The task in the image involves calculating the Pearson correlation coefficient between sales and advertising dollars, and then estimating the sales when $20,000 is spent on advertising.

Here's how to approach this problem:

1. Calculate the Pearson correlation coefficient:

   • Use the formula: [ r = \frac{n(\Sigma xy) - (\Sigma x)(\Sigma y)}{\sqrt{[n\Sigma x^2 - (\Sigma x)^2][n\Sigma y^2 - (\Sigma y)^2]}} ] where $x$ represents the advertising dollars, and $y$ represents the sales.

2. Estimate the sales when $20,000 is spent:

   • This involves using the linear regression equation: $\hat{y} = a + bx$ where $b$ is the slope of the regression line, $a$ is the y-intercept, and $x = 20$ (since the advertising dollars are in thousands).

Table Values:

Steps to Solve:

1. Summations:

   • Calculate $\Sigma x$, $\Sigma y$, $\Sigma x^2$, $\Sigma y^2$, and $\Sigma xy$.

2. Use the Pearson correlation formula to get $r$.

3. Linear regression:

   • Find $b$ (slope) and $a$ (y-intercept) using the formulas: $b = \frac{n\Sigma xy - (\Sigma x)(\Sigma y)}{n\Sigma x^2 - (\Sigma x)^2}$ $a = \frac{\Sigma y - b\Sigma x}{n}$

4. Estimate sales when $x = 20$ using $\hat{y} = a + b(20)$.

Let me know if you want me to compute the values for you, or if you would like further clarification! Would you like the calculations in detail?

Here are 5 related questions:

1. What does the Pearson correlation coefficient tell us about the relationship between sales and advertising spending?
2. How is the linear regression equation used to make predictions?
3. What are the assumptions of using Pearson correlation and linear regression in this context?
4. How would you interpret a strong or weak correlation based on the calculated Pearson coefficient?
5. Can we always use the linear relationship to predict future sales based on advertising spending?

Tip: When using the Pearson coefficient, it's important to visualize data to ensure the relationship is linear before applying linear regression.