The Importance of Effective Communication in a Team's Performance

The use of a scatterplot was also used to provide a visual representation of the correlations explained above, which can be seen in figure 1 this type of “association” would be described as more curvilinear because it does not go in a straight line or

It helps clarify relationsips between variables that cannot be examined by other methods and allows prediction

* Correlation coefficient (R-squared) – This represents how well the independent variables (X) explain the response variable (Y).

However, a correlation between two variables does not necessarily imply causation but for a causal relationship to exist between two variables there must be a correlation between the variables (Solomon W. Golomb, 2005). When predicting the Grade Point Averages, correlation might not be a good test for its prediction. This is because there is no GPA is not only influenced by intelligent quotient but it is also influenced by other external factors like Education background, family background, social and political environment among other factors. Other statistical tests may include the use of rating scales to rate qualities that cannot be directly rated through correlation by use of variables like good, fair, and excellent among others. Coefficient of correlation might also be used as a technique of predicting the Grade Point Averages. This refers to the main result of a correlation whereby it predicts significant and smaller changes among variables by use of scale r that ranges from +1.0 to -1.0.

When calculating the correlation between two variables, the objective is to see how one variable is influenced by another variable. The bivariate

True or False: Correlations are used to help identify the relationship between two variables. For

The textbook define the correlation coefficient as "a measure that is designed to indicate the strength of the relationship between two variables" (UBC Real Estate Division, 2009). The textbook also states "the correlation coefficient may be positive, negative, or zero" (UBC Real Estate Division, 2009). A strong relationship implies that there is a relationship between the two variables and "as one variable increases (decreases), the other the variable will increase (decrease)" (Estate Division, 2009). A strong relationship would have a correlation coefficient value of +1 or -1.

"Correlation is a measure of association that tests whether a relationship exists between two variables. It indicates both the strength of the association and its direction (direct or inverse). The Pearson product-moment correlation coefficient, written as r, can describe a linear relationship between two variables" Correlation (n.d). As a human service professional and completing research there are advantages and disadvantages to correlational research methods, such as using correlational research it allows us to collect data and determine the strength and direction of what it is we

The R2 value is a statistical measurement of how close the data is from the linear regression line and indicates the variation in the values from the relationship of two variables. In graph 1, the R2 value is 0.60497, which indicates a moderate positive correlation. This suggests that around 60% of the variation in the mean age among different football teams can be predicted from the relationship between the number of wins and the mean age of the team. Contrarily, 40% of the variation in mean team ages cannot be

5. Positive and negative correlations show how close two factors are related. A positive correlation shows that the

Correlation measurements can show if and how variable pairs are related (Correlation, 2012). Quantifiable, or specific amounts, of data are used to determine statistical significance in measurements. The statistical significance determines how likely the correlations are viable measurements or can be due to sampling errors or confounding variables that may not show true results. Significance levels are used to determine the viability of the measurements.

My example of a hypothesized correlation is The more a person smokes, the shorter its life expectancy. According to the article written by Cathy Payne of USATODAY, “People who smoke take at least 10 years off their life expectancy”(January 23, 2013). It is a common knowledge that smoking leads to several chronic illnesses such as lung cancer, heart disease, diabetes, strokes, etc. Therefore, smoking has a direct correlation with life expectancy. The independent variable, in this case, would be the quantity of tobacco consumed; and the dependent variable would be the life expectancy.

The difference between the Pearson correlation and the Spearman correlation is Pearson is most appropriate for measurements taken from an interval scale, while Spearman is more appropriate for measurements taken from ordinal scales. Examples of interval scales include "temperature in Farenheit" and "length in inches", in which the individual units (1 deg F, 1 in) are meaningful. Things like "satisfaction scores" tend to of the ordinal type since while it is clear that "5 happiness" is happier than "3 happiness", it is not clear whether you could give a meaningful interpretation of "1 unit of happiness". But when you add up many measurements of the ordinal type, which is what you have in your case, you end up with a measurement which is really

Relationships/Correlation: this states that when the independent variable changes, the dependent variable must change as well

can only be computed if there is a strong linear relationship exists between x and y