is this accurate: The fitted regression equation is y = 2304.5x + 34,440, where y is annual salary and x is years of experience. The slope (2304.5) means salary is predicted to rise by about $2,304.50 for each additional year of experience, and the intercept ($34,440) is the model's predicted salary at zero experience, which is used as a baseline even though 0 years wasn't observed. The R-squared of 0.984 indicates that 98.4% of the salary variation in this dataset is explained by experience, showing an excellent linear fit within the observed range, which is actually rare. The line reveals that salaries increase steadily with experience, and predictions near the data (for example, around 10-14 years) should be fairly reliable. Interpolate means to estimate a value that falls inside the range of observed x-values by using the fitted line; for instance, at 4 years the model predicts about $43,658. Because this estimate is within the data's span, it is generally more dependable than estimates outside the range. If someone wants to know when a salary of $70,000 might be reached, we can use the regression equation to solve for 70,000 = 2304.5x + 34,440 and solve for x, giving x 15.4 years of experience, which is an estimate that lies on the same linear trend and is close to the observed data, so it is relatively trustworthy