Mgm600-0803b-02 applied managerial decision-making - phase 4 individual project

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 Correlation
This 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 Y
X’XX’ X
Y’Y’
Positive and Linear CorrelationPositive and Non-Linear Correlation
2. Negative Correlation
This 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’
XX’X
Y’Y’
Negative and Linear Correlation Negative and Non-Linear Correlation
Correlation can further be classified as:
1. Perfect Correlation
Correlation 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 Correlation
In 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 Correlation
Simple correlation is confined to deducing relationship between only two variables say X and Y.
2. Multiple Correlation
Multiple 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 students
in 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 students
studentsat school
at school

Impact of subsidy receivedImpact of subsidy received
3. Also their exists zero correlation between the variables ‘ Number of classrooms connected
to the internet’ and ‘ Student Performance’.
Number ofNumber of
connectedconnected
classroomsclassrooms

Student performance
Student performance
3. 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 level
with 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 AVariable BCorrelation
Number of schoolAmount of funding received+ve
Lunch eligible students
Impact of subsidyAge of students at schoolzero
received
Number of classroomsStudent performanceminimal
connected to the
Internet
Teacher’s comfort Ability of teachers to use
level with theInternet effectively with+ve
Internettheir students
References
Goolsbee, 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. html
Types 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