\boxed{January}
Explanation
Understanding the Problem We are given a table of housing data for several months, including the inventory (in months) and the median sales price. Daniel wants to find the best time to buy a house based on this data. The best time to buy would likely be when the inventory is high (meaning there are more houses available, giving buyers more negotiating power) and the median sales price is low.
Finding the Lowest Price First, let's identify the month with the lowest median sales price. From the table, we see the lowest price is $221,700 in January.
Finding the Highest Inventory Next, let's identify the month with the highest inventory. From the table, we see the highest inventory is 6.3 months in January.
Determining the Best Time to Buy Since January has both the lowest median sales price ($221,700) and the highest inventory (6.3 months), it appears to be the best time to buy a house based on this data.
Conclusion Therefore, based on the given data, the best time for Daniel to buy a house is January, as it combines the lowest median sales price and the highest inventory.
Examples
Imagine you're trying to buy a popular video game console. If many stores have the console in stock (high inventory), you're more likely to find a good deal or even negotiate a lower price. Similarly, if the price of the console drops due to sales or promotions (low median sales price), it's a great time to buy. Analyzing housing market data is like analyzing the market for any product – you want to find the sweet spot where supply is high and prices are low to get the best deal. This strategy applies to buying cars, electronics, or even booking flights and hotels.
The total charge that flows through the device is 450 C , and this corresponds to approximately 2.81 × 1 0 21 electrons. The calculation uses the relationship between current, charge, and the number of electrons. You can find the number of electrons by dividing the total charge by the charge of a single electron.
;