Describe the relationship. Be sure to address IVs (independent variables) and strength (in context). What is the predicted value for y when x is 7.25 seconds?
Understand the Problem
The question is asking us to analyze a statistical relationship between variables using terms such as yintercept, slope, residuals, standard deviation of residuals, and coefficient of determination. This involves understanding how various statistical concepts relate to the data provided.
Answer
The analysis of the statistical relationship includes calculating the yintercept, slope, residuals, standard deviation of residuals, and coefficient of determination, represented as $R^2$.
Answer for screen readers
The specific values of the yintercept, slope, residuals, standard deviation of residuals, and the coefficient of determination will depend on the data provided. You must perform actual calculations based on the given equations and dataset.
Steps to Solve

Identify the Variables
The variables are identified asy
(actualcheckout time) andx
(minutes spent). 
Calculate the yIntercept
The yintercept is the predicted value when the independent variable (checkout time) is zero. This can be taken directly from the linear regression equation, which states that the yintercept is the starting point of the line. 
Determine the Slope
The slope represents the change iny
for a unit change inx
. According to the problem, for each additional minute, the predicted checkout time increases by a specified value. This should be noted from the provided data. 
Find Residuals
Residuals represent the difference between the actual and predicted values. Calculate this by using the formula:
$$ \text{Residual} = \text{Actual Value}  \text{Predicted Value} $$ 
Calculate Standard Deviation of Residuals
This measures the average distance between the predicted values and the actual values, calculated using:
$$ s = \sqrt{\frac{1}{n1} \sum (residuals)^2} $$
where $n$ is the number of data points. 
Determine Coefficient of Determination ($R^2$)
This statistic shows how much of the variability iny
can be explained byx
. It is calculated as:
$$ R^2 = 1  \frac{\text{SS}{res}}{\text{SS}{tot}} $$
where $\text{SS}{res}$ is the sum of squares of the residuals and $\text{SS}{tot}$ is the total sum of squares.
The specific values of the yintercept, slope, residuals, standard deviation of residuals, and the coefficient of determination will depend on the data provided. You must perform actual calculations based on the given equations and dataset.
More Information
The relationship among the variables indicates how the predictive model works. Understanding the residuals and their standard deviation helps in assessing the accuracy of predictions. The coefficient of determination, $R^2$, is a vital statistic in regression analysis as it quantifies the proportion of variance accounted for by the predictor.
Tips
 Failing to accurately calculate residuals by mixing up actual and predicted values.
 Misunderstanding the meaning of the slope and yintercept in the context of the problem.
 Incorrectly applying formulas for standard deviation and $R^2$ calculations, which can lead to the wrong interpretation of results.