Let's start by addressing the first problem.

1. Pearson Correlation Coefficient

The formula for Pearson's correlation coefficient ($r$) between two variables $X$ (Capital) and $Y$ (Profit) is:

[ r = \frac{n \sum (XY) - \sum X \sum Y}{\sqrt{[n \sum X^2 - (\sum X)^2][n \sum Y^2 - (\sum Y)^2]}} ]

Where:

• $n$ is the number of data points (here, $n = 10$),
• $X$ represents the Capital,
• $Y$ represents the Profit.

Steps:

1. Calculate $\sum X$, $\sum Y$, $\sum XY$, $\sum X^2$, and $\sum Y^2$.
2. Plug the values into the formula to find $r$.

Table of values:

Now, let's compute the sums:

• $\sum X = 429$
• $\sum Y = 160$
• $\sum XY = 15108$
• $\sum X^2 = 37795$
• $\sum Y^2 = 6426$

Substituting into Pearson's formula:

[ r = \frac{10 \times 15108 - 429 \times 160}{\sqrt{[10 \times 37795 - 429^2][10 \times 6426 - 160^2]}} ] [ r = \frac{151080 - 68640}{\sqrt{[377950 - 183041][64260 - 25600]}} ] $r = \frac{82440}{\sqrt{194909 \times 38660}} \approx \frac{82440}{86871.2} \approx 0.949$

So, the Pearson correlation coefficient $r \approx 0.949$, indicating a strong positive correlation between Capital and Profit.

2. Spearman Rank Correlation Coefficient

For the second problem, we need to compute the Spearman Rank correlation coefficient for Income and Expenses. The formula is:

$\rho = 1 - \frac{6 \sum d_i^2}{n(n^2 - 1)}$

Where:

• $d_i$ is the difference between the ranks of the two variables (Income and Expenses),
• $n$ is the number of pairs (here, $n = 7$).

We rank the Income and Expenses, compute the rank differences, and apply the formula.

Would you like me to continue solving this second part in full detail?

5 Related Questions:

1. How do we interpret the value of a Pearson correlation coefficient?
2. What are some assumptions behind the use of Pearson's $r$?
3. How does Spearman's correlation differ from Pearson's correlation?
4. What is the impact of outliers on Pearson correlation?
5. Can two variables have a high Spearman correlation but a low Pearson correlation?

Tip:

When data contains outliers or is not normally distributed, Spearman's rank correlation is often a better choice than Pearson's correlation.