This formula represents the Sum of Squared Errors (SSE), a measure often used in statistics and regression analysis to determine the accuracy of a model.

Here’s a breakdown of the formula:

$SSE = \sum_{i=1}^{n} (y_i - mx_i - c)^2$

1. SSE (Sum of Squared Errors): This is the total sum of the squared differences between the observed values ($y_i$) and the values predicted by the model ($mx_i + c$).

2. Summation ($\sum_{i=1}^{n}$): This symbol indicates that we sum the squared errors for all data points, from $i = 1$ to $i = n$, where $n$ is the total number of observations.

3. $y_i$: These are the observed values (actual data points).

4. $mx_i + c$: This represents the predicted value from a linear model, where $m$ is the slope and $c$ is the intercept of the line.

5. $y_i - mx_i - c$: This is the error term for each observation $i$, i.e., the difference between the observed value and the predicted value.

6. $(y_i - mx_i - c)^2$: By squaring the error term, we ensure that all errors are positive and larger errors contribute more significantly to the total SSE.

The goal in regression analysis is to find the values of $m$ and $c$ that minimize the SSE, indicating a model that best fits the data.

Would you like further details or have any questions about this formula?

Here are five questions you might consider asking next:

1. How do you derive the least squares estimates for $m$ and $c$?
2. What is the difference between SSE and other error metrics like Mean Squared Error (MSE)?
3. How does SSE relate to the coefficient of determination (R-squared)?
4. Can you explain the significance of minimizing SSE in regression analysis?
5. What are some limitations of using SSE as a measure of model accuracy?

Tip: In regression analysis, always check the assumptions of the linear model to ensure valid results, such as linearity, independence, homoscedasticity, and normality of errors.