Hi Class, Time series decomposition is a valuable method that helps break down a dataset into four distinct components: trend (T), cycle (C), seasonal (S), and irregular (I). The trend component reflects the long-term movement in the data, such as a steady increase in sales. Cycle captures economic fluctuations that last longer than a year. Seasonal variations repeat over fixed periods, such as increased sales during holidays, and irregular components do random events cause unpredictable fluctuations. We use additive models when the components' impact remains constant over time (e.g., seasonal variation is the same each year), and multiplicative models when the effects increase proportionally with the trend. The scatter plot shows a clear positive exponential trend in sales from 2010 to 2021. The regression equation y = 460655x - 9E+08 and an R2 value of 0.7202 indicate a strong correlation and suggest the model can explain about 72% of the variation in sales. Given this trend, I would predict continued sales growth, especially if external factors like marketing, customer base, and market demand remain stable. The data shows promising growth potential for the business in the upcoming years. Write a substantive response to this classmate in paragraph form