Statistical Analysis Project
• Call us: +44 (203) 286 8649
• contact@expertassignmenthelp.co.uk

# Statistical Analysis Project

You can download the solution to the following question for free. For further assistance in  Statistics assignments please check our offerings in Statistics assignment solutions. Our subject-matter-experts provide online assignment help to Statistics students from across the world and deliver plagiarism free solution with free Turnitin report with every solution.

(ExpertAssignmentHelp do not recommend anyone to use this sample as their own work.)

### Question

To analyse the real estate market in non-capital cities and towns Safe-As-Houses Real Estate, a large national real estate company, has collected data from random samples of residential properties for sale for a selection of non-capital cities and towns in States A, B and C.

As a research assistant for Safe-As-Houses Real Estate, you are analysing this data for the town or city specified by your sample. In addition, you compare the price data for this location with price data from another town or city. For example, if your student ID number ends in 8 your sample is Sample 8. That is, you will be analysing the real-estate market in Regional City 1, State B. You will also compare the residential property price data in Regional City 1, State B with the price data for Regional City 2, State A.

In each part of the project, you are required to analyse your sample data in response to given questions and provide a written answer.  You can assume that the written answers are components of a longer report on the real estate market in your given city or town.

### Written Answer Part C

Delete the italic text and add your content.

Each answer below should:

• Introduce and put the question in context
•  Include appropriate Excel output.
• Present the results of your procedures, intervals or tests without unnecessary statistical jargon

C.1 Price Comparison with Location X, State Y

100 to 200 words and 1 to 2 pages

Is there a difference in the mean price of residential properties for sale in the two locations?

to decide if there is a difference in average price between the residential properties for sale in the location and state specified by your sample and those in the location and state specified in the last column of your data.

In order to test the difference in the mean price of residential properties for sale in the two locations, a two-sample t-test was used while assuming significance level of 0.05S ince the population standard deviation of the price of the residential properties is not known, therefore we used a two-sample t-test. The result showed significant difference exist between the mean price of State A and State B;t(248) = 2.52;p = 0.0124.Thus,if the mean price in the two states is the same, then the probability of getting the difference in means in our sample is 0.0124.It is not a likely event. Thus, the random sample selected provide evidence of the difference in the mean price of residential properties for sale.

C.2 Predict Price of Residential Property for Sale

200 to 500 words and 2 to 4 pages

Use the simple and multiple linear regression models developed in Questions 2 and 3 to provide, and justify, a linear model to predict the price of a residential property from internal area, and/or number of bedrooms and/or if the property is a house or a unit.

• Explain choice of independent and dependent variables.
• Include your scatter plot and discuss any apparent relationship between price and internal area. Comment on the strength, shape and sign of the relationship.
• Include and justify the simple or multiple linear model which best fits the data.
• Discuss and interpret the values of the regression and correlation coefficients of the best model.
• Present the results without unnecessary statistical jargon.

The aim of the paper is to predict the price of the residential property using different independent variables. For the first cast, the only internal area was considered as an independent variable for predicting price. For the second case, the internal area of the room, bedroom, and house type was considered for predicting the sales price of the house.

Linear Regression: A linear regression approach was used to predict the price of the residential property from the internal area of the room. Thus, the dependent variable is the price of the house with independent variable being the internal area of the room.