Tuesday, June 29, 2010 @ 12:00 AM

1) Variables in our study were first defined, as shown below.
Image and video hosting by TinyPic
The 4 important readings that will be then used in our data analysis will be:
- average_1 (average of left forearm length)
- average_2 (average of right forearm length)
- average_3 (average of left foot length)
- average_4 (average of right foot length)

2) The data was then keyed in accordingly.
Image and video hosting by TinyPic
Image and video hosting by TinyPic
As can be seen, no data is missing, as most of the measurements were taken on the same day right after our common lecture (Refer to Details of Data Collection). However, for a smaller group of participants who had to attend other lessons, their measurements were taken by our group on another day.

We took 2 measurements each for the left and right forearms, as well as the left and right feet. The average of the two measurements are then calculated and later used for the data analysis.
Rationale:
'If we calculate the difference between each pair of measurements and find that the average different is 0, then we can infer that there is no systemic difference between the pairs of result i.e. on average, the duplicate readings agrees. If 1 set of reading represents the true values, as is likely in a method comparsion study, this means that there is no bias.'


3) We then proceeded on to choose a statistical test for the data analysis.
Statistical test chosen: Pearson's r
Rationale:
Our research question aims to find out if there is any association/relationship between 2 scale data
Scatter plots were then drawn to determine linearity of data
4)Scatter plot for left forearm and foot lengths:
Image and video hosting by TinyPic

5) Scatter plot for right forearm and foot lengths:
Image and video hosting by TinyPic
As seen above, both scattered plots generally showed a linear relationship between the variables.

6) Pearson's r (i.e. correlation coefficient) was generated via the SPSS.

Our findings for the left forearm and foot:
Image and video hosting by TinyPic

From the above, r=0.667, p<0.05,n=50
Therefore, the null hypothesis is rejected.
A Pearson's r of 0.667 indicates that there is a strong relationship between the left forearm length and left foot length.


Our findings for the right forearm and foot

Image and video hosting by TinyPic
From the above, r=0.720, p<0.05,n=50
Therefore, the null hypothesis is rejected.
A Pearson's r of 0.720 indicates that there is a strong relationship between the right forearm length and right foot length.




Monday, June 14, 2010 @ 11:59 PM

WE LOVE STATS.

@ 10:45 PM

Hello... testing 1 2 3