It looks like XYZ Company is trying to predict the "Quantity Sold" based on the amount spent on "Advertising." You provided a table with data on "Quantity Sold" and "Advertising." There is a missing value in the "Quantity Sold" column for an advertising expense of $1,500.

To predict this missing value, we can use a simple linear regression model, which will fit a line to the data points and help us predict the quantity sold based on the advertising spend.

Let me calculate this for you by performing a linear regression on the provided data. I'll use the existing points to find the best-fit line equation and predict the missing quantity.

I'll proceed now with the calculation.Based on the linear regression model, the predicted "Quantity Sold" for an advertising expenditure of $1,500 is approximately 6,472 units.

Would you like a more detailed breakdown of the method used or any further clarifications?

Here are 5 related questions to deepen your understanding:

1. How does linear regression predict the relationship between two variables?
2. What is the formula of the regression line and how can it be interpreted?
3. How accurate is linear regression when predicting new data points like this?
4. Can other models be used to predict Quantity Sold based on Advertising?
5. How can we measure the performance of a linear regression model?

Tip: Always check the strength of the relationship between variables (like Advertising and Quantity Sold) using metrics such as the correlation coefficient before applying regression.