Discussion
The concept of the correlation coefficient is crystal clear to me. I have understood that the value of correlation coefficient ranges from -1 to 1. The relationship between the variables increases as the value moves away from zero. However, the movement in the negative direction indicates an increase in negative relationship and movement in the positive direction indicates increased positive relationship between the variables. In a case of negative correlation, an increase in the independent variable leads to decrease in the dependent variable. On the other hand, in positive correlation, an increase in independent variable leads to increase independent variable. Lastly, in zero correlation, the increase or decrease in an independent variable does not lead change in the dependent variable. It is very simple to relate and apply correlation to real life events. For example, there is a positive relationship between the level of water in a lake and amount of rains in the catchment area.
The t-test for the correlation coefficient is as clear as mud to me. I cannot comprehend how to apply the formulae given correctly. The formula has a fraction and a square root at the numerator. Moreover, I am required to know the degree of freedom of the given data. I need an extensive revision of the concept to understand the key points and appreciate the formula.
Understanding correlation requires you to know its meaning and application. In statistics, correlation is used to show how variables relate. It is also important to know that variables may have a negative or positive relationship. This means the value of correlation can be negative or positive. Moreover, variables that have no relationship have a zero correlation coefficient. Finally, it is important for you to know how to calculate the value of correlation coefficient correctly. Note that, when you fail at the data entry stage the final value of correlation coefficient is wrong.
In linear regression, the researcher uses quantitative data to study the relationship between continuous variables. In this concept, the researcher derives a regression equation that can be used to predict the value of the dependent variable given the independent variable. The slope of the regression line is given by the coefficient of the independent variable. It is important to note that a wrong regression line will predict a wrong value of the independent variable. Thus, it is important to be keen when deriving the regression equation.
