Quantitative Methods - Assignment Example

1.0 Introduction 22.0 Explanation of Correlation and Regression analysis 23.0 Usefulness of correlation and regression 34.0 Setbacks of correlation and regression analysis 44.1 Correlation analysis 44.2 Regression analysis 4References 51.0 IntroductionQuantitative analysis encompasses diverse techniques that aid in formulation and completion of a company’s goals. The analysis owes its basing in regression study and correlation analysis. Any company undertakes statistical analysis of the two determinants to facilitate in decision-making and future expectations.

Therefore, as the sales manager of a company, I will seek to exemplify correlation, regression, their application and limitations respectively.2.0 Explanation of Correlation and Regression analysisCorrelation analysis evolves around playing with two variables that entail no designations as being dependent or independent. Therefore, someone seeks to unveil the relationship between the variables. This relationship can assume status of being positive, negative or null. Positive relationship encompasses denotations as (+ve), Negative by (–ve) and null by (0) (Asuero & Bueno 2011: p.45). Zero exuberates that there exists extremely no relationship among the variables.

Correlation thus becomes useful when trying to gauge relations of sales for the business.Regression analysis undertakes using reliant and independent variables. Exemplifications occur among the existing variables. The regression technique varies from correlation owing to the information that it becomes possible to gauge the unknown value because of having an independent data. Quantitative regression outdoes correlation because of the information of ease in predictions. Graphs entailing reliant and independent variables produce lines of finest fit (Twomey& Kroll, 2008: P.531). The best-fit precisions enable the company to explore in proliferating merchandise encompassing required exactitude in the market place.3.0 Usefulness of correlation and regression One indisputable importance of the quantitative techniques would be information that aid in estimation of variables.

For instance, sales exemplify a variable that counts mostly to a firm. Therefore, sales made at instances denote undeviating or indirect correlation to matters of taste, earnings and cost. Attention to these matters leads to improved sales because of greater quality (Parasumaran.et.al, 2007: P 345).Regression entails the importance of exemplifying trend within certain durations. Therefore, this helps in keeping trend in sales through revising production. Evaluating trends ensures that there entails observance to current precisions involving customers.

Depiction of such trends ensures that focus is paramount in firms. Regression technique also exemplifies effectiveness of advertising performed to a certain product. These exemplifications are after plotting a graph comparing sales and advertisement.The techniques also count in maintaining market forecast (Parasumaran.et.al, 2007: P 345). This explains that sales experienced within certain period would depict future ones. This occurs after obtaining coefficients that depict consistence in sales.

The company takes to planning with reference to results depicted by the graphs.Planning becomes easier through deriving unknown data from the known. For instance, reduced or improved sales depict that entails changes in fields like quality, cost and taste. This enables a certain company strategize about production. Proliferating goods that entail specification required of the market place results to increased sales.Using the two techniques aids to establish determinants related to sale (Parasumaran.et.al, 2007: P 345).

This expounds the various determinants concerning to amounts of sales achieved. This explains that the sales executive ensures that sales meet specifications of purchasers. Therefore, determinants like fashion and income encompass some of the considerations.4.0 Setbacks of correlation and regression analysis4.1 Correlation analysisCorrelation entails the setback of solely quantifying linear relationships. This results to increased problems while trying to correlate data that does fail to assume linear forms.

A second limitation emanates from the information that some variables may depict relationship when absolutely none exists. Finally, using data that depicts abnormally great values can alter the coefficient bestowed to correlation (Burtis & Neher, 2011:497).4.2 Regression analysisThis quantitative technique encompasses depictions of inconsistency. This exemplifies that data accounted for keeps fluctuating depending on conditions of certain times. It thus becomes unreliable for upcoming times. The other predicament crops from the information that validity of the assumptions compiled depicts suitability of interpretations presented.

ReferencesAsuero, A, & Bueno, J 2011, 'Fitting Straight Lines with Replicated Observations by Linear Regression. IV. Transforming Data', Critical Reviews In Analytical Chemistry, 41, 1, pp. 36-69, Academic Search Complete, EBSCOhost, viewed 19 January 2012.Burtis, M, & Neher, D 2011, 'Correlation and Regression Analysis In Antitrust Class certification', Antitrust Law Journal, 77, 2, pp. 495-532, Business Source Complete, EBSCOhost, viewed 19 January 2012.Parasuraman, A., Grewal, D., & Krishnan, R. (2007). Marketing Research.

Boston, MA, Houghton Mifflin Co. pp.340.printTwomey, P, & Kroll, M 2008, 'How to use linear regression and correlation in quantitative Method comparison studies', International Journal Of Clinical Practice, 62, 4, pp. 529-538, MEDLINE with Full Text, EBSCOhost, viewed 19 January 2012.