Quantitative Analysis for Management - Coursework Example

Quantitative Analysis for Management The aim of any business venture is to eventually expand and increase its profitability. However, most firms never live up to the expectations due to poor product lines prompted by inadequate market research. To effectively collect adequate market and business performance data, it is necessary to apply tested and approve techniques. Quantitative data in particular are essential in attaining general information such as the probable market response to a given product. This article hence relies on two articles to critique quantitative techniques of probability and regression. The study also notes that in complex business setups, more advanced techniques such as ANOVA can be utilized to process large quantities of data and facilitate business forecasting. Keywords: Probability, Business forecasting, Regression analysis, ANOVA Introduction The current business landscape is a very delicate venture for new business firms, especially those out to venture in totally new environments in emerging markets. It has often been observed that most firms tend to fail and eventually close down, especially when the top decision making organs are not quick enough to adapt to newly changing business environments. With the stiff competition in the developed markets, there has arisen greater need for large firms to open up their operations beyond the US market. This expansion process has seen the rise of firms such as Britvic, TLG, Coca-Cola and P&G develop into global market leaders. It hence leaves many wondering why certain firms are able to prosper in their international ventures while some find expansion process absolutely impossible. Based on this study, it has been determined that successful launch of new products into any given market requires thorough market research, which may be qualitative or quantitative. This enables the firm to realign its strategies and product lines in congruence to the aspirations, desires and beliefs of the local populations (Render, Stair & Hanna, 2012). This article provides a critical insight into quantitative business research methods based on two research papers. The paper also looks into research lapses observed within the papers and the possible implications on business outcomes. Overview of Quantitative Business Research According to Williams (2007), research is the process of collecting, analyzing and interpreting data in an attempt to fully understand the underlying principles and factors. In a broad spectrum, research can be widely categorized as qualitative and quantitative techniques. While qualitative techniques involve in-depth analysis of cases to clearly outline the factors causing specific variations in business set ups, quantitative analysis mainly involves collection of general information such as consumer response and attitude towards a given product, and is often based on concrete data derived from field tests and observations. The quantitative research techniques facilitate collection of large amounts of data which can hence be easily organized into easily interpretable manner that can as well be manipulated to fit into desired outcomes. There are various methodologies employed in quantitative research. Some of the common approaches include descriptive experiments and comparative studies. These can hence be applied in assessing various cases as they occur in nature. Despite the observation that qualitative and quantitative research techniques have been, in most instances, regarded as mutually exclusive events, various studies have shown that the two can be successfully applied in assessing a case. Collectively, the techniques provide adequate foundations for making appropriate decisions on when to adopt a change initiative. Nevertheless, it is important to note that both qualitative and quantitative research are designed to answer different research questions and are as well dependent on each other (Williams, 2007). Literature Review HAWKINS, G (2008): REGRESSION ANALYSIS IN VALUATION ENGAGEMENTS This article by Hawkins (2008) explores various areas of application of regression methodologies in a business setup. According to this article, it is critical to review business performance to find a suitable way forward. In fact, Hawkins (2008) notes that linear regression as a technique is able to bring out specific vital relationships between two or more variables. As a result, it can be used by firms to determine the factors that affect pricing of goods and services both in private and public institutions. It can as well be handy in property distribution in partnerships. Indeed this article achieves high level of proficiency in outlining the basics of regression techniques. However, the work could be deemed to amount to less researched work, mainly composed of personal opinions as evident from its lack of abstract and reference sources. In this article, Hawkins discusses various instances during which regression techniques are applicable. In page 1 for instance, the introductory paragraph clearly points out the incredible use of regression techniques in court processes to estimate the valuations of business ventures. It is important to note that from time to time, partnerships and business firms may undergo through their low moments. In a few occasions, it is worth to note that the problems within the firm may lead to partners opting to part ways and explore different ventures. This necessitates valuation of the firm in order to determine the share of assets each partner is liable for. Hawkins hence clearly points out to the ideal technique to be used in such instances- regression method of business valuation. In addition, the article clearly points out to pricing as a subject to regression techniques. In agreement, prices of commodities have been observed to vary within the same product lines. This is hence requires critical insight by the firm’s top decision making organs to determine the underlying reasons for these variations. The results from such analyses can be essential in designing proper ways forward and ensuring future profitability. To clearly bring out the concept of Regression, the author begins by discussing some basic principles within the technique. The discussion is as well made on the basis of a sample beer distribution company thus clearly stressing the objectivity of the article towards a business setup. To outline how regression techniques work, the author uses raw data from the sale records of the firm (Hawkins, 2008, 2). To be more precise, the article uses tables, charts and statistics throughout to elaborate fundamental principles. This is very essential for clear understanding of the relationships between business variables as brought out through the regression technique. Assuming only one or two tables were used as elaborative kits, one would assume that the observed relationships between the variables occurred by chance. The author hence manages to convince readers beyond reasonable doubt that price and economic earnings by a firm are subject to a variety of external influences. Based on this work, business appraisal process, whether passive or active, can be determined through regression. For instance, in a state of divorce among couples, there is often problem determining the correct property sharing ratios. In some cases, it has been observed that people may possess lots of properties and even shares in various companies prior to getting married. As such, the rights of the individuals to personal- individual earned wealth must be protected at all costs. As a result, it is necessary to determine the properties one owned at personal level, and distinguish them from joint family property. Consequently, Hawkins (2008) outlines regression as an appropriate methodology to be employed in property sharing process (Hawkins, 2008, 5). In supporting this proposal, the author uses various cases relevant to this study, and is through regression able to outline the strong relationship between company revenues and housing starts. In summary, this article is able to outline fundamentals and basics of regression as applied in a business environment. However, it is my observation that regression entails deeper concerns and must hence be critically and conclusively understood to be applied in business. While the aim of this article is to outline the application of regression principles in valuation of engagements, this objective has been partially met. As a result, one must consult more advanced sources to fully understand the principles of regression and to consequently be able to sustainably monitor business operations. I must however outline that the article has been able to clearly bring out a few fundamentals, especially in relation to usage and application of the Microsoft excel in the analysis. A credible journal article is expected to be not only conclusive in explaining the main objectives, but must as well be consultative and with a credible abstract. This article in my opinion hardly meets these requirements and as such cannot be considered credible enough for academic and business reliance. In fact, the article is directive towards more credible sources, an indication that the author has very little faith in the work. In page 7, the author admits to the shallowness of the content provided and as such directs the audience to a secondary data source on the same subject, but by a different author- Regression Analysis by Mendenhall and Sincich. It would as well be expected that the sources used for this analysis be provided by the author. On the contrary, no source is cited. This therefore makes it difficult to trust the validity of the article, which has used a magnitude of tables, charts, excel spreadsheets and other secondary statistics without directing the readers to the sources of the data. As such, readers may perhaps conclude that the values given for the calculations were not practical, but rather created up to fit with the intended conclusions, and to show a relationship among variables, which may not exist in reality. The basic guideline to the discussions in a research paper is an abstract or an executive summary. In this case, the article contains neither of these, and may as such not be credible enough for academic use. Finally, the author builds a lot of confidence around regression technique. In reality, the results may not be as accurate prompting use of other more sophisticated methodologies such as ANOVA for processing large data sets. FEDER & HARDIE (2008): PROBABILITY MODELS FOR CUSTOMER BASE ANALYSYS This is an example of an all-round, well researched academic business journal. The authors, Fader and Hardie have clearly outlined the fundamentals of probability model as applied in determination of the most probable client response to product launch and upgrades. The article is introduced by a short description of the intended discussion, thus effectively preparing the analysts on what to expect. As such, it is my observation that the work is able to fulfill its mandate and to clearly bring out the various aspects of probability. In any business setup, challenges are highly likely to be faced. In such instances, there is high likelihood that the firm may end up losing some clients. Similarly, there are those diehard clients who are able to stick by a brand through tough times. Consequently, the authors of this article outline the need for a business firm to determine probable future activity of clients and projected transaction levels (Fader and Hardie, 2008, 4). This determination is thus be important in business forecasting and prompt decision making processes. This study further proposes existence of two client types- the contractual and the non-contractual clients (page 6) which affect business set ups in relatively unique ways, either in the short-term or long-term run. Firms may however find it quite difficult to determine the influence specific consumer groups may have on their products. As such, this article notes that probability model can be effectively applied to establish likely consumer responses to future business ventures. In this model, it is necessary that the firm first identifies the variable in question, establishes individual level response patterns then selects the right probability distribution. These basic factors are as a result important in designing the probability model, which is essential in decision making process. Fader and Hardie (2008) further assert time, counting techniques and correct choice of variables as important components of the probability model. In agreement, it is key to outline that customer response to product often change with time depending on different factors. As such, it is correct for this article to outline a broader spectrum of events and variables on the probability model should be based. Through statistical techniques, the article has as well been able to stress on the dependence of business on individual input factors, which the authors refer to as the building blocks to the general public response to a venture (page 23). In summary, this article is satisfactory in its assessment of probability as a quantitative factor in business projections. In explaining various concepts and bringing out relationship between various variables, this work has been able to use various statistical techniques, tables, graphs and charts. The work is as well thorough search and has adequate citations and reference sources. Moreover, the work appears to general with no clear objectives or conclusions. Nevertheless, it must be pointed out that the research work clearly demonstrates relationship between various variable through the vivid methodologies and results given. In summary, Business research is a vital pillar for business success. As such, firms must put in place proper mechanisms to ensure adequate research before blindly venturing into expansion processes. It is also key to note that the past experiences of a firm are a great determinant of its future prosperity. Based on the quantitative techniques analyzed in this case, it is obviously true that firms must put in place strategies to analyze their past experience as this greatly helps in decision making processes. As an example, a business firm may opt to rebrand so as to alleviate the negative publicity that had for one time or another been associated with the firm. A notable example of a firm that has rebranded and succeeded thereafter is the Animal Rights Movement whose rebranding enabled it to win the hearts of people (Spasser, 2013). References Williams, C. (2007). Research methods.Journal of Business & Economic Research. Retrieved from: http://www.cluteinstitute.com/ojs/index.php/JBER/index Hawkins, G. (2008). Regression analysis in valuation engagements. The Journal of the Business Valuation Committee of the American Society of Appraisers. Retrieved from: http://www.businessvalue.com/resources/Valuation-Articles/Regression-in-Business-Valuation.pdf Spasser, A. J. (2013).Winning hearts and minds: using "ag-gag" outrage and corporate rebranding to achieve a public image makeover for the animal rights movement. Masters Project, USF. Paper 4. Retrieved from: http://repository.usfca.edu/cgi/viewcontent.cgi?article=1003&context=capstone Fader, P. & Hardie, B. (2008). Probability models for customer-base analysis. 19th Annual Advanced Research and Techniques Forum. Retrieved from: http://www.brucehardie.com/talks/ho_cba_tut_art_08.pdf Render, B. & Stair, Jr. R., & Hanna, M. (2012). Quantitative analysis for management. Custom ed. Boston, MA. Pearson. Retrieved from: http://home.kku.ac.th/chrira/050243/QAforMGMT.pd Read More