MIS770A: Foundation Skills in Data Analysis - Case Study - Statistic Assignment Help

Assignment Task:

Task:

QUESTION 1 (2+4+7+1+2+5 = 21 Marks)
In the preliminary comment above it is mentioned that the housing data was collected on a sample of 120 houses.

(a) Using the Baycoast case study, describe an example of the use of:
(i) Stratified sampling.

(ii) Non-random sampling.

(b) One of the objectives of the survey is to estimate the average age of all the houses sold over the past 12 months to within plus or minus 2 years, with 95% confidence. Assuming that the population standard deviation for age is approximately 10 years, what sized sample would be required?

(c) The sample of 120 houses was taken from a population of approximately 5000 houses that were sold in the city of Baycoast over the past 12 months. The REIV has been asked to undertake a similar survey in another city, where the population size is 50000, but they are concerned that a much larger sample would be required for the same level of accuracy. What advice would you give?

QUESTION 1 (2+3+3+3+4+1+4= 20 Marks)
In the preliminary comment above it is mentioned that the data was collected on a sample of 152 companies.

(a) Explain why the data analyst would only have taken a sample of companies andwhy it is important that the sample be collected using random sampling.

(b) Is the size of the sample important? One executive had suggested using a smaller sample than 152, while another executive had suggested using a larger sample. Explain any advantages and disadvantages oftheseoptions.

(c) The organisation conducting the survey wants the sample to be large enough so that they can estimate the average Market_Cap_$M to within $30 million with 99% confidence. How large a sample should they take? (Assume a population standard deviation of $250 million). Show your workings.

QUESTION 1

(a)
(i) Give example. eg. suburbs. Explain how the sample could be split up acrosssuburbs
(ii) Give example. eg. sample taken from only Auction results. Explain why notrandom.
(b) n = 97
n = (z*sigma/e)^2 = (1.96x10/2)^2 = 96.04, so round up to 97

(c) Except for very small populations, the sample size required is in effect independent of the population size. For example, the sample size will be the same or similar for populations of size 5000, 50000, 500000, etc. (given the same confidence level, ME and population SD). Could also discuss how the Central Limit Theorem (CLT) shows us that irrespective of the underlying population distribution, as long as the sample size is greater than 30 or so, the underlying distribution of the sample means will also be approximately normal, therefore population parameter estimates will be useful/valid.

(d) Quantitative (Numerical)

(e) Column charts are generally used with categorical data. However, since Number of Bedrooms is a discrete numerical variable with only a small number of possible values (from 2 to 5 bedrooms) it could be used in this case.

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