LABS
LABS
—
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?
“`{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?
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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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72.5079727425428 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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46.994483410107 | 47.1793791441458 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
54.9882553143553 | 49.9612944208481 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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:
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 |
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 |
|||
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 |
Does not defend how the
selected tool could provide support, benefits, and value for |
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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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
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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
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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
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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
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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.
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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:
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. 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:
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%