To solve the problem, we need to calculate the following:

1. The y-intercept of the least-squares regression line (denoted as $b_0$).
2. The sample correlation coefficient (denoted as $r$).

Given:

• The slope $b_1$ is provided by the problem.
• The data points are given for campaign costs $x$ and the corresponding increase in sales $y$.

Step 1: Find the mean values of $x$ and $y$.

Let's calculate the means $\bar{x}$ and $\bar{y}$ first.

$\bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i$

$\bar{y} = \frac{1}{n} \sum_{i=1}^{n} y_i$

where $n$ is the number of data points.

Step 2: Calculate the y-intercept $b_0$.

The y-intercept $b_0$ is calculated using the formula:

$b_0 = \bar{y} - b_1 \cdot \bar{x}$

Step 3: Calculate the sample correlation coefficient $r$.

The sample correlation coefficient $r$ is calculated using:

$r = \frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=1}^{n} (x_i - \bar{x})^2 \cdot \sum_{i=1}^{n} (y_i - \bar{y})^2}}$

Let me calculate these values step by step.### Results: (a) The value of the y-intercept $b_0$ of the least-squares regression line is approximately 6.26 (rounded to two decimal places).

(b) The value of the sample correlation coefficient $r$ for these data is approximately 0.743 (rounded to three decimal places).

Would you like further details on the computations or have any other questions? Here are five related questions you might be interested in:

1. How is the slope of the regression line determined?
2. What does the correlation coefficient tell us about the relationship between campaign cost and sales increase?
3. How would the regression line change if one data point was removed?
4. How do you interpret the y-intercept in this context?
5. What are the assumptions underlying linear regression?

Tip: A strong correlation coefficient near 1 or -1 indicates a strong linear relationship, while a coefficient near 0 suggests a weak or no linear relationship.