investment decisions
Introduction
Discussion on investing and decision-making around the Super Bowl.
Importance of making wise financial decisions to avoid losing money.
Business Analytics and Investment Decisions
Chapter 3 focuses on investment decision making.
Key concepts:
Investment Decisions: Choosing the best investment option based on data.
Variables to Consider:
Return on Investment (ROI)
Return on Assets (ROA)
Return on Equity (ROE)
Fair Value, Stock Price, and Market Capitalization
Sales vs. Revenue
Clarification of key concepts:
Sales: Total number of products sold.
Revenue: Total money earned including sales and other income (e.g. investments).
Example discussion comparing teams, investment strategies, and ensuring diversity in investments.
Class Structure
Objectives for the class:
Review of reading assignments and homework.
Progress updates on projects.
Project includes PowerPoint presentation as well as student involvement.
Chapter 3 Overview
Main focus on data analysis and summary measures.
Central Location: Understanding mean, median, and mode.
Mean (Average):
Calculated by summing observations and dividing by the number of observations.
Notation:
Mu (μ) represents mean.
Sigma (Σ) represents sum of observations.
Median:
Less affected by outliers, providing a better central measure in skewed data.
Mode:
The value that appears most frequently in a dataset.
Can be unimodal, bimodal, or multimodal, indicating how many times values repeat.
Measures of Dispersion
Range: Difference between highest and lowest values in the dataset.
Quick indication of data spread.
Statistical Terms Related to Quartiles
Quartiles: Values that divide the dataset into four equal parts.
Q1 (First Quartile): Represents the 25th percentile.
Q2 (Median): Represents the 50th percentile.
Q3 (Third Quartile): Represents the 75th percentile.
Importance of Quartiles: Helps in understanding data distribution and making comparative analysis.
Project Analysis and Class Activities
Students will present findings as part of their assessments.
Focus on coaching students to analyze data effectively.
Conclusion
Reinforcement of importance of data in making informed business decisions.
Encouragement for students to engage actively in discussions and projects.