Iv.Proposed Model. This Section Introduced The Proposed

* Correlation coefficient (R-squared) – This represents how well the independent variables (X) explain the response variable (Y).

The quantitative research is also based on statistical reports on correlations, regression analysis, comparisons of means and variances, and statistical significance of findings.

The thirty data sets were plotted into a scatterplot and a linear regression analysis was used to show the

Each of the independent variables was analyzed separately to determine their impact on the dependent variable; and the researchers used four separate tables to show logistic and linear models for predicting the correlation.

In agile projects, various stakeholders (IT and Business teams) need to collaborate to share information and clarifications for common understanding about the various type of changes in various stages of the project. If IT and Business teams don’t collaborate regularly during the project, then it is difficult to identify and manage the various types of changes (i.e. requirement changes) that can come up during the project. Effective collaboration is very important when there are changes in the project {Maruping, 2009 #2416}. A weak IT -Business collaboration is an agility inhibitor in software development {Vidgen, 2009 #2414}. This collaborative decision making approach among various stakeholders is necessary for anticipating and responding

Successful software project managers usually are concerned with different type of risks that can affect the project performance and objectives. In order to, to reduce the impact of risk issues and have a good project, my project team in ENB were able to develop a risk plan in advance to identify, prioritize and analyze these risk. These risks represented in schedule risks, cost and technical.

At first, identifying the potential problems. Though I will pay more attention to the top three classical problems, it is necessary to analyze other potential problems in specific project by investigating similar IT projects that is “Failure” or “Success” before kicking off project. The “similar” can be explained in many aspects, such as same goals, cost, scope, same industry, background and so on. I need to investigate how such similar project fails. For example, we assume that a company want to launch an ERP projects recently. In this area, there are many companies did it before. Failure of high-profile ERP projects is common. In order to avoid mistakes that usually occurs in such IT project, it can investigate many failure projects, such as Airforce, Saudi Telecom and so on. The project failure of Airforce due to the following reasons: weak teamwork & composition and lack of top management support. The Saudi Telecom’s failure attributes to lack of top management support, inadequate of company culture, and huge system customization (Aldammas & Al-Mudimigh, 2011). As you

Nowadays, the economy evolves rapidly and levels of competition force companies to adopt new approaches In order to keep up with market changes. Many large and medium scale companies have multiple projects and programs in progress, and they are using PMO as an essential part to apply to project management practices. Over time, many companies with long histories of successful PMO and project management practices may want to accelerate their project performance and adopt Agile as a new approach for their projects. As a result, this change can play an important role to raise different type of problems and difficulties between the existing PMO and Agile development team (Agile developers).

My research is based on this topic below. The data is attached in the spreadsheet. This is a multiple regression analysis. I have attached a PDF file that explains the case and the spreadsheet version with all the data recorded from the PDF file. Pleas emae sure you include all the graphs, plots and please use megastat software.

In real world situations, often one needs to analyze the relationship between one dependent variable and a set of more than one independent variables. In this case, the bivariate model can be extended to include additional independent variables.

Agile is an iterative and incremental (evolutionary) approach to software development which is performed in a highly collaborative manner by self-organizing teams within an effective governance framework, with "just enough" ceremony, that produces high quality solutions, in a cost effective and timely manner which meets the changing needs of its stakeholders [1].

The Agile development focuses on the interaction and coordination among the developers and customers. The software engineering in this philosophy motivates or improves customer satisfaction, software delivery, small project teams – consisting software engineers and the stakeholders, informal methods, and minimal software engineering work products, delivering operational software on time that has improved analysis and design. This operational software increment is considered as an important work product.

In conclusion, agile software development is a very exciting and useful approach, although some project suit agile more than others. The collaboration and visibility can provide a much better and rewarding experience for the team to develop software products. Over the last years, many companies dramatically improved their performances

Determine whether the correlation is significant Calculate and interpret the simple linear regression equation for a set of data Understand the assumptions behind regression analysis Determine whether a regression model is significant

Abstract Early predictions of software defects during software development life cycle play a vital role to produce successful software project. Besides, it reduces the cost of software development, maintains high quality, improves software testing and facilitates procedures to identify defect-prone of software packages. We need more research studies today to improve convergence across research studies due to its use a different of inconsistent measurements. Therefore, to avoid several types of bias: different sizes of the dataset are used in our study to avoid the bias of data. Comparative studies relied on appropriate accuracy indicators suitable for the software defect model are utilized to overcome the problems of bias error in