I see you've uploaded an image related to a mathematical problem involving data and a formula for linear regression. Let me extract the necessary information from the image and work through the problem.

Here’s what I gathered from the image:

• The table shows two columns for X (Tahun) and Y (Harga Mobil):
  • X (Years): 5, 6, 7, 8, 9, 10, 11
  • Y (Car Prices): 85, 100, 103, 110, 105, 90, 70

The task involves:

1. Using the linear regression formula to predict the relationship between the variables. $Y = a + bX$ With the following given formula to calculate the slope b: $b = \frac{n \sum XY - \sum X \sum Y}{n \sum X^2 - (\sum X)^2}$ And a (the intercept) calculated as: $a = \frac{\sum Y - b \sum X}{n}$ Where:
   • n is the number of data points
   • ∑XY is the sum of the products of X and Y
   • ∑X and ∑Y are the sums of X and Y values
   • ∑X² is the sum of the squares of X

Let me calculate the values for the regression equation based on the table data.The linear regression equation based on the data is:

$Y = 112.71 - 2.25X$

This means that:

• The intercept ($a$) is approximately 112.71.
• The slope ($b$) is -2.25, indicating that for each additional year (increase in $X$), the car price ($Y$) decreases by 2.25 units.

Would you like to further explore this equation or ask about predictions?

Here are some related questions you might consider:

1. How would you use this equation to predict car prices in future years?
2. How does the slope affect the interpretation of the relationship between $X$ and $Y$?
3. Can we calculate the residuals for each data point to evaluate the model fit?
4. What would the car price be in year 12 using this model?
5. Can this model be improved with additional factors?

Tip: When using a linear model for predictions, remember that it may not be accurate for data points far outside the given range.