MAJOR PROJECT SPSS ANALYSIS OF GIVEN DATA SET - CASE STUDY

Background

This report is constructed for analyzing the predicted information using regression model about John’s house located on Elm Street in Loxton (a small town). The house is 205 Square Meters and is 10km far from shopping center. There are other 548 houses being sold in Loxton between 2013 and 2018. The data for each of house including age, size, stories, agents and town is given that will be discussed in the report. This paper, will present analysis of the data using different variables through regression for recommending a price at which John should sell his house. 

Task 1

The current section will be based on summary of the variables. Continuous variables will be summarized using the descriptive statistics analysis and simple line graphs while the categorical variables are summarized through frequency tables (Powers & Xie, 2008). 

• Analysis of Price Variables for Acquiring Price Values

Out of all thirteen variables, crime, age, price, town, stories, size and sold are selected to be the variables that influence the price of house. Binary predictors are not included in the above simple line graph analysis. The above line graphs show the outliers and normality of all the variables. It is clear from above that the observations 414 and 375 in price are outliers with observations as $310,000 and $20000. These two entries do not lie in the normal range of price. Moreover, the size variable also shows the outlier of 456 sq meters for 414 observation. These outliers are different from the continuous data for both variables. Before running further tests, these outliers must be removed from the original data so that analysis do not contain the outliers (extreme or lowest values) (Bryman & Cramer, 1997). 

• Descriptive Analysis of All Relevant Variables 

On average, the age of house is 15 years, the town is 52.55 km away from house, the price is $417,013, the crime rate is 3% and stories are 1. The descriptive table above shows the minimum and maximum for each variable i.e. minimum price is $20000, minimum age of 8 years, minimum 1 story and minimum crime rate of 1%. The maximum price is $3,100,000, maximum age is 21 years, maximum stories are 4 and maximum town center distance is 60km. For a normal distribution, skewness and kurtosis should be between -3 and +3 (Ho, 2006). For all the variables above, the skewness values for town (-1.169), sold (0.036), age (-0.005), size (2.867), stories (2.833) and crime (-1.181) lie within the given range of absolute 3 showing normality. While the kurtosis for all the variables except price, size and stories do not lie within the range.