Homes For Sale

Data were collected from a random sample of 120 homes for sale in the United States. The variables in the data set include the following:

• Price: asking price (in thousands of dollars)
• Size: livable area (in thousands of square feet)
• Beds: number of bedrooms
• Bath: number of bathrooms

Research question

1. Is there a relationship between the size of a house and asking price?

2. Can the asking price of a house be predicted using the size, number of bedrooms, and number of bathrooms?

I conducted the correlation for the question 1, and the regression for the question 2. The result of them are below;

Based on the hypothesis test and the confidence interval, this study provides no evidence that the relationship between the size of a house (M = 2.034, SD = 1.118) asking price (M = 479.708, SD = 686.636). There is a large effect size. A Pearson's product-moment correlation was run to assess the relationship between the size of a house and asking price. There was a strong positive correlation between the size of a house and asking price, r=0.653, p<0.01, with size of a house 43% of the variation in asking price of house.

The linear regression was conducted to assess the relationship between pairs of Homes for Sales variables: the price (M=479.708, SD=686.636), the size(M=2.034, SD=1.118, number of bedrooms(M=3.275, SD=1.115), number of bathroom(M=2.324, SD=1.067).

There was moderate positive relationship between the price and the size, r=0.603, p<0.01. There was no linear relationship between the price and the number of bedrooms, r=0.326, p<0.01. There was moderate positive relationship between the price and the number of bathrooms, r=0.602, p<0.01.

A linear regression established that the size could statistically significantly predict the asking price of house, F(1,118)=87.489, and the size accounted for 43% of the explained variability in the asking price of house. The regression equation was: predicted the price of house=-335.50+400.759x(size). A linear regression established that the number of bedrooms could statistically significantly predict the asking price of house, F(1,118)=14.069, and the number of bedrooms accounted for 11% of the explained variability in the asking price of house. The regression equation was: predicted the price of house=-178.582+201.005x(the number of bedrooms). A linear regression established that the number of bathrooms could statistically significantly predict the asking price of house, F(1,118)=67.085, and the number of bathrooms accounted for 36% of the explained variability in the asking price of house. The regression equation was: predicted the price of house=-420.524+387.405x(the number of bathrooms)

Question> And I should write the suggestion for the future research, so what can be the future research ideas based on the finding this study? or what can be the future research suggestions?