Read Chapters 7 and 8 in the textbook, Business Analytics: Communicating with Numbers, 2e. : Jaggia, S. (2023). Business analytics: Communicating with numbers (2nd ed.). McGraw-Hill Higher Education
Review the infographic “Linear Regression Analysis – An Example” that is attached with the following descriiption:
This image comprises three graphs that explain three types of linear regressions using a constant variable “Earnings” and three variables (“Savings,” “Expenses,” “Spending Habits”), one for each example. The three examples are shown as three tips from a wise economist on how you can save more money.
The first example shows a positive linear relationship between savings and earning indicating that savings should increase in proportion to earnings.
The second example shows a negative linear relationship between expenses and earnings indicating that one should not increase expenses if their earnings increase, else there are fewer savings.
The third example shows no linear relationship between variables such as spending habits and earnings. Your higher earnings do not necessarily make you a big spender. Similarly, you may not have substantial earnings, but you may still be a big spender. Alternatively, you may even be prudent and not be a big spender regardless of how much you may earn.
The journal’s intent is for you to apply what you have learned to how data analytics is applied in the industry. After reading the assigned chapters and resources, write a reflective journal on what regression analysis means and how that impacts business decision-making. Your journal should be between two to three pages excluding the cover and reference page. For assistance writing a reflective journal, refer to the Sample Reflective Writing resource.