Question Description
I’m working on a statistics writing question and need a sample draft to help me understand better.
Assignment: Estimating Software Development Workloads
Last week, you examined how operating system, database management system, and programming language affected work load per function point. This would provide useful information for project management, but there is not yet enough information to get a work load estimated based on function point analysis. This week, you will utilize regression analysis to develop this estimate.
First, you will investigate the overall relationship between work hours and point analysis. Start in Excel which is great for graphing and basic analysis.
 Create a scatter plot in Excel plotting work hours on the yaxis and function point count on the xaxis. Copy this chart to your overall document. Explain what trends you notice looking at the chart.
 Now, you can investigate the trend with regression and correlation. Right click on one of the data points on your graph and select Add Trendline.
 Consider which trends might be logical for this situation. A linear trend would mean every function point required an average number of work hours to complete. Exponential would mean that each function point in a large project would take more time than in a small project. Logarithmic would mean that each function in a large project would take less time than in a small project. Polynomial or Power would indicate a complex dependence. Which of these do you think might model this situation? Why?
 Select the options to Display Equation on chart and Display Rsquared value on chart. The Rsquare value describes how well the trend line fits the data. Click through the different regression types and find which has the best (highest) Rsquared value. Identify the regression type and record the equation and the Rsquared value in your overall document.
 If you made your polynomial higher order, the fit would get even better, but that does not necessarily make it a good model. The complex relationship does not logically fit the situation. A linear relationship is more logically explained. Find the best fit linear equation and record it along with the Rsquare value. What do the slope and intercept each tell you about work hours and function points.
 Presumably, a project with no function points would not require any work hours. You can model that situation by going back into your linear trend line settings and selecting Set Intercept = 0. Record the resulting equation and Rsquared value. What does this tell you about the relationship between work hours and function points?
 Although you now have a model for determining work hours based on the number of function points, it is not the best model possible. You know from your previous analysis that there are significant differences for different operating system, database management system, and language. Taking these into account will support a better model. Describe how you might take these nominal variables into account in creating a better model.
 Estimate how many workhours would be required at this facility to complete a 1000 function pointproject on Unix, IDMS, and Cobol. Explain how you arrived at this estimate.Describe how that estimate could be useful to the facility.
 For a project done atanother facility by another group, would your estimate based on results at thisfacility hold true? Why or why not? What factors might influence thecomparability of two software teams?
***What the professor is grading on…
Assignment: Estimating Software Development Workloads 
STAT 2
Article: Document: 
Media:

Developing Criteria Deficient Criteria Unsatisfactory Criteria Not Submitted
Element 1: Scatter Plot and Point Analysis Plot
Points:
10 (10.00%)
Student accurately creates and labels a scatter plot of work hours vs. point analysis and thoroughly explains general trends evident in the work hours vs. point analysis plot. There are no errors.
Points:
9.5 (9.50%)
Student accurately creates and labels a scatter plot of work hours vs. point analysis and thoroughly explains general trends evident in the work hours vs. point analysis plot. There are one or two minor errors or details missing.
Points:
8.5 (8.50%)
Student creates and labels a scatter plot of work hours vs. point analysis and explains some general trends evident in the work hours vs. point analysis plot. There are some errors or details missing.
Points:
7.5 (7.50%)
Student creates and labels a scatter plot of work hours vs. point analysis and explains at least one general trend evident in the work hours vs. point analysis plot. There are several errors or details missing.
Points:
6.5 (6.50%)
Student inaccurately creates and labels a scatter plot of work hours vs. point analysis and/or does not explain some general trends evident in the work hours vs. point analysis plot.
Points:
2.5 (2.50%)
Student provided an incomplete or cursory description that does not directly address this element and/or meet minimal requirements.
Points:
0 (0.00%)
Student did not submit this element.
Element 2: Regression Model and BestFit
Points:
15 (15.00%)
Student provides a thorough and detailed explanation defending which regression most logically models this situation and thoroughly explains which regression has the best fit based on rsquared value. Several and examples support thinking.
Points:
14.25 (14.25%)
Student provides a detailed explanation defending which regression most logically models this situation and thoroughly explains which regression has the best fit based on rsquared value. Several or examples support thinking. There are one or two minor errors.
Points:
12.75 (12.75%)
Student provides an explanation with some details defending which regression most logically models this situation and explains with some details which regression has the best fit based on rsquared value. Some resources or examples support thinking.
Points:
11.25 (11.25%)
Student provides an explanation with few details defending which regression most logically models this situation and explains with few details which regression has the best fit based on rsquared value. Few resources or support thinking. Many details are missing or lack clarity.
Points:
9.75 (9.75%)
Student provides a cursory or incomplete explanation with vague or missing details defending which regression most logically models this situation and/or explains with vague or missing details which regression has the best fit based on rsquared values. There are no examples or sources to support thinking.
Points:
3.75 (3.75%)
Student provided an incomplete or cursory description that does not directly address this element and/or meet minimal requirements.
Points:
0 (0.00%)
Student did not submit this element.
Element 3: Linear Model and Interpretation
Points:
15 (15.00%)
Student finds the best fit linear equation, gives rsquared value, and provides a thorough and detailed explanation interpreting both the slope and intercept in terms of the model. There are no errors.
Points:
14.25 (14.25%)
Student finds the best fit linear equation, gives rsquared value, and provides a detailed explanation interpreting both the slope and intercept in terms of the model. There are one or two minor errors.
Points:
12.75 (12.75%)
Student finds the best fit linear equation, gives rsquared value, and provides an explanation interpreting both the slope and intercept in terms of the model. There are some errors or details missing.
Points:
11.25 (11.25%)
Student finds the best fit linear equation and gives rsquared value with equation and interprets this equation with respect to the model but does not address slope and intercept. There are significant errors or details missing.
Points:
9.75 (9.75%)
Student provides a linear model as either a graph or equation but does not interpret the model. There are significant errors or details missing.
Points:
3.75 (3.75%)
Student provided an incomplete or cursory description that does not directly address this element and/or meet minimal requirements.
Points:
0 (0.00%)
Student did not submit this element.
Element 4: Origin Model
Points:
15 (15.00%)
Student finds the best fit linear equation with zero intercept, gives rsquared value, and provides a thorough and detailed explanation interpreting both the slope and intercept in terms of the model. There are no errors.
Points:
14.25 (14.25%)
Student finds the best fit linear equation with zero intercept, gives rsquared value, and provides a detailed explanation interpreting both the slope and intercept in terms of the model. There are one or two minor errors.
Points:
12.75 (12.75%)
Student finds the best fit linear equation with zero intercept, gives rsquared value, and provides an explanation interpreting both the slope and intercept in terms of the model. There are some errors or details missing.
Points:
11.25 (11.25%)
Student finds the best fit linear equation with zero intercept and gives rsquared value with equation and interprets this equation with respect to the model but does not address slope and intercept. There are significant errors or details missing.
Points:
9.75 (9.75%)
Student provides a linear model as either a graph or equation but does not interpret the model. There are significant errors or details missing.
Points:
3.75 (3.75%)
Student provided an incomplete or cursory description that does not directly address this element and/or meet minimal requirements.
Points:
0 (0.00%)
Student did not submit this element.
Element 5: Improving Model
Points:
15 (15.00%)
Student provides a thorough and detailed explanation describing strategies for creating a better model utilizing the nominal variables. Several sources and examples support thinking.
Points:
14.25 (14.25%)
Student provides a thorough and detailed explanation describing strategies for creating a better model utilizing the nominal variables. Several sources or examples support thinking. There are one or two minor errors.
Points:
12.75 (12.75%)
Student provides an explanation with some details describing strategies for creating a better model utilizing the nominal variables. Some sources or examples support thinking.
Points:
11.25 (11.25%)
Student provides an explanation with few details describing strategies for creating a better model utilizing the nominal variables. Few sources or examples support thinking.
Points:
9.75 (9.75%)
Student provides a cursory or incomplete explanation with vague or missing details describing strategies for creating a better model utilizing the nominal variables. No sources or examples support thinking.
Points:
3.75 (3.75%)
Student provided an incomplete or cursory description that does not directly address this element and/or meet minimal requirements.
Points:
0 (0.00%)
Student did not submit this element.
Element 6: Data Transferability
Points:
15 (15.00%)
Student provides a thorough and detailed explanation of whether or not data from one software team is transferable to another and thoroughly explains factors that might effect this application. Several sources and examples support thinking.
Points:
14.25 (14.25%)
Student provides a thorough and detailed explanation of whether or not data from one software team is transferable to another and thoroughly explains factors that might effect this application. Several sources or examples support thinking. There are one or two minor errors.
Points:
12.75 (12.75%)
Student provides an explanation with some details of whether or not data from one software team is transferable to another and explains some factors that might effect this application. Some sources or examples support thinking.
Points:
11.25 (11.25%)
Student provides an explanation with few details of whether or not data from one software team is transferable to another and explains at least two factors that might effect this application. Few sources or examples support thinking.
Points:
9.75 (9.75%)
Student provides a cursory or incomplete explanation with vague or missing details of whether or not data from one software team is transferable to another and/or explains few if any factors that might effect this application. No sources or examples support thinking.
Points:
3.75 (3.75%)
Student provided an incomplete or cursory description that does not directly address this element and/or meet minimal requirements.
Points:
0 (0.00%)
Student did not submit this element.
Element 7: Form and Style
Points:
15 (15.00%)
Student submission is fully consistent with Academic Writing Expectations, wellorganized, errorfree, and demonstrates a thorough understanding of the audience and purpose of writing. The submission follows the APA standard for intext citation and reference of sources including the use of page numbers or URLs.
Points:
14.25 (14.25%)
Student submission is fully consistent with Academic Writing Expectations, wellorganized, and demonstrate a thorough understanding of the audience and purpose of writing. There are one or two grammatical or spelling errors. The submission follows the APA standard for intext citation and reference of sources including the use of page numbers or URLs.
Points:
12.75 (12.75%)
Student submission adheres to the Academic Writing Expectations and is wellorganized. There are a few grammatical or spelling errors. All included references in the submission follow the APA standard for intext citation and reference of sources including the use of page numbers or URLs.
Points:
11.25 (11.25%)
Student submission inconsistently applies the Academic Writing Expectations. There are several grammatical or spelling errors. Included references in the submission inconsistently follow the APA standard for intext citations and reference of sources.
Points:
9.75 (9.75%)
Student submission does not adhere to the Academic Writing Expectations. There are numerous grammatical or spelling errors. Included references in the submission do not follow the APA standard for reference of sources.
Points:
3.75 (3.75%)
Student provided an incomplete or cursory description that does not directly address this element and/or meet minimal requirements.
Points:
0 (0.00%)
Student did not submit this element.