures that measure emotion
– Cognitive – job satisfaction measures that measure cognitions (thinking)
– Years – years on the job
– Type_work – type of employee measured (secretary, assistant, manager, boss)
# Data Screening:
Assume the data is accurate with no missing values. You will want to screen the dataset using all the predictor variables to predict the outcome in a simultaneous multiple regression (all the variables at once). This analysis will let you screen for outliers and assumptions across all subsequent analyses/steps. Be sure to factor type_work.
“`{r starting}
“`
## Outliers

a. Leverage:
i. What is your leverage cut off score?
ii. How many leverage outliers did you have?
“`{r leverage}
“`

b. Cook’s:
i. What is your Cook’s cut off score?
ii. How many Cook’s outliers did you have?

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“`{r cooks}
“`

c. Mahalanobis:
i. What is your Mahalanobis df?
ii. What is your Mahalanobis cut off score?
iii. How many outliers did you have for Mahalanobis?

“`{r mahal}
“`

d. Overall:
i. How many total outliers did you have across all variables?
ii. Delete them!
“`{r overall}
“`
# Assumptions:
## Additivity:
a. Include a correlation table of your independent variables.
b. Do your correlations meet the assumption for additivity (i.e. do you have multicollinearity)?
“`{r additivity}
“`
## Linearity:
a. Include a picture that shows how you might assess multivariate linearity.
b. Do you think you’ve met the assumption for linearity?
“`{r linearity}
“`
## Normality:
a. Include a picture that shows how you might assess multivariate normality.
b. Do you think you’ve met the assumption for normality?
“`{r normality}
“`
## Homogeneity and Homoscedasticity:
a. Include a picture that shows how you might assess multivariate homogeneity.
b. Do you think you’ve met the assumption for homogeneity?
c. Do you think you’ve met the assumption for homoscedasticity?

“`{r homogs}
“`
# Hierarchical Regression:
a. First, control for years on the job in the first step of the regression analysis.
b. Then use the factor coded type of job variable to determine if it has an effect on organizational citizenship behavior.
c. Last, test if cognitive and affect measures of job satisfaction are predictors of organizational citizenship behavior.
d. Include the summaries of each step, along with the ANOVA of the change between each step.

“`{r hierarchical}
“`
# Mediation
a. Calculate a mediation model wherein the number of years mediates the relationship between affective measurements and OCB.
b. Include each path and summaries of those models.
c. Include the Sobel test.
d. Include the bootstrapped indirect effect.
“`{r mediation}
“`
# Write up:

Hierarchical regression only!
a. Include a brief description of the experiment, variables, and order entered into steps.
b. Include a brief section on the data screening/assumptions.
c. Include the all F-values for each step of the model – you can reference the above table.
d. Include all the b or beta values for variables in the step they were entered. So, you will not have double b values for any predictor – you can reference the above table.
e. Include an interpretation of the results (dummy coding, do our results match the study results, etc.).


title: “t-Tests”
author: “Enter Your Name”
date: “`r Sys.Date()`”
output: word_document

“`{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
“`
*Title*: Estimation of physical activity levels using cell phone questionnaires: A comparison with accelerometry for evaluation of between-subject and within-subject variations
*Abstract*: Physical activity promotes health and longevity. From a business perspective, healthier employees are more likely to report to work, miss less days, and cost less for health insurance. Your business wants to encourage healthy livestyles in a cheap and affordable way through health care incentive programs. The use of telecommunication technologies such as cell phones is highly interesting in this respect. In an earlier report, we showed that physical activity level (PAL) assessed using a cell phone procedure agreed well with corresponding estimates obtained using the doubly labeled water method. However, our earlier study indicated high within-subject variation in relation to between-subject variations in PAL using cell phones, but we could not assess if this was a true variation of PAL or an artifact of the cell phone technique. Objective: Our objective was to compare within- and between-subject variations in PAL by means of cell phones with corresponding estimates using an accelerometer. In addition, we compared the agreement of daily PAL values obtained using the cell phone questionnaire with corresponding data obtained using an accelerometer.
# Dataset:
– Gender: male and female subjects were examined in this experiment.
– PAL_cell: average physical activity values for the cell phone accelerometer (range 0-100).
– PAL_acc: average physical activity values for the hand held accelerometer (range 0-100).
APA write ups should include means, standard deviation/error, t-values, p-values, effect size, and a brief description of what happened in plain English.
“`{r starting}
“`
# Data screening:
## Accuracy:
a) Include output and indicate how the data are not accurate.
b) Include output to show how you fixed the accuracy errors, and describe what you did.

“`{r accuracy}
“`
## Missing data:
a) Include output that shows you have missing data.
b) Include output and a description that shows what you did with the missing data.

“`{r missing}
“`
## Outliers:
a) Include a summary of your mahal scores that are greater than the cutoff.
b) What are the df for your Mahalanobis cutoff?
c) What is the cut off score for your Mahalanobis measure?
d) How many outliers did you have?
e) Delete all outliers.

“`{r outliers}
“`
# Assumptions:
## Additivity:
a) We won’t need to calculate a correlation table. Why not?
## Linearity:
a) Include a picture that shows how you might assess multivariate linearity.
b) Do you think you’ve met the assumption for linearity?

“`{r linearity}
“`
## Normality:
a) Include a picture that shows how you might assess multivariate normality.
b) Do you think you’ve met the assumption for normality?
“`{r normality}
“`
## Homogeneity/Homoscedasticity:
a) Include a picture that shows how you might assess multivariate homogeneity.
b) Do you think you’ve met the assumption for homogeneity?
c) Do you think you’ve met the assumption for homoscedasticity?
“`{r homog-s}
“`
# Independent t-test:
1) Run an independent t-test to determine if there are differences in gender for the cell phone measurement of physical activity level.
a. Use the equal variances option to adjust for problems with homogeneity (if necessary).
b. Include means and sds for your groups.
c. Is there a significant difference in the ratings?

“`{r ind1}
“`
2) Effect size: What is the effect size for this difference? Be sure to list which effect size you are using.
“`{r effect1}
“`
3) Power: Determine the number of participants you should have used in this experiment given the effect size you found above.
“`{r power1}
“`
4) Graphs: Include a bar graph of these results.
“`{r graph1}
“`
5) Write up: include an APA style results section for this analysis (just the t-test not all the data screening).
# Dependent t-test:
6) Run a dependent t-test to tell if there are differences in the cell phone and hand held accelerometer results.
a. Include means and sds for your groups.
b. Is there a significant difference in the ratings?
“`{r dep1}
“`
7) Effect size: What is the effect size for this difference? Be sure to list which effect size you are using.
“`{r effect2}
“`
8) Power: Determine the number of participants you should have used in this experiment given the effect size you found above.
“`{r power2}
“`
9) Graphs: Include a bar graph of these results.
“`{r graph2}
“`
10) Write up: include an APA style results section for this analysis (just the t-test not all the data screening).
# Theory:
11) List the null hypothesis for the dependent t-test.
12) List the research hypothesis for the dependent t-test.
13) If the null were true, what would we expect the mean difference score to be?
14) If the null were false, what would we expect the mean difference score to be?
15) In our formula for dependent t, what is the estimation of systematic variance?
16) In our formula for dependent t, what is the estimation of unsystematic variance?

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gender PAL_cell PAL_acc
male 72.4006570351277 85.1370858719345
76.2062996236089 74.6959138225082
73.9894180950249 66.7842060128155
70.8665372809887 65.4232948393563
54.8597935442081 67.4557297142323
77.9428213253685 83.2949210027534
69.106757360934 66.7267940307791
75.0644352678202 72.3854161281744
67.690800874886 51.9653490922108
73.0956931780271 74.5262699893203
NA 52.3786938575334
80.7885433804063 76.2271047043759
69.1510462374321 55.7394383985716
78.6761864584267 75.5666101223992
82.1840134797691 69.3291091417178
72.5881982207414 63.8441979148815
60.8752944426126 69.8522165528652
85.4194894102543 76.4244170597175
70.3795418254552 71.3548359622226
83.9443234031233 68.9070753560881
74.7783439061255 72.5852818708896
81.9823120573364 82.6472765986365
71.1537383216659 65.446526374528
74.1502037172106 68.2750448245837
77.6928590730127 68.5426984264035
70.8320280343964 70.7658184142912
63.3136123988265 61.3697366637545
73.0270354117649 74.1959036113411
72.0998103355967 66.9589501205765
63.9739048278267 64.3910024134725
72.5867264483627 70.3243824046032
65.1000733364443 63.2019880967467
78.4483955038249 69.2453539671429
90.2097428580619 74.2856646491723
76.2933898266054 69.6312660388654
84.4844617874705 82.241034494705
85.0554396824544 85.0694356496738
73.5877926539369 71.5126424751607
77.7323610551133 65.7271179464633
71.8797101617864 62.6315166212805
74.2292069418433 79.424840043561
79.1233476325559 77.7298876098711
77.819384036623 79.0504868020557
70.5362858869002 62.966500312717
70.2123528654554 69.7462910930523
73.0988593385123 75.3487112135503
73.0561505266601 60.7000763505234
71.4232123393147 60.549204963135
67.3508041186572 61.5220529118362
67.76346409569 59.402077669912
75.1795789090658 76.6171647013762
74.368405753655 67.9810318743127
74.7597829003647 80.6204749350757
85.8342232484146 84.7287736856981
59.0195558703319 58.8082479939406
73.9590236930434
77.9278594960994 69.730726605992
73.044817154869 64.7612654776963
72.274328833067 72.4651573922092
69.3051195146289 62.8878982708685
83.5182436761721 71.1858044277597
81.2188059597867 72.3438776551328
78.2664034073347 74.3044235344201
62.038733675014 59.7568589351898
70.1339026109148 61.7161203295751
72.745881780121 62.3704110527748
79.7551458199661 65.2151817402737
78.8219443792875 74.0659868010663
73.4362357962979 70.2232850790912
75.8394533142271 73.7603701723708
81.9345948789685 81.6726306622105
80.7780112459981 77.8168191770759
64.4389399660445 65.0891742238283
72.0851556073606 70.262435230257
78.0031596452471 78.9446639567774
83.2007915610454 76.8252997817864
70.884176513492 70.5079226605996
63.5767438349245 69.446648205887
63.1693061799022 67.0352126723534
72.5628327633008 62.170790184715
89.1763118099635 89.5578875531754
76.0563325757639 69.8494489157275
76.4530106412504 74.1872279784795
74.7441033759586 70.9552462013045
72.7007153532424 77.6801760009618
57.1097942447942 50.9140622561547
69.5381197904941 64.6277077720865
79.494275229266 77.8863848615241
66.6557349049712
76.5363085307671 65.100208652412
61.8604152881768 55.3871871381033
72.3791138110899 63.3832401383328
84.8051305473612 93.6115271073327
71.0076992718115 63.2341214060054
80.9325286599697 66.9488723062493
64.3073299954988 64.1717374509959
87.2804135659903 88.5637895094386
74.697018341742
88.8566033134163 73.4761506320622
82.4864317997046 77.6827696888457
female 51.616168395204 58.6858930718538
65.1843694427764 57.702425741375
57.6460878839399 35.8330758962889
52.6546523844225 58.333214584317
52.7997212922793 49.2724870069073
61.226214758117 64.8385164463736
53.1153595876983 51.3802294811831
54.9720379204206 62.5531537959167
69.5866632975411 69.7654607399738
46.1836249123455 48.7005481878049
79.2594210686776
56.9946920845849 54.3556811001809
52.9654978248591 50.1938501196584
63.7205270732329 41.0057945673038
40.4447355736901 32.0332467565067
56.4387580922722 48.3211368945957
57.4474638303147 39.1965148497016
53.8853036603842 46.0202933783171
57.5615230471273 62.8306894177542
56.1749122849326 58.9976122775338
64.1352918514364 66.3472519699943
67.4802958845261 49.4521766855608
52.1348583779612 47.6347432456076
58.0591849873238 66.4055373507641
59.4533903316241 68.9282180713862
49.8982379566972 33.1053584564401
53.0529496507504 46.007294287504
64.138642177445 51.4168044511176
55.0272495005356 53.5706395032084
62.1159997505588 63.6384850714131
54.1614958447524 47.7875489495303
63.8642349286339 67.8321221810552
50.2286092147927 42.4014357623181
55.0087074707257
68.8569392117939 52.2712258484566
62.0625527496114 53.7929934466563
44.5148073957955 47.592812666847
48.9616359131003 55.8702159171738
56.8109523474601 60.9019862401383
65.3672997736966
47.6909666747157 43.8167161247407
53.1983381043173 57.4435810163839
75.2794214604864 68.5921001920408
55.910375914201 59.5351848982978
69.2982876785673
62.5187947298996 51.3387619611688
64.5380154166359 60.295703326528
51.5160392013021 44.7848896160542
59.4742109453489
58.1462923479262 60.5163145513654
56.5248903427374 45.4096392984706
54.8504714578117 51.4023461477762
53.8797239498076 57.8816954563351
59.8720985850282 60.0534226202175
53.4419227770739 35.3819751585917
59.2258722460321 44.1019980760806
61.598928543317 51.2944549607942
51.6240812034678
62.2915484062591 42.4340785961811
50.196023090885 52.9204525565029
50.0489972545898 36.1108458018804
57.4584538278174 40.1313131919599
56.1628882291369 53.3286883192153
41.3326138273037 32.9287056271615
56.1845480014142 49.9206677865009
59.3255642770734 52.4765728718719
43.6611457318824 46.5159064523431
48.475687689923 61.8942629272446
55.5744324171805 55.9475791431643
48.0797120780012 49.8802020437662
72.5079727425428
51.1149578338325 42.3755553934392
71.8395207127395 65.9105078442087
67.1551365153855 63.3566794159513
50.4912137243717 52.6298669162426
57.4535107424998 64.459078261412
53.1241348361738 46.8776245290464
78.4143751483127 72.4168679609986
63.8193489545053 46.1935591286504
65.0457959672517 66.7943293695867
61.0568225726414
57.3829445713105 51.6385184919046
54.1165704063913 58.8588059622564
62.9863042887545 53.924260267259
62.0773848345567 67.6852738372381
52.8070608618836 47.7286111634228
62.4811637151255 45.3512695918287
63.0707169540065 51.9111986070632
56.0193254790578 63.304253016477
56.4543595630912 54.2613025436027
55.0043438280189 68.8073777952618
48.8297631664403 23.6775691899542
59.7746897745555 48.8845264077475
46.994483410107 47.1793791441458
54.9882553143553 49.9612944208481
60.5237773187336 45.7733174149099
68.869835837059 82.9969108375415
54.7216388851341 52.0388453026458
48.9675338582696 57.144612047691
38.324566178386 50.865215973678

ANLY 500 – Principles of Analytics I

Final Project Guidelines

The purpose of the project is to learn how to formulate a problem statement or research question, determine how to best find a solution to the stated problem or answer to the research question, do that and then develop a final written report and presentation. The project is team-based or individual, I leave the choice to you. Individual grades will include points for how well they contributed to the team effort.

The course project has three (3) deliverables:

1) Project proposal,

2) Presentation and,

3) Final Report

Each of these deliverables will be described in the paragraphs below. As an overview, each team will select a company,

organization

, or industry to target as their focus. Collect some data that will allow you to develop a case study to address an problem. Each case study presents a situation, challenge or problem the company, organization, or industry has had or is having. The primary objective of the course project is to determine how analytics could or can help the respective company address the situation or overcome the challenge or problem it is facing.

To do this each team must review the case study, formulate a problem statement or research question as appropriate, and then identify the appropriate analytics methods or techniques to complete an analysis where possible.

Part 1:
Each team must develop a proposal as described below consisting of 20% of the grade. Proposals due date refer to Moodle.

Part 2:
Last, the team should develop their presentation as described below consisting of 40% of the grade. Final presentations are due by the Third Executive Meeting.

Part 3: The third piece to the final project is the final written report as described below consisting of 40% of the grade. Final Reports consisting of 40% due date refer to Moodle.

1. Project Proposal

The project proposal is intended to introduce the company and its situation, problem or challenge. It should include all relevant information for that introduction. The proposal should try to answer the following questions:

· What is the problem you are trying to solve or question you are trying to answer?

· What data do you need?

· What work do you plan to do in the project?

· Which algorithms/techniques/models do you plan to use/develop? Be as specific as you can.

· How will you evaluate what you’ve done?

· What do you expect to submit/accomplish by the end of the project?

Proposal Requirements:

· 1-2 pages

· 12 pt font

· Times New Roman.

· Word or pdf.

· Double Spacing.

· APA formatting

.

2. Presentation

By the time your presentation is due you should have completed at least 90% of your project work. The presentation can serve as a draft of your final report but without your final analysis and results, but I do suggest having at least test results of a model. You should include at least the following in your presentation:

· What the problem is that you are trying to solve or question you are trying to answer.

· All relevant background information including any relevant literature you have/will use.

· The overall process you will follow for the entire project.

· A description of your data including how you obtained it.

· A description of any relevant, interesting exploratory data analyses.

· A description of the methods/techniques/tools/algorithms you have/will use to complete the project. Include test results if applicable.

· A description of the challenges you have had working on the project.

· A discussion of the parts of the project that have been completed.

· A discussion of the parts of the project that remain to be completed.

· A discussion of how you will finish the final project report and presentation.

Presentation Requirements:

· 10 slides minimum

· ppt

· APA formatting

3. Final Project Report

The final report and presentation should cover virtually everything about the project. It should cover the situation, problem or challenge that required attention, the relevant background, related work, data, and technical details of the analysis, conclusions and possible directions for future work. It is recognized that not all of the following sections will pertain to each report. However, it is strongly recommended that these section topics be used as a guideline for your final project reports. Final presentations can follow your final report in text and graphical content.

Introduction, motivation and general description of the situation, problem or challenge.

· Following the proposal and status report, what is the situation, problem or challenge you are addressing?

· What preliminary examination leads you to believe analytics could help?

· What are the shortcomings of the current work/analysis that analytics could help with?

Related work.

· Provide a thorough background for the project; e.g. about the company, about the situation, problem or challenge, about other companies that have undergone similar situations, problems or challenges and how they handled them or did not, etc.

· How does this project relate to other work that has been done on this situation, problem or challenge?

Data

· Give a complete description of the data you use during the project, including any you reject.

· Provide the source(s) of your data.

· Provide a detailed description of your data.

· Provide any exploratory data analyses you complete.

Technical Approach

· Give a detailed description of the process for your entire project.

· Given a detailed description of your approach to the analytics you have proposed to use including any algorithms, methods, tools or techniques. You do not have to describe well known approaches themselves, e.g. linear regression. You do have to describe how you applied the approach you used.

Test and evaluation

· Describe how you test your approach to ensure that it is valid.

· Discuss the validity of your approach.

· Describe how you will evaluate your results and/or conclusions including any specific metrics, output data, completed analyses, etc.

· Discuss the baseline you will use to compare your results to.

· Discuss how well your approach worked to address the situation or challenge, solve the problem or answer the research question.

· Discuss any potential future work. For example, if you were not able to resolve the situation or problem or answer the research question what will it take to do so? What else needs to be done?

· Evaluate and report whether or not someone unfamiliar with your work could accurately replicate it.

Written work and Presentation Style

· Written work will be graded using the rubric provided.

· Presentation style will be graded on comprehensiveness and inclusiveness, as well as using the rubric provided.

Final Report Requirements:

· Refer to ANLY_500_Report_Formatting

Grading Guidelines for Deliverables:

25%

25%

Critical Elements

Exemplary (100%)

Proficient (90%)

Needs Improvement (70%)

Not Evident (0%)

Value

Data Source

Business Value

Meets “Proficient” criteria and

provides relevant

examples

or

in-depth analysis of the data to

support the explanation

Logically describes the business

value of the available data and

data sources for the

organization

Describes the business value of

the available data and data

sources for

the organization

,

but with gaps in logic

Does not describe the business

value of the available data and
data sources for the
organization

25%

Application

Meets “Proficient” criteria and

provides relevant examples to

support business value

Accurately explains how

the

phases of the

methodology will

enable proper

execution of

the data solution

Explains how the phases of the

methodology will

enable proper execution of the

data solution, but the explanation

contains errors or omissions

Does not explain how the phases

of the methodology

will enable proper execution of

the data solution

Analytic Structure

Selection

Meets “Proficient” criteria and
provides relevant examples to

support the defense of the

structure

Logically defends how the

selected structure could provide

support, benefits, and value for

the organization

Defends how the selected

structure could provide support,

benefits, and value for the

organization, but with gaps in

logical application to the

organization

Does not defend how the

selected structure could provide
support, benefits, and value for
the organization

Articulation of

Tool Selection

Meets “Proficient” criteria and

includes a comparison of benefits

of the selected tool over other

potential options

Logically defends how the

selected tool can produce

analysis and reporting that could

provide support, benefits, and

value for the organization

Defends how the selected tool

can produce analysis and

reporting that could provide

support, benefits, and value for

the organization, but with gaps in

logical application to the
organization

Does not defend how the

selected tool could provide

support, benefits, and value for
the organization

15%

Additional Data

Sources

Meets “Proficient” criteria and

explanation of added value is

qualified with relevant, real-world

examples

Accurately explains how

additional internal or external

data sources may add further

value

to the organization

as

supported by evidence

Explains how additional internal

or external data sources may

add further value to the

organization, but explanation is

not accurate or not supported

Does not explain how additional

internal or external data

sources may add further value

to the organization

10%

Grading Rubric for Final Project:

· Presentation = 50%

· Final Report = 50%

· Total = 100%

4

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Preparation of Papers for IEEE Computer Society TRANSACTIONS

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2

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1

2

)

F

irst A. Author, Second B. Author

J

r., and Third C. Author, Member, IEEE

Abstract—These instructions give you guidelines for preparing papers for IEEE Computer Society Transactions. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. Please note that use of IEEE Computer Society templates is meant to assist authors in correctly formatting manuscripts for final submission and does not guarantee how the final paper will be formatted by IEEE Computer Society staff. This template may be used for initial submissions; however, please consult the author submission guidelines for formatting instructions as most journals prefer single column format for peer review. An abstract should be 100 to 200 words for regular papers, no more than 50 words for short papers and comments, and should clearly state the nature and significance of the paper. Abstracts must not include mathematical

exp

ressions or bibliographic references. Please note that abstracts are formatted as left justified in our editing template (as shown here).

Index Terms—Keywords should be taken from the taxonomy (http://www.computer.org/mc/keywords/keywords.htm). Keywords should closely reflect the topic and should optimally characteri

z

e the paper. Use about four key words or phrases in alphabetical order, separated by commas (there should not be a period at the end of the index terms)

xxxx

xxxx/0x/$xx.00 © 200x IEEE Published by the IEEE Computer Society

————————————————

· F.A. Author is with the National Institute of Standards and Technology, Boulder, CO 80305. E-mail: author@ boulder.nist.gov.

· S.B. Author Jr. is with the Department of Physics, Colorado State University, Fort Collins, CO 80523. E-mail: author@colostate.edu.

· T.C. Author is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309. On leave from the National Research Institute for Metals, Tsukuba, Japan E-mail:

author@nrim.go.jp

.

***Please provide a complete mailing ad

dr

ess for each author, as this is the address the 10 complimentary reprints of your paper will be sent

Please note that all acknowledgments should be placed at the end of the paper, before the bibliography (note that corresponding authorship is not noted in affiliation box, but in acknowledgment section).

—————————— ——————————

IEEE TRANSACTIONS ON journal name, manuscript ID first page 1

2

even page IEEE TRANSACTIONS ON XXXXXXXXXXXXXXXXXXXX, vol. #, no. #, MMMMMMMM 1996

AUTHOR: TITLE odd page 3

1 Introduction

T

HIS document is a template for Microsoft Word versions 6.0 or later. If you are reading a paper version of this document, please download the electronic file from the

template download page

so you can use it to prepare your manuscript.

When you open the document, select “Page Layout” from the “View” menu in the menu bar (View

|

Page Layout), which allows you to see the footnotes. Then type over sections of the document or cut and paste from another document and then use markup styles. Please keep the template at 8.5” x 11”—do not set the template for A4 paper. The pull-down style menu is at the left of the Formatting Toolbar at the top of your Word window (for example, the style at this point in the document is “Text”). Highlight a section that you want to designate with a certain style, and then select the appropriate name on the style menu. The style will adjust your fonts and line spacing. Use italics for emphasis; do not underline. Do not change the font sizes or line spacing to squeeze more text into a limited number of pages. Please be certain to follow all submission guidelines when formatting an article or it will be returned for reformatting.

To modify the running headings, select View | Header and Footer. Click inside the text box to type the name of the journal the article is being submitted to and the manuscript identification number. Click the forward arrow in the pop-up tool bar to modify the header or footer on subsequent pages.

To insert images in Word, position the cursor at the insertion point and either use Insert | Picture | From File or copy the image to the Windows clipboard and then Edit | Paste Special | Picture (with “Float over text” unchecked).

IEEE Computer Society staff will edit and complete the final formatting of your paper.

2 Procedure for Paper Submission

2.1 Review Stage

Detailed submission guidelines can be found on the author resources Web pages. Author resource guidelines are specific to each journal, so please be sure to refer to the correct journal when seeking information. All authors are responsible for understanding these guidelines before submitting their manuscript. For further information on both submission guidelines, authors are strongly encouraged to refer to

http://www.computer.org/portal/web/peerreviewjournals/author

.

2.2 Final Stage

For papers accepted for publication, it is essential that the electronic version of the manuscript and artwork match the hardcopy exactly! The quality and accuracy of the content of the electronic material submitted is crucial since the content is not recreated, but rather converted into the final published version.

All papers in IEEE Computer Society Transactions are edited electronically. A final submission materials check list, transmission and compression information, and general publication materials can be found at: http://www.computer.org/portal/web/peerreviewjournals/author.

2.3 Figures

All tables and figures will be processed as images. You will have the greatest control over the appearance of your figures if you are able to prepare electronic image files. Save them to a file in PostScript (PS) or Encapsulated PostScript (EPS) formats. Use a separate file for each image. File names should be of the form “fig1.ps” or “fig2.eps.”

For more information on how to format your figure or table files for final submission, please go to

http://www.computer.org/portal/web/peerreviewjournals/author#figures

and

View transactions art_guide (PDF, 4.69MB)

.

2.4 Copyright Form

An IEEE Computer Society copyright form must accompany your final submission. You can get a , .html, or version at

http://computer.org/copyright.htm

. Authors are responsible for obtaining any security clearances.

For any questions about initial or final submission requirements, please contact one of our staff members. Contact information can be found at:

http://www.computer.org/portal/web/volunteercenter/staff

.

3 Sections

As demonstrated in this document, the numbering of sections is upper case Arabic numerals, then upper case Arabic numerals, separated by periods. Initial paragraphs after the section title are not indented. Only the initial, introductory paragraph has a drop cap.

4 Citations

IEEE Computer Society style is to note citations in individual brackets, followed by a comma, e.g. “

[

1], [5]” (as opposed to the more common “[1, 5]” form.) Citation ranges should be formatted as follows: [1], [2], [3], [4] (as opposed to [1]-[4], which is not IEEE Computer Society style). When citing a section in a book, please give the relevant page numbers [2]. In sentences, refer simply to the reference number, as in [3]. Do not use “Ref. [3]” or “reference [3]” At the beginning of a sentence use the author names instead of “Reference [3],” e.g., “Smith and Smith [3] show … .” Please note that references will be formatted by IEEE Computer Society production staff in the same order provided by the author.

5 Equations

If you are using Word, use the MathType add-on (

http://www.mathtype.com

) for equations in your paper (Insert | Object | Create New | Microsoft Equation or MathType Equation). “Float over text” should not be selected.

For display equations as seen below, number equations consecutively with equation numbers in parentheses flush with the right margin, as in (1). First, use the equation editor to create the equation. Then, select the “Equation” markup style. Press the tab key and write the equation number in parentheses. To make your equations more compact, you may use the solidus ( / ), the exp function, or appropriate exponents. Use parentheses to avoid ambiguities in denominators. Punctuate equations when they are part of a sentence, as in

(1)

Be sure that the symbols in your equation have been defined before the equation appears or immediately following. Italicize symbols (T might refer to temperature, but T is the unit tesla). Per IEEE Computer Society, please refer to “(1),” not “Eq. (1)” or “equation (1),” except at the beginning of a sentence: “Equation (1) shows … .” Also see The Handbook of Writing for the Mathematical Sciences, 1993. Published by the Society for Industrial and Applied Mathematics, this handbook provides some helpful information about math typography and other stylistic matters. For further information about typesetting mathematical equations, please visit the IEEE Computer Society style guide:

http://www.computer.org/portal/web/publications/style_math

.

Please note that math equations might need to be reformatted from the original submission for page layout reasons. This includes the possibility that some in-line equations will be made display equations to create better flow in a paragraph. If display equations do not fit in the two-column format, they will also be reformatted. Authors are strongly encouraged to ensure that equations fit in the given column width.

6 Helpful Hints

6.1 Figures and Tables

Because IEEE Computer Society staff will do the final formatting of your paper, some figures may have to be moved from where they appeared in the original submission. Figures and tables should be sized as they are to appear in print. Figures or tables not correctly sized will be returned to the author for reformatting.

Detailed information about the creation and submission of images for articles can be found at http://www.computer.org/portal/web/peerreviewjournals/author#figures where you can View transactions art_guide (PDF, 4.69MB) . We strongly encourage authors to carefully review the material posted here to avoid problems with incorrect files or poorly formatted graphics.

Place figure captions below the figures; place table titles above the tables. Figure captions appear as left justified. Table captions are restricted to one sentence and are formatted as title case. Any additional sentence in a table caption will be formatted as a footnote below the table (see Table 1 in this document). If your figure has two parts, include the labels “(a)” and “(b)” as part of the artwork. Please verify that the figures and tables you mention in the text actually exist. Figures and tables should be called out in sequential order, as this is how they will be placed in your paper. For example, avoid referring to figure “8” in the first paragraph of the article unless figure 8 will again be referred to after the reference to figure 7. Please do not include figure captions as part of the figure. Do not put captions in “text boxes” linked to the figures. Do not put borders around the outside of your figures. Per IEEE Computer Society, please use the abbreviation “Fig.” even at the beginning of a sentence. Do not abbreviate “Table.” Tables are numbered numerically.

For journals that use print for publication, please verify with IEEE Computer Society that the journal you are submitting to does indeed accept color before submitting final materials. Do not use color unless it is necessary for the proper interpretation of your figures.

Figures (graphs, charts, drawing or tables) should be named fig1.eps, fig2.ps, etc. If your figure has multiple parts, please submit as a single figure. Please do not give them descriptive names. Author photograph files should be named after the author’s LAST name. Please avoid naming files with the author’s first name or an abbreviated version of either name to avoid confusion. If a graphic is to appear in print as black and white, it should be saved and submitted as a black and white file (grayscale or bitmap.) If a graphic is to appear in color, it should be submitted as an RGB color file.

Fig. 1. Magnetization as a function of applied field. Note that “Fig.” is abbreviated. There is a period after the figure number, followed by one space. It is good practice to briefly explain the significance of the figure in the caption.

Figure axis labels are often a source of confusion. Use words rather than symbols. As an example, write the quantity “Magnetization,” or “Magnetization M,” not just “M.” Put units in parentheses. Do not label axes only with units. As in Fig. 1, for example, write “Magnetization (A/m)” or “Magnetization (Am1),” not just “A/m.” Do not label axes with a ratio of quantities and units. For example, write “Temperature (K),” not “Temperature/K.” Table 1 shows some examples of units of measure.

Multipliers can be especially confusing. Write “Magnetization (kA/m)” or “Magnetization (103 A/m).” Do not write “Magnetization (A/m) 1,000” because the reader would not know whether the top axis label in Fig. 1 meant 16,000 A/m or 0.016 A/m. Figure labels should be legible, approximately 8 to 12 point type. When creating your graphics, especially in complex graphs and charts, please ensure that line weights are thick enough that when reproduced at print size, they will still be legible. We suggest at least 1 point.

6.3 Footnotes

Number footnotes separately in superscripts (Insert | Footnote)[footnoteRef:2]. Place the actual footnote at the bottom of the column in which it is cited; do not put footnotes in the reference list (endnotes). Use letters for table footnotes (see Table 1). Please do not include footnotes in the abstract and avoid using a footnote in the first column of the article. This will cause it to appear above the affiliation box, making the layout look confusing. [2: It is recommended that footnotes be avoided (except for the unnumbered footnote with the receipt date on the first page). Instead, try to integrate the footnote information into the text.]

TABLE 1
Units for Magnetic Properties

Statements that serve as captions for the entire table do not need footnote letters.

aGaussian units are the same as cgs emu for magnetostatics; Mx

=

maxwell, G = gauss, Oe = oersted; Wb = weber, V = volt, s = second, T = tesla, m = meter, A = ampere, J = joule, kg = kilogram, H = henry.

6.4 Lists

The IEEE Computer Society style is to create displayed lists if the number of items in the list is longer than three. For example, within the text lists would appear 1) using a number, 2) followed by a close parenthesis. However, longer lists will be formatted so that:

1. Items will be set outside of the paragraphs.

2. Items will be punctuated as sentences where it is appropriate.

3. Items will be numbered, followed by a period.

6.5 Theorems and Proofs

Theorems and related structures, such as axioms corollaries, and lemmas, are formatted using a hanging indent paragraph. They begin with a title and are followed by the text, in italics.

Theorem 1. Theorems, corollaries, lemmas, and related structures follow this format. They do not need to be numbered, but are generally numbered sequentially.

Proofs are formatted using the same hanging indent format. However, they are not italicized.

Proof. The same format should be used for structures such as remarks, examples, and solutions (though these would not have a Q.E.D. box at the end as a proof does). 

7 End Sections

7.1 Appendices

Appendices, if present, appear online as supplemental material. In the event multiple appendices are required, they will be labeled “Appendix A,” “Appendix B, “ etc.

IEEE Computer Society Transactions accepts supplemental materials for review with regular paper submissions. These materials may be published on our Digital Library with the electronic version of the paper and are available for free to Digital Library visitors. Please see our guidelines below for file specifications and information. Any submitted materials that do not follow these specifications will not be accepted. All materials must follow US copyright guidelines and may not include material previously copyrighted by another author, organization or company. More information can be found at

http://www.computer.org/portal/web/peerreviewjournals/author#supplemental

.

7.2 Acknowledgments

The preferred spelling of the word “acknowledgment” in American English is without an “e” after the “g.” Use the singular heading even if you have many acknowledgments. Avoid expressions such as “One of us (S.B.A.) would like to thank … .” Instead, write “F. A. Author thanks … .” Sponsor and financial support acknowledgments are included in the acknowledgment section. For example: This work was supported in part by the US Department of Commerce under Grant BS123456 (sponsor and financial support acknowledgment goes here). Researchers that contributed information or assistance to the article should also be acknowledged in this section. Also, if corresponding authorship is noted in your paper it will be placed in the acknowledgment section. Note that the acknowledgment section is placed at the end of the paper before the reference section.

7.3 References

Unfortunately, the Computer Society document translator cannot handle automatic endnotes in Word; therefore, type the reference list at the end of the paper using the “References” style. See the IEEE Computer Society’s style for reference formatting at:

http://www.computer.org/portal/web/publications/style_refs

. The order in which the references are submitted in the manuscript is the order they will appear in the final paper, i.e., references submitted nonalphabetized will remain that way.

Please note that the references at the end of this document are in the preferred referencing style. Within the text, use “et al.” when referencing a source with more than three authors. In the reference section, give all authors’ names; do not use “et al.” Do not place a space between an authors’ initials. Papers that have not been published should be cited as “unpublished” [4]. Papers that have been submitted or accepted for publication should be cited as “submitted for publication” [5]. Please give affiliations and addresses for personal communications [6].

Capitalize all the words in a paper title. For papers published in journals not published in English, please give the English citation first, followed by the original foreign-language citation [7].

7.4 Additional Formatting and Style Resources

Additional information on formatting and style issues can be obtained in the IEEE Computer Society Style Guide, which is posted online at:

http://www.computer.org/portal/web/publications/styleguide

. Click on the appropriate topic under the Special Sections link.

8 Conclusion

Although a conclusion may review the main points of the paper, do not replicate the abstract as the conclusion. A conclusion might elaborate on the importance of the work or suggest applications and extensions. Authors are strongly encouraged not to reference multiple figures or tables in the conclusion—these should be referenced in the body of the paper.

Acknowledgment

The authors wish to thank A, B, C. This work was supported in part by a grant from XYZ.

References

[1] J.S. Bridle, “Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition,” Neurocomputing—Algorithms, Architectures and Applications, F. Fogelman-Soulie and J. Herault, eds., NATO ASI Series F68, Berlin: Springer-Verlag, pp. 227-236, 1989. (Book style with paper title and editor)

[2] W.-K. Chen, Linear Networks and Systems. Belmont, Calif.: Wadsworth, pp. 123-135, 1993. (Book style)

[3] H. Poor, “A Hypertext History of Multiuser Dimensions,” MUD History, http://www.ccs.neu.edu/home/pb/mud-history.html. 1986. (URL link *include year)

[4] K. Elissa, “An Overview of Decision Theory,” unpublished. (Unpublished manuscript)

[5] R. Nicole, “The Last Word on Decision Theory,” J. Computer Vision, submitted for publication. (Pending publication)

[6] C. J. Kaufman, Rocky Mountain Research Laboratories, Boulder, Colo., personal communication, 1992. (Personal communication)

[7] D.S. Coming and O.G. Staadt, “Velocity-Aligned Discrete Oriented Polytopes for Dynamic Collision Detection,” IEEE Trans. Visualization and Computer Graphics, vol. 14,  no. 1,  pp. 1-12,  Jan/Feb  2008, doi:10.1109/TVCG.2007.70405. (IEEE Transactions )

[8] S.P. Bingulac, “On the Compatibility of Adaptive Controllers,” Proc. Fourth Ann. Allerton Conf. Circuits and Systems Theory, pp. 8-16, 1994. (Conference proceedings)

[9] H. Goto, Y. Hasegawa, and M. Tanaka, “Efficient Scheduling Focusing on the Duality of MPL Representation,” Proc. IEEE Symp. Computational Intelligence in Scheduling (SCIS ’07), pp. 57-64, Apr. 2007, doi:10.1109/SCIS.2007.367670. (Conference proceedings)

[10] J. Williams, “Narrow-Band Analyzer,” PhD dissertation, Dept. of Electrical Eng., Harvard Univ., Cambridge, Mass., 1993. (Thesis or dissertation)

[11] E.E. Reber, R.L. Michell, and C.J. Carter, “Oxygen Absorption in the Earth’s Atmosphere,” Technical Report TR-0200 (420-46)-3, Aerospace Corp., Los Angeles, Calif., Nov. 1988. (Technical report with report number)

[12] L. Hubert and P. Arabie, “Comparing Partitions,” J. Classification, vol. 2, no. 4, pp. 193-218, Apr. 1985. (Journal or magazine citation)

[13] R.J. Vidmar, “On the Use of Atmospheric Plasmas as Electromagnetic Reflectors,” IEEE Trans. Plasma Science, vol. 21, no. 3, pp. 876-880, available at http://www.halcyon.com/pub/journals/21ps03-vidmar, Aug. 1992. (URL for Transaction, journal, or magzine)

[14] J.M.P. Martinez, R.B. Llavori, M.J.A. Cabo, and T.B. Pedersen, “Integrating Data Warehouses with Web Data: A Survey,” IEEE Trans. Knowledge and Data Eng., preprint, 21 Dec. 2007, doi:10.1109/TKDE.2007.190746.(PrePrint)

First A. Author All biographies should be limited to one paragraph consisting of the following: sequentially ordered list of degrees, including years achieved; sequentially ordered places of employ concluding with current employment; association with any official journals or conferences; major professional and/or academic achievements, i.e., best paper awards, research grants, etc.; any publication information (number of papers and titles of books published); current research interests; association with any professional associations. Author membership information, e.g., is a member of the IEEE and the IEEE Computer Society, if applicable, is noted at the end of the biography.

Second B. Author Jr. biography appears here.

Third C. Author biography appears here.

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The purpose of the project is to learn how to formulate a problem statement or research question, determine how to best find a solution to the stated problem or answer to the research question, do that and then develop a final written report and presentation. The project is team-based or individual, I leave the choice to you. Individual grades will include points for how well they contributed to the team effort.

The course project has three (3) deliverables:

· Project proposal,

· Presentation and,

· Final Report

Each of these deliverables will be described in the paragraphs below. As an overview, each team will select a company, organization, or industry to target as their focus. Collect some data that will allow you to develop a case study to address a problem. Each case study presents a situation, challenge or problem the company, organization, or industry has had or is having. The primary objective of the course project is to determine how analytics could or can help the respective company address the situation or overcome the challenge or problem it is facing.

 

        To do this each team must review the case study, formulate a problem statement or research question as appropriate, and then identify the appropriate analytics methods or techniques to complete an analysis where possible.

          
Part 1:
 Each team must develop a proposal as described below consisting of 0% of the grade. Proposals refer to you creating the idea for your project. 

          
Part 2:
 Last, the team should develop their presentation as described below consisting of 50% of the grade. Final presentations due date refer to Canvas.

          Part 3:
 The third piece to the final project is the final written report as described below consisting of 50% of the grade. Final Reports consisting of 40% due date refer to Canvas.

1. Project Proposal

The project proposal is intended to introduce the company and its situation, problem or challenge. It should include all the relevant information for that introduction. The proposal should try to answer the following questions:

· What is the problem you are trying to solve or question you are trying to answer?

· What data do you need?

· What work do you plan to do in the project?

· Which algorithms/techniques/models do you plan to use/develop? Be as specific as you can.

· How will you evaluate what you’ve done?

· What do you expect to submit/accomplish by the end of the project?

Proposal Requirements:

· 1-2 pages

· 12 pt font

· Times New Roman.

· Word or pdf.

· Double Spacing.

· APA formatting

.

                    

2. Presentation

By the time your presentation is due you should have completed at least 90% of your project work. The presentation can serve as a draft of your final report but without your final analysis and results, but I do suggest having at least test results of a model. You should include at least the following in your presentation:

· What the problem is that you are trying to solve or question you are trying to answer.

· All relevant background information including any relevant literature you have/will use.

· The overall process you will follow for the entire project.

· A description of your data including how you obtained it.

· A description of any relevant, interesting exploratory data analyses.

· A description of the methods/techniques/tools/algorithms you have/will use to complete the project. Include test results if applicable.

· A description of the challenges you have had working on the project.

· A discussion of the parts of the project that have been completed.

· A discussion of the parts of the project that remain to be completed.

· A discussion of how you will finish the final project report and presentation.

Presentation Requirements:

· 10 slides minimum

· ppt

· APA formatting

 

3. Final Project Report

The final report and presentation should cover virtually everything about the project. It should cover the situation, problem or challenge that required attention, the relevant background, related work, data, and technical details of the analysis, conclusions and possible directions for future work. It is recognized that not all of the following sections will pertain to each report. However, it is strongly recommended that these section topics be used as a guideline for your final project reports. Final presentations can follow your final report in text and graphical content.

Introduction, motivation and general description of the situation, problem or challenge.

· Following the proposal and status report, what is the situation, problem or challenge you are addressing?

· What preliminary examination leads you to believe analytics could help?

· What are the shortcomings of the current work/analysis that analytics could help with?

Related work.

· Provide a thorough background for the project; e.g. about the company, about the situation, problem or challenge, about other companies that have undergone similar situations, problems or challenges and how they handled them or did not, etc.

· How does this project relate to other work that has been done on this situation, problem or challenge?

Data

· Give a complete description of the data you use during the project, including any you reject.

· Provide the source(s) of your data.

· Provide a detailed description of your data.

· Provide any exploratory data analyses you complete.

Technical Approach

· Give a detailed description of the process for your entire project.

· Given a detailed description of your approach to the analytics you have proposed to use including any algorithms, methods, tools or techniques. You do not have to describe well-known approaches themselves, e.g. linear regression. You do have to describe how you applied the approach you used.

Test and evaluation

· Describe how you test your approach to ensure that it is valid.

· Discuss the validity of your approach.

· Describe how you will evaluate your results and/or conclusions including any specific metrics, output data, completed analyses, etc.

· Discuss the baseline you will use to compare your results to.

· Discuss how well your approach worked to address the situation or challenge, solve the problem or answer the research question.

· Discuss any potential future work. For example, if you were not able to resolve the situation or problem or answer the research question what will it take to do so? What else needs to be done?

· Evaluate and report whether or not someone unfamiliar with your work could accurately replicate it.

Written work and Presentation Style

· Written work will be graded using the rubric provided.

· Presentation style will be graded on comprehensiveness and inclusiveness, as well as using the rubric provided.

Final Report Requirements:  

· Refer to ANLY_500_Report_Formatting

Grading Guidelines for Deliverables:

25%

25%

Critical Elements

Exemplary (100%)

Proficient (90%)

Needs Improvement (70%)

Not Evident (0%)

Value

Data Source

Business Value

Meets “Proficient” criteria and provides relevant examples or in-depth analysis of the data to support the explanation

Logically describes the business value of the available data and data sources for the organization

Describes the business value of the available data and data sources for the organization, but with gaps in logic

Does not describe the business value of the available data and data sources for the organization

25%

Application

Meets “Proficient” criteria and provides relevant examples to support business value

Accurately explains how the phases of the methodology will enable proper execution of the data solution

Explains how the phases of the methodology will enable proper execution of the data solution, but the explanation contains errors or omissions

Does not explain how the phases of the methodology will enable proper execution of the data solution

Analytic Structure

Selection

Meets “Proficient” criteria and provides relevant examples to support the defense of the structure

Logically defends how the selected structure could provide support, benefits, and value for the organization

Defends how the selected structure could provide support, benefits, and value for the organization, but with gaps in logical application to the organization

Does not defend how the selected structure could provide support, benefits, and value for the organization

Articulation of

Tool Selection

Meets “Proficient” criteria and includes a comparison of the benefits of the selected tool over other potential options

Logically defends how the selected tool can produce analysis and reporting that could provide support, benefits, and value for the organization

Defends how the selected tool can produce analysis and reporting that could provide support, benefits, and value for the organization, but with gaps in logical application to the organization

Does not defend how the selected tool could provide support, benefits, and value for the organization

15%

Additional Data

Sources

Meets “Proficient” criteria and explanation of added value is qualified with relevant, real-world examples

Accurately explains how additional internal or external data sources may add further value to the organization as supported by evidence

Explains how additional internal or external data sources may add further value to the organization, but explanation is not accurate or not supported

Does not explain how additional internal or external data sources may add further value to the organization

10%

 

Grading Rubric for Final Project:

· Presentation = 50%

· Final Report = 50%

· Total = 100%

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