Software Selection

3.2.1 Introduction

In this section, the processes followed to achieve the final software selection are discussed. The section starts with the introduction of the types of statistical packages, namely Microsoft Excel, MATLAB and SAS, that were considered for the computer programme. Each package is introduced, followed by their respective applications, advantages and disadvantages. This section concludes with the decision matrix used for software selection.

3.2.2 Types of Statistical Packages

As mentioned under the needs analysis, in section 3.1.2, the way to achieve the objectives that were set for this design is to build a model through the use of statistical packages. The types of statistical packages considered for this design is shown in Figure 3.1 below.

Figure 3.1: Types of Statistical Packages Considered 3.2.2.1 Microsoft Excel

Microsoft Excel (full name Microsoft Office Excel) is a spreadsheet14 application written and distributed by Microsoft. It features calculation, graphing tools, pivot tables and a macro programming language called Visual Basic for Application (henceforth known as VBA) (Microsoft Corporation, 2003 and Wikimedia Foundation Inc., 2007a). There are various add-on applications available that can conduct more in-depth analysis, examples of which are ‘Analysis ToolPak’ and ‘Solver Add-In’.

Some strengths and weaknesses of Microsoft Excel are described below:

Strengths

 It is user- friendly, very easy to learn.

 It can import, organise and explore data sets (Microsoft Corporation, 2007). This implies that Excel has strong analytical functionality. As a result, professional- looking graphs can be created.

 Ability to graphically compare results from a model and observations (Carleton College, 2007).

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A spreadsheet is a grid of information, often financial information, (Wikimedia Foundation Inc., 2007b).

Types of Statistical Packages

Microsoft Excel MATrix LABoratory (MATLAB)

Statistical Analysis System (SAS)

 Smart documents. These are documents that are programmed to extend the functionality of a workbook by dynamically responding to the context of ones actions. For example, the documents can be connected to a database that automatically fills in some of the required information (Microsoft Corporation, 2003).

Weaknesses

 Microsoft Excel was built based on floating point calculation. As a direct consequence, its statistical accuracy has been criticized, since it lacks certain statistical tools (Wikimedia Foundation Inc., 2007a).

 It is effective at certain tasks and not others (Wikimedia Foundation Inc., 2007b). Excel is effective at analytical functions, such as generating graphics, but not effective in mathematical modelling.

 It is loosely structured. Therefore it is easy for someone to introduce an error, either accidentally or intentionally. An example of this is that there is a lack of revision control. It is difficult to determine who changed what and when. This can cause problems with regulatory compliance, among other things (Wikimedia Foundation Inc., 2007b).

3.2.2.2 MATLAB

MATLAB is the abbreviation for MATrix LABoratory. It is a high performance language for technical computing. It can integrate visualisation, computation and programming in an easy-to-use environment, where problems and solutions are expressed in familiar mathematical notation. Some applications of this programme are maths & computation, data acquisition, data analysis, graphics application, modelling, simulation and statistical analysis (The MathWorks Inc., 2006 and Wikimedia Foundation Inc., 2007c).

Strengths

 It is relatively easy to learn (Northeastern University: College of Computer and Information Science, 2003).

 MATLAB code is optimised to be relatively quick when performing matrix operations. It’s an interactive system whose basic elements don’t require dimensioning. Therefore, this package is more robust than Excel, allowing complicated technical problems to be solved (The MathWorks Inc., 2006 and Northeastern University: College of Computer and Information Science, 2003).  There are various toolboxes (add-on applications for specific solutions in a field)

that can be accessed easily (The MathWorks Inc., 2006).

 Although the package is primarily procedural, MATLAB does have some object orientated elements (Wikimedia Foundation Inc., 2007c).

Weaknesses

 MATLAB is an interpreted language, making it, for most part, slower than a compiled language such as C++ (Northeastern University: College of Computer and Information Science, 2003).

 It is designed for scientific computation; therefore it is not a general purpose programming language and not suitable for some things. (Northeastern University: College of Computer and Information Science, 2003). An example is that MATLAB doesn’t support references, which makes it difficult to implement certain data structures (Wikimedia Foundation Inc., 2007c). This point can also be identified as a characteristic of this package.

3.2.2.3 SAS

SAS (originally known as Statistical Analysis System) is an integrated system of software products. Some applications of this software are statistical & mathematical analysis, operations research & project management, business planning, forecasting & decision supports, report writing and graphics.

Strengths

 Being one of the most powerful data mining technologies, there is a huge user base for this software (Yates, 2006).

 It can handle large data sets (Mitchell, 2007).

 It can perform the vast majority of statistical analyses.

Weaknesses

 Relatively hard to learn (Yates, 2006 and Wikimedia Foundation Inc., 2007d) for a person with limited programming experience. One of the reasons is that the syntax it uses is unlike that of any other programming language.

 Doesn’t have sophisticated graphical functions (Mitchell, 2007 and Wikimedia Foundation Inc., 2007d). The graphics generated by SAS are not as clear and structured as those produced by Excel.

 Costs, especially when compared to its open source competitors such as R- squared statistics. It is an open source statistical package that can be downloaded free of charge.

3.2.3 Decision Process

Based on the DRS, discussed in section 3.1, the most important factors15 that affect the choice of statistical packages used, as identified from Table 3.1, are: the processing speed of the package, the cost to obtain the licence of the package and the ease of learning the package.

By combining DRS and the strengths & weaknesses of each of the packages considered, this gives rise to Table 3.2 below, where each of the packages have been benchmarked against each other.

From Table 3.2, each of the three factors have been assigned different weighting factor, based on the DRS. Also, the score of 5 refers to the package being considered as the best

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in the category and 0 being the least desirable in the category. The choice of scores was chosen to show the differentiation between the choices. Therefore, the scores were made to demonstrate a decision matrix.

Table 3.2: Decision Matrix of Concepts

Weighting Factor Maximum Score Minimum Score Microsoft Excel MATLAB SAS Speed 0.5 10 0 0 3 5 Cost 0.2 10 0 5 3 0 Ease to Learn 0.3 10 0 5 3 0 TOTAL 2.5 3 2.5

Therefore, the package with the highest score from Table 3.2, MATLAB was chosen as the final package that is to be used for this design project. With this decision, a complete programme for the discussion in Chapter 2 needs to be undertaken, following the other design requirements in Table 3.1.