MGM600-0803B-02 Applied Managerial Decision-Making - Phase 4 Individual Project - Essay Example

MGM600-0803B-02 Applied Managerial Decision-Making Phase 4 Individual Project Name of the Student Subject Name of the Concerned Professor September 27, 2008 Applied Managerial Decision-Making While studying the characteristics of a series involving only a single variable, which is generally referred to as univariate distribution, one is often required to confine oneself to the measure of central tendency, its variation, skewness, etc.

However, when the idea of a bivariate distribution or a multivariate distribution comes into scenario, one may require to know how the two or more given variables are related to each other and to quantify the extent of their relationship. At such times, correlation allows a manager to assess how the two variables are related. Thus:“Correlation is the statistical measurement of the relationship between two variables. Possible correlations range from +1 to -1 (Wagner, 2008).” To gauge the extent of correlation between two variables, it is vital to know whether the correlation between them is positive or negative (Pink Monkey, 2008):1.

Positive CorrelationThis type of relationship between the two variables exists when both of them move in the same direction, i.e. either both of them move upward or downward. If we consider two variables X and Y, they are positively or directly correlated if the have values as given under in two cases and when these values are plotted on a graph, the graph will rise from left to right.(a) X = 5 8 9 13 16 20 25 Y = 23 29 31 35 42 43 45 (b) X = 100 95 80 68 55 50 Y = 20 18 17 14 10 9 Y YX’ X X’ XY’ Y’Positive and Linear Correlation Positive and Non-Linear Correlation2.

Negative CorrelationThis type of relationship exists between the two variables when both of them move in the opposite directions i.e. one variable moves upwards while the other moves downwards. If two variables are negatively correlated then they will have the values as given under and if these values are plotted on a graph, the graph will fall from left to right.(a) X = 5 8 9 13 16 20 25 Y = 45 43 42 35 31 29 23(b) X = 100 95 80 68 55 50 Y = 9 10 14 17 18 20 Y Y X’ X X’ X Y’ Y’Negative and Linear Correlation Negative and Non-Linear CorrelationCorrelation can further be classified as:1.

Perfect CorrelationCorrelation between the two variables is said to be perfectly positive if the coefficient of correlation is calculated as +1 and perfectly negative if the coefficient of correlation is calculated as -1.2. No CorrelationIn case there exists no interdependence between two given variables, the correlation will not exist and its value will be 0.Another possible classification of correlation is:1. Simple CorrelationSimple correlation is confined to deducing relationship between only two variables say X and Y.2. Multiple CorrelationMultiple correlation refers to the relationship between more then two variables at a time.

Forecasting is a critical job for the managers around the world and correlation is a statistical tool that can considerably help the managers at Widge Corp to arrive at decisive conclusions, so far as the marketing of their soft drinks in public schools is concerned.1. According to the article ‘Closing the Digital Divide: Internet Subsidies in Public Schools by Goolsbee and Guryan, there definitely exists a positive correlation between the two variables ‘Number of school lunch eligible students in the school’ and ‘Amount of federal and state funding for the school for education related programs’ (2003) .

Thus it will be positively beneficial for the managers at Widge Corp to focus their marketing activities on the schools where the number of ‘School lunch eligible students’ is high. Number of school lunch eligible studentsin the school Amount of Federal and State funding. 2. As per the given study, there exists no correlation between ‘Impact of subsidy received’ the ‘Age of students at school’. Pragmatically speaking, the given two variables are not of much use to the managers at Widge Corp.

Age of Age of studentsstudents at schoolat school Impact of subsidy received Impact of subsidy received 3. Also their exists zero correlation between the variables ‘Number of classrooms connected to the internet’ and ‘Student Performance’.Number of Number ofconnected connected classrooms classrooms Student performance Student performance3. According to the given article, there exists a positive correlation between ‘Teacher’s comfort level with the internet’ and the ‘Ability of teachers to use Internet effectively with their students’.

Teacher’s comfort levelwith the Internet Ability of teachers to use Internet effectively with their students Thus the schools where the teachers are more conversant with the Internet are more likely to receive Federal and State funding and it will be beneficial for the managers to focus on such schools. Variable A Variable B Correlation Number of school Amount of funding received +ve Lunch eligible students Impact of subsidy Age of students at school zero received Number of classrooms Student performance minimal connected to the Internet Teacher’s comfort Ability of teachers to use level with the Internet effectively with +ve Internet their students ReferencesGoolsbee, D.

, & Guryan, J. (2003). Closing the Digital Divide: Internet Subsidies in Public Schools. Capital Ideas, Vol. 5 No. 1. Retrieved Sept. 27, 2008, from http://www.chicagogsb .edu/capideas/summer03/digitaldivide.htmlTypes of Correlation, (2008). Pink Monkey.com. Retrieved Sept. 27, 2008, from http://www .pinkmonkey.com/studyguides/subjects/stats/chap6/s060630.asp Wagner, K.V., (2008). Correlation. Psychology Dictionary. Retrieved Sept. 27, 2008, from http://psychology.about.com/cindex/g/def_correlation.htm