Final answer:
Mr. Cheap should collect data on sales, customer traffic, competitor pricing, operating costs, and supplier costs. He can then use statistical methods, such as regression analysis and standard deviation calculations, to predict monthly profits and optimize his stock levels.
Step-by-step explanation:
Data Collection for Predicting Monthly Profit
To predict his monthly profit, Mr. Cheap should collect the following types of data:
- Sales data from similar stores, including the number of hardware products stocked and the revenue generated from each.
- Customer traffic data, which can indicate potential sales volume.
- Competitor pricing, to understand the market rate for computer hardware and set prices competitively.
- Information about operating costs, including rent, utilities, staff salaries, and marketing expenses.
- Supplier costs for different hardware products to calculate profit margins.
Once the data is collected, Mr. Cheap can use statistical methods such as regression analysis to create a predictive model of profits based on different stock levels. Additionally, he should consider the median and variation of product prices and costs, as these will impact his optimal pricing strategy.
For analyzing competitor pricing data, standard deviation is a crucial measure. If the standard deviation from the collected data is significantly higher than the manufacturer's claim, Mr. Cheap could argue there's greater variability in the pricing than suggested. This will affect his pricing strategy and potentially his profit margins. It's essential to use a statistical significance test, such as a t-test, at the 5 percent significance level to determine if the observed standard deviation is genuinely larger.