The estimated regression equation is a linear equation that predicts profits based on advertising cost.
To analyze the relationship between profits and advertising costs, you can use statistical techniques such as regression analysis. Regression analysis helps determine how changes in one variable (advertising cost) are related to changes in another variable (profits) based on historical data. In this case, you have weekly data on profits and advertising costs. First, you need to organize your data into a table with two columns: one for profits and another for advertising costs. Then, you can use statistical software like Excel, R, Python, or other appropriate tools to perform the regression analysis.
The regression analysis will provide you with a regression equation that describes the relationship between profits and advertising costs. This equation will allow you to make predictions or estimations about profits based on different levels of advertising costs. Keep in mind that the results of the regression analysis should be interpreted carefully. While a correlation between profits and advertising costs may be found, it doesn't necessarily imply causation.
You may need to use the appropriate technology to answer this question. The owner of a retail store randomly selected the following weekly data on profits and advertising cost. Week Advertising Cost ($) Profit ($) 1 0 205 \2 \50 265 \3 250 425 \4 150 305 \5 125 330 (a) Write down the appropriate linear relationship between advertising cost and profits. The appropriate linear relationship is E(y) =