Our data consists of 3 important sets of readings:
1. BMI
2. Average HEIGHT
3. Average WEIGHT
The type of test used was a correlation test as we were to find whether the test conducted has a relationship between the height and BMI. As both the variables are scale data, Pearson’s product moment correlation coefficient was used to determine the relationship.
The 4 assumptions of Pearson’s R are as follows:
1. All the observations must be independent of each other
2. The dependent variable should be normally distributed at each value of the independent variable.
3. The dependent variable should have the same variability at each value of the independent variable.
4. The relationship between the dependent and independent variables should be linear.
The above 3 assumptions were met and a scatter plot was used to prove assumption 4 by testing for linearity.
Our Findings (A):
Scatter Plot for BMI vs Height:
The scatter plot shows a general positive linear trend between the BMI and height, although the relationship is very weak. Therefore the assumption 4 is met. We then proceeded into doing the Pearson’s r to obtain the correlation coefficient which will then indicate the strength of relationship between the BMI and height.
In short, the above shows: (r= 0.053, p>0.05, n= 60). We do not reject the null hypothesis.
Result: There is a positive, very weak relationship between Height and BMI.
In conclusion, a person’s BMI is not related to his/her height.
Our Findings (B):
In our previous findings, we found out that a person’s BMI is not related to his/her height, hence we intend to embark on a secondary question : Is a person’s BMI related to their weight?
Ho: There is no significant relationship between the BMI and weight.
H1: There is a significant relationship between the BMI and weight.
Scatter Plot for BMI vs Weight:
The scatter plot shows a general positive trend, and there is no violation of the linearity assumption. We then proceeded into doing the Pearson’s r to obtain the correlation coefficient which will then indicate the strength of relationship between the BMI and weight.
From the above table, a Pearson’s r value is 0.752. This shows that there is a significant, positive and a strong relationship between BMI and weight. The p value is 0.000, which is less than 0.05. When p<0.05, we reject the null hypothesis.
In short, the above shows: (r= 0.752, p<0.05, n= 60). We reject the null hypothesis.
Result: There is a significant, positive and strong relationship between BMI and weight.
In conclusion, a person’s BMI is related to his/her weight.
