To find the best prediction of the number of clicks on the advertisement if 1,500 people visit the website, we can model the data with a linear equation $y = mx + b$, where $x$ is the number of visits, $y$ is the number of clicks, $m$ is the slope, and $b$ is the y-intercept.

Step 1: Calculate the Slope (m)

The slope $m$ can be estimated by taking the change in $y$ over the change in $x$ for pairs of points in the data.

Step 2: Calculate the Intercept (b)

Using the slope, we can then substitute one of the points into the linear equation to solve for $b$.

Step 3: Apply the Model to Predict Clicks for 1,500 Visits

Once we have $m$ and $b$, we can substitute $x = 1500$ to find $y$.

Would you like a detailed step-by-step calculation?

Related Questions:

1. How do you find the slope between two points?
2. What is linear regression, and how is it used in predictions?
3. How does the correlation between visits and clicks affect predictions?
4. What are alternative methods to model non-linear data?
5. How do changes in advertising strategy impact click predictions?

Tip: When solving for linear models, it’s helpful to identify outliers, as they can impact the slope and intercept significantly.