Silver Package - Sample size of 1,000 listings:

• 95% confidence interval for the mean of the Northeast house listing price has a margin of error of $7,750
• Cost for service to B&K: $10,000

From the information given, the average listing price of a house is X. This study examines a sample size of 1,000 and a margin of error that is 8,000. With this the 95% confidence interval is [302,250, 317,750].

Today I will be choosing from three packages, each at a different price point and choosing the best package for my company would be a very important decision. One would assume that the most expensive package would be the best but because of the point size we are able to provide the most accurate data by giving the largest sample size. But the best package is not important, it's the best package for you and the best package for the money. There are three main factors that play a part in analytics, confidence level, sample size and margin of error. Confidence level is how certain we are that the mean or average selling price of all the houses sold in that region will fall within our margin of error. When all data points are examined, they are called a population and it would be very costly to analyze all selling prices, so we use a sample. Sample sizes vary but generally the larger the sample the more accurate the data. Larger samples help even out the outliers or odd balls so to speak. And then there is the margin of error which calculates how far off the mean could be. The error of margin can go both ways, like saying the average selling price of a house is x dollars, plus or minus y dollars and by using a larger sample we are able to dial in the data and provide a smaller margin of error.

All three packages have a confidence level of 95%, so it will not change based on the packages you choose. The Bronze package has the smallest sample size of 100 listings which would cause it to have a higher margin of error with less data points to calculate and I's the cheapest option. The Silver package has 1,000 data points which makes it ten times greater than the margin of error and can be about a third of what the bronze package would be. The Gold package would be the more accurate package with 4,000 data points and has a margin of error about half of the silver package but at two and a half times the price. It is also important to take into consideration when deciding on a package the margin of error in relation to what is being analyzed, which in this case would be the listing price of houses. I wouldn't choose the Bronze package due to the sample size being too small and it creates a wider margin of error while the Gold package is the most accurate when deciding is the increased price worth the reduced margin of error? I would recommend the Silver package at 40% of the price of the Gold, the margin of error is twice as large on the Silver. Also, when comparing that to the mean of the list prices of $310,000 the margin of error is only 2.5% in either direction of the mean. In real estate a fluctuation of 2.5% is within an acceptable level and saves you $15,000. There for I would recommend the Silver package with a sample size of 1,000 and a cost of $10,000. With the Silver package I am 95% confident the true mean of the list price of houses in the Northeast region falls between $302,250 and $317, 750.

I choes this package but here is the question from my teacher please answer: Regardless of the package you pick, how does the real estate company make back the money they spent? What value can be created by having more than a point estimate of the mean housing price?