We Have Numbers Of Free Samples

1. The dependent variable in this example is “diabetes”, which is dependent on the changing values of the various factors. The independent variables of this example are calorie intake, BMI, Cholesterol and age. The differences among these variables cause the dependent variable diabetes to change (Hollingsworth & Smith, 2003).

2. For identifying which group has shown greatest reduction in calorie intake, t-tests should be performed. T-tests are used whenever the difference between two groups is needed to be assessed. Intervention is the technique to be used here when one group undergoes training for fitness and another does not. The test identifies the groups are dependent or independent (Hollingsworth, 2003).

3. There are several assumptions that are needed to reach to the t-tests. The assumptions are regarding the variables and the scale of measurement applied to the collected data. For conducting the t-tests also it is to be determined that how many groups are to be assessed. If two groups exist, then it has to be identified which type of groups are they. The groups can be independent or dependent (paired test) groups. Also the data must be in an interval level order to perform a t-test. By comparison of the two groups, it can be said whether the groups are dependent or independent.

4. The BMI and Caloric intake are positively correlated. This shows that when there is an increase in caloric intake, then the BMI increases. Thus the variables share a positive relation.

5. The Body Mass Index and HDL Cholesterol share a negative correlation. Whenever there is an increase in HDL cholesterol, the Body Mass Index decreases and also when the HDL cholesterol decreases, the Body Mass Index increases (Hollingsworth & Smith, 2003).

6. The age and caloric intake does not share any correlation as they are not strongly relating with each other. The value is not any near between +1 and -1 thus, these variables share no relation. The value of p indicates the Pearson product- moment correlation coefficient. It shows whether the values are correlating with the Pearson product moment correlation coefficient that is 0.7544. It is an indicator which shows up to which extend would d the pair of the two variables would lie on a straight line on the graph. The value of p is less than 0.001 that means the values share a negative relationship (Hollingsworth, 2003).

7. Correlation has several limitations. The several problems with the correlation are mentioned below:

It cannot be stated that one variable of the matrix causes the variable. Though there is a high state of association among the variables, it cannot be perfectly said that one variable causes another variable or is completely dependent on other variable. Also correlation does not allow considering anything except the data given in the example. The correlation results may not be exactly relevant or give an exact value.  The numbers may not show the exact figures (Hollingsworth, 2003).

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