Abstract:
A system and method for determining the stability of a project. One embodiment includes computing at least two project progress parameters of a project for numerically describing elements of the project. Regression parameters are computed based upon the project progress parameters and correlation coefficients are computed utilizing the regression parameters. The correlation coefficients describe the strength of the correlation of the project progress parameters for indicating the stability of the project as project develops.

Description:
FIELD OF THE INVENTION 
     The present invention relates generally to development project applications, and, more particularly, but not by way of limitation, to the application of statistical analysis techniques to establish metrics for use in assessing progress of the project applications. 
     BACKGROUND OF THE INVENTION 
     In general, metrics have been used to perform such tasks as business process assessment, software design analysis, and software complexity analysis. For example, metrics used for process assessment are typically focused on measuring complexity and tracking process execution times. Because there are limited metrics available that provide reliable tools for assessment of tasks, such as the development of business requirements specifications, the nature of business requirements specifications and other documentary summary data for project applications has made identification of meaningful metrics problematic. Moreover, the challenges of assessing the impacts of project components on the overall progress add to the aforementioned challenges created by documentary summary data. It should be understood, of course, that each project team uses a different developmental approach for defining a requirements specification. For example, some use waterfall, while others use spiral developmental approaches. Regardless of the developmental approach, many project developers use writing methods learned early in their lives. Fundamentally, project developers develop an outline, followed by the content that brings the outline to life. However, at the most fundamental level, the structure of most requirements specifications consists of branches  205   a – 205   f , collectively  205 , and leaves  210   a – 210   f , collectively  210 , as illustrated in  FIG. 2A . 
     A leaf is, unambiguously, a subsection of a branch. The IEEE 1998-830 standard, a widely used requirements template, provides an example of this structure. For example, section “1.1.1” is a leaf of section “1.1”, assuming that there is no section “1.1.1.1”. Although not shown in  FIG. 2A , each section has a title, e.g., a natural language structure would use a function name, an object oriented structure would use an object name, a requirements specification document would use a section title, and an accounting structure would use an account name. Furthermore, each branch  205  and leaf  210  may also include content, such as text, numbers, images, etc., that define the specification document, accounting ledger, software program, or other document, for example. 
     Consequently, project managers tasked with managing a requirements specification, as previously stated, may lack reliable methods, beyond raw experience, to successfully monitor the progress of a project. As such, the issue of team synergy (where the individual goal-directed actions are focused on the common end result, with a clear understanding of current reality in relation to the end result) and the impacts of teamwork on project progress, regardless of the project nature, has been unmeasurable, thus far. Hence, the inability to measure progress of the project makes judging success extremely difficult. 
     SUMMARY OF THE INVENTION 
     To remedy the deficiencies of determining progress of project applications, the principles of the present invention provide a system and method for employing statistical analysis techniques to quantify the success of project applications by establishing metrics for use in assessing progress of a project application. The statistical analysis techniques use parameter dependencies to provide an operator of a project the ability to assess the progress of the project application. Particularly, regression analysis provides an appropriate computational solution to quantify the success of the project application. Additionally, correlation coefficients derived from the regression analysis provide for determining the strength of the correlation of the parameter dependencies (e.g., age of branch and number of branch modifications). The correlation coefficients provide the operator with confidence that the statistical analysis is reliable. 
     One embodiment of the present invention provides a system and method for determining the stability of a project. This embodiment includes computing at least two project progress parameters of a project for numerically describing elements of the project. Regression parameters are computed based upon the project progress parameters and correlation coefficients are computed utilizing the regression parameters. The correlation coefficients describe the strength of the correlation of the project progress parameters for indicating the stability of the project as it develops. 
     The project progress parameters may include, for example, total number of leaves, number of modifications performed on the branches, number of modifications performed on the leaves, average age of leaves in the project, and average age of branches in the project. The project progress parameters are generated from performing statistical analysis on collected project data and used as parameters in mathematical equations to compute the regression parameters. These mathematical equations may include normal equations used in regression analysis, slope equations of a regression model, intercept equations of the regression model, and correlation coefficient equations of the regression model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of the principles of the present invention and the scope thereof is more readily appreciated by reference to the following Detailed Description of the presently-preferred embodiments of the present invention and to the appended claims when taken in conjunction with the accompanying Drawings wherein: 
         FIG. 1  is an exemplary network based system in which the principles of the present invention operate; 
         FIG. 2A  is an exemplary block diagram of the project data including branches and leaves; 
         FIG. 2B  is an exemplary output from a project summary generation process according to  FIG. 4  that describes the block diagram of  FIG. 2A ; 
         FIG. 3  is an exemplary flow diagram of a process for determining the stability of a project according to the principles of the present invention; 
         FIG. 4  is an exemplary flow diagram of the project summary data record generation process according to the principles of the present invention; 
         FIG. 5  is an exemplary flow diagram for analyzing summary data records as produced by the flow diagram  400  of  FIG. 4 ; 
         FIG. 6  is an exemplary flow diagram of a long-term regression analysis process as produced by the flow diagram  500  of  FIG. 5 ; 
         FIG. 7  is a plot comparison of hypothesized models versus actual data collected from an example project resulting from the exemplary flow diagram  500  of  FIG. 5 ; 
         FIG. 8  is a plot comparison of a hypothesized model versus an actual correlation coefficient from regressing the branch modifications (BM) on the age of the branch (BA) of the example project according to  FIG. 6 ; 
         FIG. 9  is a plot comparison of a hypothesized model versus an actual correlation coefficient from regressing the leaf modifications (LM) on the age of the leaf (LA) of the example project according to  FIG. 6 ; and 
         FIG. 10  is a plot comparison of a hypothesized model versus an actual correlation coefficient from regressing the number of leaves (LN) produced per day to number of branches (BN) produced per day of the example project according to  FIG. 6 . 
     
    
    
     DETAILED DESCRIPTION 
     Although the present invention is open to various modifications and alternative constructions, a preferred exemplary embodiment that is shown in the above-referenced drawings is described herein in detail. It is to be understood, however, that there is no intention to limit the invention to the particular forms disclosed. One skilled in the art can recognize that there are numerous modifications, equivalences and alternative constructions that fall within the spirit and scope of the invention as expressed in the claims. 
     Data collected from a project is used to perform regression analysis to determine information about status of the project (i.e., stability of a project&#39;s organization and content). Additionally, regression analysis and hypothesis testing are used to describe actual progress of the project and to evaluate that progress in relation to expected progress. These techniques allow project managers to assess contributions of individual project team members and to forecast possible need for additional or different project team resources. 
     Referring to  FIG. 1 , there is illustrated an exemplary network based system  100  having a server  105 . The server  105  includes a memory  110 , a modem  115 , and a microprocessor  120 . The microprocessor  120  collects project data by reading a data file  125  or a database  130  through a data bus  135 . The project data may be a requirements document, specification document, proposal document, request for proposal document, sales performance, manufacturing process, accounting system, distribution system, or software development, for example. The project data is used by the microprocessor  120  during execution of project analysis tools  140 , including project process assessment tools  141  and statistical analysis tools  142 , to generate statistical data such as project progress parameters, regression parameters, and correlation coefficients. Alternatively, the microprocessor  120  can collect project data from a communication network  145  via data packets  150  transmitted over a wired or wireless interface  155 . 
     It should be understood, of course, that the aforementioned database can be represented as a single database  130  or as separate databases  130   a ,  130   b , and  130   c . The databases  130   a – 130   c  are created and updated using the project analysis tools  140  and are used for project progress analysis. For example, the project process assessment tools  141  are used to parse the requirements document data. Subsequently, the project process assessment tools  141  causes raw project data to be stored in the project summary data records database  130   a , thereby creating or updating a repository of daily project summary data records  160   a , shown as a single project summary data record  160   b  in  FIG. 2B . Moreover, the statistical analysis tools  142  are used to perform statistical analysis on the project summary data records  160   a  contained in database  130   a . Accordingly, project progress parameters are generated and the project process assessment tools  141  are used to store the project progress parameters in database  130   b . Thus, a repository of daily project progress parameters  165  is created or updated, shown as a single record in Table 1. Furthermore, the statistical analysis tools  142  are used to perform regression analysis on the project progress parameters  165  contained in database  130   b . Thus, regression parameters are generated and used to populate regression models. Following, the project process assessment tools  141  are used to store the regression models in database  130   c , thereby creating or updating a repository of long-term regression analysis results, shown as a single record in Table 2. 
     It should be further understood that a user can operate within a system environment using a terminal  170  coupled to the server  105  to implement the principles of the present invention. Alternately, the user can operate outside of the system environment using a remote terminal  175  and server  180  connected to the communication network  145  to implement the principles of the present invention. 
     Further referring to  FIG. 2A , there is illustrated an exemplary block diagram  200   a  of the project data organized in a representative tree structure including branches  205  and leaves  210 , in which the branches  205  represent structure components of the requirements document and the leaves  210  represent content components of the requirements documents. Additionally, an alternate embodiment of the present invention operates on documents configured in a content markup fashion. Particularly, markup defines a hierarchical structure for text or data. In the case of XML (eXtensible Markup Language), and other markup languages, the markup defines a tree structure. Consequently, markup is concerned with the logical structure and content of the text or data, e.g., to indicate sections, subsections, and headers in a document similar to the branch and leaf structure. For example, an XML document would be formatted in the following manner: 
     
       
         
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 &lt;document&gt; 
               
               
                   
                 &lt;header#&gt;&lt;1.1&gt;&lt;/header#&gt; 
               
               
                   
                 &lt;title&gt;&lt;title of paragraph 1.1&gt;&lt;/title&gt; 
               
               
                   
                 &lt;content&gt;&lt;Hello World!&gt;&lt;/content&gt; 
               
               
                   
                 &lt;/document&gt;. 
               
               
                   
                   
               
             
          
         
       
     
     Referring to  FIG. 2B , the expressiveness of the branch and leaf structures can be developed using an incremental approach. For example, for every branch  205   a ′– 205   f ′, collectively  205 ′, and leaf  210   a ′– 210   f ′, collectively  210 ′, there is included an object ID  215 , object number  220 , level in document  225 , branch or leaf entry  230 , date of creation  235 , number of modifications  240 , modification status  245 , user name  250 , and age of artifact  255 . Accordingly, leaf and branch metrics (i.e., graphics indicating analysis results generated from using regression statistics computed to assess progress of a project) provided by the principles of the present invention help project managers understand the progress of this development pattern. 
     Referring to  FIG. 3 , there is illustrated an exemplary flow diagram  300  of a process for determining the stability of a project, configured in accordance to the principles of the present invention. The process starts at step  305 . At step  310 , the project summary data records (e.g., branch and leaf data  205  and  210 ) are used to perform statistical analysis to compute project progress parameters, which numerically describe elements of the project. Basically, the project elements are branch and leaf objects contained within a “super artifact” as illustrated in  FIG. 2B . The project progress parameters represent various project characteristics and may be stored in a database. The project progress parameters may include: total number of branches, total number of leaves, number of modifications performed on the branches, number of modifications performed on the leaves, average age of leaves in the project, and average age of branches in the project, for example. 
     At step  315 , the project progress parameters are used to perform regression analysis to compute regression parameters. This process employs statistical equations used to perform regression analysis, i.e., normal equations (eqns. 1–3) used in regression analysis, including (i) slope (eqn. 4) of the regression model equation, (ii) intercept (eqn. 5) of the regression model equation, (iii) correlation coefficient (eqn. 6) of regression equation, and (iv) alternate equation for the correlation coefficient (eqn. 7) of regression, to compute the regression parameters, develop models of the project development process, and assess the strength of the relationships being analyzed with the metrics that have been established. These equations can be expressed as:
 
 S   XX   =Σ   i   2 −(Σ X   i ) 2   /n   (1)
 
 S   YY   ΣY   i   2 −(Σ Y   i ) 2   /n   (2)
 
 S   XY   =XY   i −(Σ X   i )(Σ Y   i )/ n   (3)
 
 b   1   =S   XY   /S   XX   (4)
 
 b   0   ={overscore (Y)}−b   1   {overscore (X)}   (5)
 
 R   2   =b   1   S   XY   /S   YY   (6)
 
     
       
         
           
             
               
                 
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                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     The regression parameters (i.e., slope, intercept, correlation coefficient) populate a third data record that includes statistical results indicative of the development of the project. At step  320 , the regression parameters are used to compute correlation coefficients. At step  325 , the process ends. 
     The correlation coefficients, when taken together with project summary data records, paint an informative picture about the stability of the project, i.e, the strength of the relationships between independent variables and dependent variables. Additionally, the correlation coefficients are used to populate regression models. Fundamentally, these regression models provide project managers with a means for comparing idealized or estimate curves with actual curves ( FIGS. 7–10 ) in order to adjust their tactical objectives appropriately. Principally, regression analysis is performed to facilitate daily project progress assessments as well as forecast the need for additional resources, whereas, long-term regression analysis is performed in order to accomplish long-term project assessment. 
     Referring to  FIG. 4 , there is illustrated an exemplary flow diagram  400  of the project summary data record or artifact generation process according to the principles of the present invention. The process starts at step  405 . At step  410 , project data is collected either by reading a project data file  125  or databases  130   a – 130   c , collectively  130 , on the server computer  105 . Alternately, the project data can be received from a communication network  145  via data packets  150 . At step  415 , the project data is parsed to extract pertinent data for generating branch and leaf data  205  and  210  (i.e., artifacts) to implement project progress analysis. The project data is summarized  160   b  as illustrated in  FIG. 2B  and can be used for the project analysis using regression analysis as described by equations (1–7). Particularly, artifacts can be used for the analysis of the project up to the day the analysis is performed. At step  420 , the artifacts are saved to a project summary data records database  130   a , which is a repository  160   a  updated with artifact objects (i.e., project summary data records). At step  425 , the process ends. 
     Referring to  FIG. 5 , there is illustrated an exemplary flow diagram  500  for analyzing summary data records as produced by the flow diagram  400  of  FIG. 4 . The process starts at step  505 . At step  510  project summary data records  160   a  are accessed from the project summary data records database  130   a . At step  515 , the project summary data records are used to perform statistical analysis. At step  520 , the project progress parameters are saved to a project progress parameters database  130   b , which is a repository  165  updated with project progress parameters. At step  525 , the process ends. Accordingly, Table 1 represents exemplary project progress parameters from performing statistical analysis on the project summary records  160   a . It should be understood that other desired project progress parameters could be generated. 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Project Progress Parameters 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 DATE; 
                 (27 7 2000) 
               
               
                   
                 AVG-BRANCH-AGE; 
                 665.665620 
               
               
                   
                 SD-BRANCH-AGE; 
                 29.875499 
               
               
                   
                 AVG-LEAF-AGE; 
                 667.192628 
               
               
                   
                 SD-LEAF-AGE; 
                 0 
               
               
                   
                 AVG-MODIFIED-BRANCHES; 
                 22.642857 
               
               
                   
                 SD-MODIFIED-BRANCHES; 
                 44.936891 
               
               
                   
                 AVG-MODIFIED-LEAVES; 
                 88.785714 
               
               
                   
                 SD-MODIFIED-LEAVES; 
                 180.132079 
               
               
                   
                 AVG-BRANCH- 
                 3.660910 
               
               
                   
                 MODIFICATIONS; 
               
               
                   
                 SD-BRANCH-MODIFICATIONS; 
                 4.892677 
               
               
                   
                 AVG-LEAF-MODIFICATIONS; 
                 1.827304 
               
               
                   
                 SD-LEAF-MODIFICATIONS; 
                 3.134201 
               
               
                   
                 TOTAL-BRANCHES; 
                 637 
               
               
                   
                 TOTAL-LEAVES; 
                 3179 
               
               
                   
                 AVG-BRANCHES; 
                 45.5 
               
               
                   
                 SD-BRANCHES; 
                 65.055893 
               
               
                   
                 AVG-LEAVES; 
                 227.071428 
               
               
                   
                 SD-LEAVES; 
                 326.246123 
               
               
                   
                   
               
             
          
         
       
     
     Referring to  FIG. 6 , there is illustrated an exemplary flow diagram  600  of a long-term regression analysis process as produced by the flow diagram  500  of  FIG. 5 . The process starts at step  605 . At step  610 , project progress parameters  165  (e.g., Table 1) are accessed from the project progress parameters database  130   b . At step  615 , the project progress parameters  165  are used to perform a long-term regression analysis. In particular, this process uses the project progress parameters  165  to compute regression parameters using equations (1–3) as previously described above to generate the long-term regression analysis data. At step  620 , the long-term regression analysis data is saved or updated to a long-term regression analysis database  130   c . The process ends at step  625 . Table 2 represents the resulting long-term regression parameters from performing the long-term regression analysis (step  615 ) on the project progress parameters  165 . 
     
       
         
               
             
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 Regression Parameters 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Regression Model: 
                 TOTAL-LEAVES-ON-TOTAL-BRANCHES 
               
               
                 slope 
                 22.3333333333333 
               
               
                 intercept 
                 −2494.66666666667 
               
               
                 correlation coefficient 
                 1.0 
               
               
                 Regression Model: 
                 AVG-LEAF-AGE 
               
               
                 slope 
                 2.25261845640354E−14 
               
               
                 intercept 
                 107.480733944954 
               
               
                 correlation coefficient 
                 2.11182980287832E−15 
               
               
                 Regression Model: 
                 AVG-BRANCH-AGE 
               
               
                 slope 
                 2.25261845640354E−14 
               
               
                 intercept 
                 186.142857142857 
               
               
                 correlation coefficient 
                 2.11182980287832E−15 
               
               
                 Regression Model: 
                 TOTAL-LEAVES-ON-TOTAL-BRANCHES 
               
               
                 slope 
                 4.19907168643631 
               
               
                 intercept 
                 −41.8422726491319 
               
               
                 correlation coefficient 
                 0.641106895479179 
               
               
                   
               
             
          
         
       
     
     The metrics described hereinafter allow project managers to monitor the operational progress of a project. Additionally, the 20 principles of the present invention analyze the progress of the project on a tactical and strategic basis. These terms (i.e., operational, tactical, strategic) are based on the systematic approach set forth in the U.S. military publication, entitled “A Tactical Evolution-FM 100-5” and have been modified for business purposes, as discussed in detail below. 
     In business terms, tactical progress is the management of resources to achieve tactical objectives, which summarily supports operational progress. For example, generally speaking, tactical progress can be defined as the type and amount of work completed each day. Thus, tactical objectives define the type and amount of work team members are expected to complete daily. Specifically, tactical objectives may be set which direct some team members to focus on branch work, and others to focus on leaf work. Based on the type of work, some team members may be directed to work on leaves in parallel, while other team members work serially on branches. 
     Moreover, in business terms, operational progress is the employment of resources to attain strategic objectives through the design, organization, integration, and execution of tactical measures. For example, generally speaking, operational progress can be defined as the summation of tactical progress on a weekly basis. Thus, operational objectives are based on branch and leaf metrics. Specifically, idealized curves (scaled for a specified schedule) are compared with actual curves and tactical objectives are adjusted appropriately. 
     Furthermore, in business terms, strategic progress is the art of managing tactical and operational progress to complete an engagement (e.g., a requirements specification) on time, within budget, and within some previously defined parameters. For example, generally speaking, strategic progress can be defined as the summation of operational progress. Thus, strategic objectives relate to cost, effort, and schedules. Specifically, the development of the project is monitored in relation to the strategic objectives, and operational and tactical adjustments are made accordingly. 
     Referring to  FIG. 7 , there is illustrated a plot comparison  700  of hypothesized models versus actual data collected from an example project resulting from the exemplary flow diagram  500  of  FIG. 5 . As discussed above, most project developers first construct a requirements specification outline before proceeding with defining the content of the sections within the outline. This pattern of development explains the popularity of templates (for example, IEEE 1998-830) that predefine the outline. Those skilled in the art recognize that it is very easy to spend too long perfecting an outline, or languish trying to find an outline that works for a given project. By examining the ratio of the number of branch modifications over the life of a project (BM) versus the average branch age in days (BA), project managers can assess the stability of the structure of project application, such as a requirements specification. 
     Logically, if a project team does not start with a template, a project manager should expect a fast growing population of young branches with a large number of modifications. Thus, the ratio  701  of the BM to BA initially increases as seen by curves  701   a  and  701   b  between days 1–3. However, at some point in time, the increase in BM slows as the project manager finds a structure that works for the problem that the project team specifies. Furthermore, as the project manager&#39;s satisfaction with the structure increases, the project team introduces fewer branches, leading to an increasing BA as the branch population ages. Thus, the ratio quickly heads towards zero. Curve  701   a  depicts the logical representation of this assumption or hypothesis. 
     For example, point S 1  represents the start of the project. Logically, point S 1  is initially undefined, as all the branches have an initial age of zero. Furthermore, point S 2  represents the point at which the project developer determines the specification structure suffices for the problem at hand with modifications. Finally, point S 3  represents the end of the project. 
     Examining the relationships between the points leads to additional comments and determinations a project manager can make about specification structure. Particularly, the area beneath the curve line between points S 1  and S 2  represents the energy required to build the structure. If the project developer uses a template estimated as appropriate for the problem, then the distance between points S 1  and S 2  should equal zero, enabling the project developer to simply add content without having to create structure. 
     As stated above, the area beneath the curve line between points S 2  and S 3  represents the energy or effort required to adjust the initial structure to match the actual appropriateness of the template to the problem. Accordingly, the smaller the area below the curve  701   a  between S 2  and S 3 , the more appropriate the initially chosen structure illustrates. To further assess the stability of the structure model, curve  701   b , representing actual data collected from the sample project discussed above, is included in the plot  700 . The curve  701   b  reflects the project team&#39;s initial estimate that the structure developed by day three was appropriate for the problem the project team faced. For example, each segment of the curve  701   b  with a positive slope represents the project team reassessing the actual appropriateness of the initial structure and adjusting the structure to better fit the problem. Interestingly, the dip between day 10 and day 11 represents an appendix (new structure component) to the project that the project team introduced late in the project. 
     Once project developers realize a structure that appears to work, they can progress with defining the content that ultimately defines the problem in detail. Unfortunately content definition is another task that often challenges project developers. While project developers often suffer from writer&#39;s block, a more common problem is understanding when to stop defining content. It should be understood that content for a requirements specification project is text and/or graphics, while content for a software application project is source code. 
     Due to the common problem of not understanding when to stop defining content, the energy expended on content definition is the primary contributor to slipping schedules. Therefore, by examining the ratio  702  of the number of leaf modifications over the life of a project (LM) versus the average leaf age in days (LA), project managers can assess content stability for a specification. 
     Logically, assuming the project team does not start with a template, a project manager expects an increasing population of new, young leaves with few modifications as the project team stabilizes the structure. Leaves may become branches or future branches during project development. Thus, at the beginning of the project, the ratio  702  of LM to LA gradually increases at a slightly faster rate than the ratio  701  of BM to BA because the focus is initially on structure. However, at some point after point S 2  described above, the project manager expects the project team to shift its focus from structure definition to content definition. At this point, generally speaking, the project developers introduce multiple leaves per branch. Moreover, at some point, the project team stops producing new content and works with project reviewers to ensure that the existing material is ready for endorsement. Curve  702   a  depicts the logical representation of this assumption. 
     For example, point C 1  represents the start of the project. Similar to structure stability, point C 1  is undefined as the average leaf age is zero. The ratio C 2 /S 2  represents the point at which the project developer determines that the specification structure suffices for the problem at hand and shifts focus to content. Furthermore, point C 3  represents the point at which the project manager and project reviewers agree that the content sufficiently represents the problem, but requires modifications. Finally, point C 4  represents the end of the project. 
     Examining the relationships between the points leads to additional comments and determinations a project manager can make about content. Particularly, the area under the curve between points C 2  and C 3  represents the energy or effort required to specify the problem detail (i.e., to add content to the structure of the project). It should, of course, be understood to those skilled in the art that, collectively, elicitation methods (e.g., induction), documentation methods, and requirements reuse work to reduce this area. 
     However, the area beneath the curve line between points C 3  and C 4  represents the energy or effort required to adjust (i.e., edit) the detail to appropriately define the problem detail. Clearly, employing review methods help to reduce this area. To further assess the stability of the content model, curve  702   b , representing actual data collected from the aforesaid sample project, is included in the plot  700 . The curve  702   b  reflects the project team&#39;s initial challenge to obtain a structure that enables managing a problem. Consequently, around day  10 , the project team realizes a structure and works long hours over the next few days in order to make up for the time lost establishing the structure. In effect, the project team uses the LM to LA ratio  702  to determine the proximity of points C 2  and C 3 , which the project team uses to ensure it realizes the delivery date and to maximize review time. 
     The description of content stability and structure stability, as discussed above, can be combined by examining the ratio  703  of the number of leaves for a given day (LN) to the number of branches for the same day (BN) in order to study production stability. Logically, a project manager expects an initial increase in the number of branches and future branches (initially appearing as leaves) as the structure becomes increasingly unstable. Thus, the ratio  703  of LN to BN hovers around a constant, approaching one as illustrated by curves  703   a  and  703   b . Once the project team feels the structure is appropriate, the project manager expects the content stability to increase as the project team focuses on creating leaves to capture problem detail. Thus, the ratio  703  of LN to BN sharply increases. After the project team develops the raw material and both the content and structure stability increase, the project manager expects the LN to BN ratio  703  to stabilize and remain constant. Curve  703   a  depicts the logical representation of these assumptions. 
     For example, point P1 represents the start of the project. As in the other models, point P1 is technically undefined because the number of branches and number of leaves both equal zero at the start of a project. Furthermore, ratio P2/S2 represents the point at which the content stabilizes and defining the content takes precedence. Moreover, ratio P3/C3 represents the point at which the specification contains enough material to represent the problem in the view of both the project team and the reviewers. Finally, point P4 represents the end of the project. 
     Examining the relationships between the points leads to additional comments and determinations a project manager can make about production stability. Particularly, the number of days between points P2 and P3 represents the rate at which the project team can identify the problem detail. 
     As discussed, the distance between points P2 and P3 represents the granularity of the branches. For example, the smaller the distance, the more finely grained the structure (i.e., negligible transition points), potentially decreasing a reviewer&#39;s ability to understand a specification. Alternatively, a very large distance represents a structure with coarse granularity (i.e., extensive transition points), also presenting a potential risk of lowering a reviewer&#39;s ability to understand a specification. To further assess the stability of the production model, curve  703   b , representing actual data collected from the sample project discussed above, is included in the plot  700 . The curve  703   b  reflects the project team&#39;s ability to produce the raw material required to specify the problem, however, the stability curves above, indicate that the material produced suffers from prolonged periods of instability. In other words, production was not the problem, but adjusting the material produced to meet the client&#39;s needs was a problem. Interestingly, the dip between day 10 and day 11 represents an appendix (new production component) to the project that the project team introduced late in the project, described in the structure stability section above. 
     It should, of course, be understood to those skilled in the art that the above-mentioned metrics taken alone may not provide individual power greater than the power provided by all of the metrics taken together. However, viewed together in the plot  700 , the metrics paint an informative picture about the progress of a project. 
     Referring to  FIG. 8 , there is illustrated a plot comparison of a hypothesized model versus an actual correlation coefficient from regressing the branch modifications (BM) on the age of the branches (BA) of the example project, according to  FIG. 6 . Based on the above discussion of the structure stability metric, it is understood that the number of modifications a project developer makes to a branch is dependent on the age of the branch. 
     Logically, if these dependencies exist, a project manager initially expects to see a strong negative correlation between the number of modifications to a branch and its age because the project developer makes a large number of modifications to a branch on the branch&#39;s first day of existence. Next, the project manager expects the correlation to head towards zero on its way to positive as the project developer makes more modifications. Eventually, the correlation heads back towards a strong negative correlation as the project developer stops making modifications to the aging branch. Accordingly, curve  800   a  depicts an idealized or hypothesized representation of these assumptions. Additionally, curve  800   b , representing the actual correlation between the number of branch modifications and the corresponding branch age, is included in plot  800  to further assess the appropriateness of the project team&#39;s initially asserted assumptions. 
     Referring to  FIG. 9 , there is illustrated a plot comparison of a hypothesized model versus an actual correlation coefficient from regressing the leaf modifications (LM) on the age of the leaf (LA) of the example project, according to  FIG. 6 . Logically, the correlation coefficient resulting from a regression analysis of the number of leaf modifications on the age of the leaf is similar to the correlation coefficients that accompany the structure stability metric, but is shifted to account for the delayed attention to content. Thus, a project manager expects to initially see a strong negative correlation between the number of modifications to a leaf and its age as the leaves and age grow together. Because the structure is the primary initial focus, project managers should expect an initially weaker correlation coefficient for content than structure. Once the structure stabilizes, the correlation coefficient for content shoots towards zero on its way to positive as the project developer shifts focus to content. Moreover, once the content stabilizes, project managers expect the correlation coefficient to dive negatively as leaves age, but experience fewer modifications. Accordingly, curve  900   a  depicts an idealized or hypothesized representation of these assumptions. Additionally, curve  900   b , representing the actual correlation between the number of leaf modifications and the corresponding leaf age, is included in plot  900  to further assess the appropriateness of the project team&#39;s initially asserted assumptions. 
     Referring to  FIG. 10 , there is illustrated a plot comparison of a hypothesized model versus an actual correlation coefficient from regressing the number of leaves (LN) produced per day to number of branches (BN) produced per day of the example project, according to  FIG. 6 . Logically, if the production stability assumptions stated above are correct, project managers expect the correlation coefficient of the regression of the number of leaves produced per day on the number of branches produced per day to, initially, be very highly correlated as branches and leaves are produced at a similar rate. Moreover, as the structure stabilizes, project managers expect to see the correlation coefficient head towards negative one (−1) as leaves increase at a much greater rate than branches. Accordingly, curve  1000   a  depicts an idealized representation of these assumptions. Additionally, curve  1000   b , representing the actual correlation between the number of leaves produced per day and the number of branches produced per day, is included in plot  1000  to further assess the appropriateness of the project team&#39;s initially asserted assumptions. 
     Although the presently preferred embodiment is directed toward systems and methods that facilitate project developers in the application of statistical analysis techniques and hypothesis testing to establish metrics for use in assessing project progress, it should be understood that the principles of the present invention may be implemented to perform any analysis process where there is a potential for the value of one variable to be dependent on the value of another and the project regression analysis process is merely a current example. 
     As will also be recognized by those skilled in the art, the innovative concepts described in the present application can be modified and varied over a wide range of applications. For example, although the embodiments implement project progress analysis, the invention is not limited to such a process and can be practiced in other general business processes, such as Sales Performance Assessment, Manufacturing Process Assessment, and Distribution System Performance Evaluation. Accordingly, the scope of the present invention should not be limited to any of the specific exemplary teachings discussed, but is limited by the following claims.