Correlation between Variables Understanding Business Forecasts

Part 1 Variable A: Number of school lunch eligible students in the school Variable B: Amount of funding received by the school for federal and state education-related programs Correlation: Positive Correlation Reason: The higher the number of school lunch eligible students, the higher the rate of subsidy Variable A: Impact of subsidy received Variable B: Age of students at school Correlation: Negative correlation Reason: The subsidy had a greater degree of response in elementary schools than it did in high schools Variable A: Number of classrooms connected to the internet Variable B: Student performance as measured by standardized test scores Correlation: Minimal correlation Reason: The study came to the conclusion that "despite the noticeable increase in classroom Internet connections, the authors find very little evidence that the program has any impact on student achievement, as measured by test scores in a variety of subjects" (Golsbee and Gurayan 2005).

Variable A: teachers' comfort level with the internet Variable B: ability of teachers to use internet effectively with their students Correlation: Positive correlation Reason: the study found that the teachers' inability to work with computers explained the reason as to why "greater internet access may not directly result in better prepared students" (Golsbee and Gurayan 2005). Part 2 To form the correct conclusions from an analysis of a statistical nature, one must be able to not only understand, but also determine the relationship between various variables.

In essence, the relation between two variables could be positive, negative, or minimal. Correlation, in basic terms, seeks to gauge the nature of association between two variables (Sullivan, 2007). The relevance of correlation when inferring the correct results from available data cannot, therefore, be overstated. In basic terms, positive correlation (or direct correlation) exists in instances where an increase in variable A leads to an increase in variable B.

Positive correlation can also be said to be present or evident in instances where a decrease in variable A also leads to a decrease in variable B. Positive correlation could be inferred between employee motivation (variable A) and performance at the workplace (variable B). In this case, better employee motivation (via increased salaries and wages, enhanced benefits, appreciation awards, etc.) is likely to lead to enhanced worker performance at the workplace.

Another example is whereby a decrease in return on investment (ROI) over a prolonged period of time (variable A) leads to a decrease in the share price of a business (variable B) as investors seek more profitable opportunities elsewhere. Negative correlation, on the other hand, "is one in which the values of the two variables tend to go in opposite directions. " (Sullivan, 2007, p. 114). Negative correlation is also referred to as inverse correlation.

A good example of negative correlation is whereby a decrease in the unit price of a specific item (variable A) leads to an increase in the quantities sold for the same item (variable B). In the words of Sullivan (2007, p. 114), "the higher the correlation, the stronger the relationship is between two variables. .." In that regard, therefore, minimal correlation exists where the relationship between two variables is weak. While the positive correlation between two variables gets stronger as we approach +1, negative correlation.