ming that the price for housing within a neighborhood have a normal distribution, with mean $100,000, and standard deviation $35,000. What percentage of houses in the neighborhood have prices between $90,000 and $130,000? What price of housing is such that 12% of all houses in th
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
- Assuming that the price for housing within a neighborhood have a
normal distribution , with mean $100,000, and standard deviation $35,000.- What percentage of houses in the neighborhood have prices between $90,000 and $130,000?
- What price of housing is such that 12% of all houses in the neighborhood have lower prices?
1.
Given:
The probability of houses in the neighborhood have prices between $90,000 and $130,000 is:
By using the standard normal table,
Hence, the percentage of houses in the neighborhood that have prices between $90,000 and $130,000 is 41.92%.
Trending now
This is a popular solution!
Step by step
Solved in 2 steps
