Discovering Da Vinci

The Researchers.

Juliana (Group Leader)

Eileen (Analyst)

Eunice (Blog Editor)

Samantha (Blog Editor)

TingShu (Data Collector)

Violet (Data Collector)

The mighty group!

We are Occupational Therapy Students from Nanyang Polytechnic doing up a project for Statistics module. Assigned to choose a topic of our choice, we decided to discover more about Da Vinci's theory of his Vitruvian man drawing. Thus we embark on an exciting journey using statistical approach. Do enjoy our blog! (:

5 - Data Analysis.

Our Data

Analysis of Results

Scatter Plot:

The scatter appears to follow a general positive linear trend, with 76.4% of the results falling on the slope.

Pearson’s Correlation Coefficient:

Pearson’s correlation coefficient range from -1.0 to +1.0.

· +1.0 indicates a perfect positive relationship
· -1.0 indicates a perfect negative relationship

Assumptions- Pearson’s r:

Assumption 1- All observations must be independent of each other
Assumption 2- The dependent variable should be normally distributed at each value of the independent variable.
Assumption 3- The dependent variable should have the same variability at each value of independent variable.
Assumption 4- The relationship between the dependent and independent variables should be linear.

Table 1:

From table 1, the Pearson’s correlation coefficient is 0.874. This indicates a very strong relationship between average wingspan and average height.

There is a positive, very strong and significant association between average wingspan and average height. (r=0.874, p<0.05, N=30)

Since p<0.05, we can reject Ho at 5% level of significance.

Linear Regression:

The linear equation is:

Average wingspan = 1.061* (average height) – 8.282

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