Influence the selling price of residential properties

Assignment Help Applied Statistics

Reference no: EM133775407

Statistics for Managers

Purpose of the assessment - Students are required to show understanding of the principles and techniques of business research and statistical analysis taught in the course.
Integrate theoretical and practical knowledge from the discipline of Statistics for Business Decision Making to solve business needs;
Synthesise advanced theoretical, practical knowledge from the discipline of statistics for business decision and be able to apply statistical tools and techniques to solve business problems;
Critically analyse a scenario and apply and justify statistical techniques to solve business problems and the explain the results to a range of stakeholders.
Work well autonomously as well as within group settings to identify and apply statistical solutions to a business scenario

Assume your group is the data analytics team in a renowned Australian company. The company offers its assistance to a distinct group of clients, including (but not limited to) public listed companies, small businesses, and educational institutions. The company has undertaken several data analysis projects, all based on multiple regression analysis. One such project is related to the real estate market in Australia, and the team needs to answer the following research question based on their analysis.

Research question:
How do different factors, such as the size of the land, the number of bedrooms, the distance to the nearest secondary school, and the number of garage spaces, influence the selling price of residential properties?

Task

Create a data set (in Excel) that satisfies the following conditions. (You are required to upload the data file separately).
Minimum number of observations - 100 observations.
The data set should be based on houses sold from 01/04/2024 onwards. (To verify the data set, you are required to add a hyperlink to each property's details from the real estate websites that you used.)

Question 1: Conduct a descriptive statistical analysis in Excel using the data analysis tool. Create a table that includes the following descriptive statistics for each variable in your data set: mean, median, mode, variance, standard deviation, skewness, kurtosis, and coefficient of variation.

Question 2: Provide a brief commentary on the descriptive statistics you calculated. Describe the characteristics of the distribution for each variable based on these statistics.

Question 3: Create an appropriate graph to illustrate the distribution of the number of bedrooms in your data set.

Question 4: Derive a suitable graph to represent the relationship between the dependent variable and the land size in your data set and comment on the identified relationship.

Question 5: Based on the data set, perform regression analysis and correlation analysis, and answer the questions given below.

Derive the multiple regression equation.Interpret the meaning of all the coefficients in the regression equation.
Interpret the calculated coefficient of determination.
At a 5% significance level, test the overall model significance.
At a 5% significance level, assess the significance of the independent variables in the model.
Based on the correlation coefficients in the correlation output, assess the correlation between explanatory variables and check for the possibility of multicollinearity.

Question 6. Based on the answers above, write a brief summary addressing the research question.

Reference no: EM133775407

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