After collecting the raw data, we have tabulated the data in SPSS student edition to have them analyzed.
The data we have analyzed determines whether or not the hypothesis is supported. The following figures, which are also illustrated below describing each figure, are the tests that we have applied in SPSS.
 |
| Figure 1. |
The figure shows the raw data that we have entered into SPSS.
(including the first and second attempts in taking the measurement, and the average which have been calculated)
 |
| Figure 2. |
This figure illustrates the frequencies of the average of the foot measurements.
(including the percentage, valid percentage and cumulative percentage. There is no missing value in the compiled date. There may be 3 instances when two data has the same numeric value.)
 |
| Figure 3. |
This figure illustrates the frequencies of the average of the forearm measurements.
(including the percentage, valid percentage and cumulative percentage. There is no missing value in the compiled date. There may be 3 instances when two data has the same numeric value.)
 |
| Figure 4. |
The comparison of the both variables (Descriptive data - describes the average value of both variables and other mathematical calculation of the variable such as; median, minimum and maximum values of each variable, etc)
 |
| Figure 5. |
We use scatter plot as our variables are scale - large number of data points.
The scattered plot is possible in showing the correlation between the two variables.
In this manner, we can determine the degree to which the variables are related. (which have been tabulated representing the Pearson's R below)
The clustering pattern of the variables is in an upward manner, which suggest the scatter plot a positive linear relationship.
In this manner, we can determine the degree to which the variables are related. (which have been tabulated representing the Pearson's R below)
The clustering pattern of the variables is in an upward manner, which suggest the scatter plot a positive linear relationship.
 |
| Figure 6. |
Pearson's R reading - reflects the degree of linear relationship between two variables.
Pearson's R correlation coefficient range from -1.0 to +1.0 - being the perfect association of both negative and positive relationship respectively.
From what we've analyzed, the Pearson's R of the study is +0.707, which is a close score to the perfect association score - +1.0.
This explains that both our variables have strong positive relationships between one another.
This reading (Pearson's R) concludes that our hypothesis is supported and our null hypothesis is rejected.
Thus the conclusion is written as follows: (r=0.707, p<0.05, N= 30)
 |
| Figure 7. |
 |
| Figure 8. |
 |
| Figure 9. |
These three figures above are figures of the Regression Equation - it measures the quantity of both the variables by figuring out the correct equation - the equation of the linear graph. With the correct equation, one can calculate, predict and estimate a value of either of the two variables.
In mathematical theory, the linear graph(straight line) formula is y = m(x) + c.
m = the gradient of the straight line
c = the y-intercept (the value of y when the line cuts the y-axis at x=0)
Values y and x are the values of the two variables.
The circled values, as shown above, are the values valid for the formula to create the equation.
Therefore, our equation are as follows:
y = m(x) + c
y = 0.63x + 9.71
(with y representing the forearm, and x representing the foot - basing on our scatter plot)
Consequently, to find out the either of the variables' value, one must use this equation above and apply it mathematically.
In addition, the equation also directly reflects our scatter plot and linear graph(figure 5). Therefore, the calculation of variables is possible by referring to the graphs's guidelines and the mathematical method using the equation above.
