The correlation coefficient a concept from statistics is a measure of how well trends in the predicted values follow trends in past actual values. It is a measure of how well the predicted values from a forecast model "fit" with the real-life data.
The correlation coefficient is a number between 0 and 1. If there is no relationship between the predicted values and the actual values the correlation coefficient is 0 or very low (the predicted values are no better than random numbers). As the strength of the relationship between the predicted values and actual values increases so does the correlation coefficient. A perfect fit gives a coefficient of 1.0. Thus the higher the correlation coefficient the better.
In this case it can be seen that there is a strong association between the two variables. Further more it can be seen that there is a negative association between these two variables because an increase in dependent variable is accompanied on the whole by a decrease in independent variable (Best 2001). Negative association is indicated by a negative sign in the correlation coefficient.
Question 5:
For this question I have shown that how we can use student t test. The data contains the responses to two different drug applications by each individual. I have used, a paired t-test, to determine whether the two sets of measurements (A, B) differ from each other.
We use this test to compare two small sets of quantitative data when data in each sample set are related in a special way(Best 2001).
Criteria
The number of points in each data set must be the same, and they must be organized in pairs, in which there is a definite relationship between each pair of data points
If the data were taken as random samples, you must use the independent test even if ...