Understanding Trend Model Coefficients

Consider a time-series dataset representing the quarterly sales of a company over five years. The sales data has shown a consistent upward trend throughout this period. To analyze this trend, a simple linear trend model is applied, which can be represented as:

$$ Y_t = eta_0 + eta_1 t + ext{e}_t $$

Where:

• $$ Y_t $$ is the sales at time $t$
• $$ eta_0 $$ is the intercept of the regression model
• $$ eta_1 $$ represents the trend coefficient, indicating the average change in sales per time period
• $$ ext{e}_t $$ is the error term

Given this information, what can we conclude about the component represented by $$ eta_1 $$ in this trend model?