# Data Analysis Methods

Pages: 2 (409 words) Published: April 7, 2013
* Data Analysis Methods
I am using correlation for surveys and observational studies, person correlation if relationship is linear, otherwise Spearman correlation. And get the answer about correlative between per capita income and the percentage of labour force employed in agriculture. * Results

From Figure 1 it appears that there is an association between the percentage of labour force employed in agriculture and per capita income. The points in the scatter plot are irrelevant. When the per capita income increases, the percentage of labour force employed in agriculture decreases.

Figure 1: Scatter/Dot of labour force employed in agriculture Table 1: Summary statistics
Figure 1: Scatter/Dot of labour force employed in agriculture. From Figure 1 there appears to be an association between agriculture and income in 1960. A test of the null hypothesis of no difference in means gave a p-value of 0.001, and the difference in means confidence to lay in the interval units.

* Appendix
Step1: Determine the Parameter of interest.
* When analysing frequency distributions our interest centres on the parameter of interest if an association between per capita income and the percentage of labour force employed in agriculture. * Choose the person correlation coefficient ρ as the parameter of interest. Step2: Write the hypotheses.

* The parameter of interest is the proportion distribution for per capita income and the percentage of labour force employed in agriculture. * Ho: P=0 there is not association between per capita income and the percentage of labour force employed in agriculture. * Ha: p>0 there is association between per capita income and the percentage of labour force employed in agriculture Step3: Specify test criteria.

* Use a T-test with 18 degrees of freedom.
* A significance level of α=0.05 will apply.
Step 4: Computer P-value.
Correlations|
| Per capita income, 1960 (\$)| Correlation coefficient rs=-0.795...