Introduction
The correlation between GPA and IQ, given for analysis, can be interpreted as having positive dynamics. At first glance, there is a hypothesis that the intelligence of a student is directly related to their overall attestation results. The higher the intelligence, the higher their GPA, and vice versa – these factors seem to be mutually influencing. However, judging by the exceptions to this linearity, there are regular departures from this rule, proving that the relationship between the GPU and IQ is not so unambiguous. Thus, the doubtfulness of this connection constitutes an alternative hypothesis. According to this hypothesis, the positive dynamics observed at first glance may not be as characteristic as it initially seems. A large number of exceptions falling out of the straight line of this statistic and strongly deviating from the main dynamics may raise doubts about the truth of the original hypothesis.
Description of Variables
The statistical graph proposed for analysis has the characteristics of a rationing date due to the fact that the measurement scales proposed in it are not capable of being below zero (Kaliyadan & Kulkarni, 2019). In a rational date, in contrast to an interval date, the appearance of a negative countable is impossible, since zero is always taken as an abstract reference point. Both systems of selection and interpretation of data, the results of which are compared in the graph, cannot have a negative numerical characteristic. Thus, both countables can be classified as rational data types. It should also be added that both variables can be categorized as both countable and dependent. Independent variables imply a type of information that a scientist can manipulate, experimentally transforming it to obtain a result (Kaliyadan & Kulkarni, 2019). In this case, the collected information should not be distorted or changed in any form, since it is not objectively related to the actions of scientists. Although the relationship between the variables is not obvious, this experimental design attempts to link the two variables to produce an interpretive result.
Correlation Overview
There is indeed a significant correlation between the two variables – this implies the presence of positive dynamics tending from zero to one. Inside the chart, you can draw a clear ascending line from zero to one, indicating that there is a noticeable positive correlation in the chart. Correlation would be negative in the case of a gradual decrease in the line, and if it was impossible to determine the dynamics, one would have to speak about the absence of correlation or its weakness. Pearson’s rating in the region of 0.7-0.8 out of one suggests a moderately strong type of positive correlation. Based on the results of the correlation, it can be concluded that the initial hypothesis about the presence of a connection between the GPU and IQ was justified.
However, the assumption that this date does not have sufficient weight should also be considered. Correlation cannot be reliably interpreted as a causal relationship between one factor and another. The reasons that lie behind the coincidence between the GPA and the intelligence of the student may in fact differ in each individual case. The statistical dimension, due to its structure, does not cover the possibility of specifying the cause. Therefore, it cannot be said that the dynamics directly demonstrate that intelligence determines the graduation marks or that the diligence of the student improves their intellectual skills. Thus, an alternative hypothesis may be that the correlate is unfair in the selection of the data and requires the inclusion of other measurement data, for example, the social status of the students.
Reference
Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian Dermatology Online Journal, 10(1), 82–86.