Correlation is a method to measure the association in between two variables. When we compare the correlating scores of two variables, we are trying to determine whether the variables are related to each other or not. The purpose of doing correlations is to allow us to make a prediction about one variable based on what we know about another variable.
A frequency distribution is the tabulation of raw data obtained by dividing it into classes of some size and computing the number of data elements (or their fraction out of the total) falling within each pair of class boundaries. A frequency distribution can be modeled as a histogram or as a pie chart (Frequency Distribution).
A pie chart shows the differences between two separate variables or subjects. A pie chart is a graph that is in the shape of a circle which represents a total of 100%. Other variables or subjects are shown on the chart with respect to their relative percentages to the whole. The different subjects are shown in different colors and the size of each subject in the pie is proportional to the percentage of the subject.
A bar graph shows raw data and it is designed to show different values of two or more subjects but instead of using the pie to represent data it uses horizontal and vertical bars that represent a different value. The bar graph has numbers along the side of the bars to indicate the value of the variable and there are scales which show what variable is being measured.
The difference between the pie chart and the bar graph is that a bar graph is capable of showing change over time. While a single pie chart cannot show changes over time by itself, it can only represent the given percentages at a fixed point in time.
A graphical display of a frequency table is called a frequency polygon. The X-axis has the intervals shown on it while the number of scores in each interval is represented by the height of a point located above the middle of the