The basic idea behind the chi-square goodness of fit test is to divide the range of the data into a number of intervals. Then the number of points that fall into each interval is compared to the expected number of points for that interval if the data, in fact, come from the hypothesized distribution. More formally, the chi-square goodness of fit test statistic can be defined as follows.
Here again, the data type of both independent variable and the dependent variable is discontinuous and categorical. The relevant test based on data type of dependent and independent variable is CHI-SQUARE Contingency tables. There are ten different groups based on the mood condition i.e. depressed to elated captured in 10 different categories. The Hypothesis tests may be performed on contingency tables in order to decide whether or not effects are present. Effects in a contingency table are defined as relationships between the row and column variables; that is, are the levels of the row variable differentially distributed over levels of the column.
Frequency tables of two variables presented simultaneously are called contingency tables. Contingency tables are constructed by listing all the levels of one variable as rows in a table and the levels of the other variables as columns, then finding the joint or cell frequency for each cell. The cell frequencies are then summed across both rows and columns. The sums are placed in the margins, the values of which are called marginal frequencies. The lower right-hand corner value contains the sum of either the row or column marginal frequencies, which both must be equal to N. variables. The significance of this hypothesis test means that interpretation of the cell frequencies is warranted.