Regression [Use R program] - Statistics Project Example

There have been numerous studies that attempted to quantify the value people have placed on the quality of schools by analyzing the features and prices of houses that are around these schools in various districts. This paper is meant to study the factors that contribute to the sales prices of single family houses which are located within the school district in Goleta, Southern Santa Barbara in California. This research is applied, descriptive and it is developed from a quantitative point of view.

The population of the study is formed by thirty six (36) home sales transactions recorded from the last three years. It represents a sample of possible sales and transactions that could be appearing in areas within the Goleta city. This data consists of six variables which are the price of a house in thousand dollars, number of bedrooms, number of bathrooms, the size of the house, size of the lot and the year in which the house was built. Thus, the sales price is the dependent variable which is determined by the other variables in the data.

The number of bedrooms, number of bathrooms, the size of the house, size of the lot and the year in which the house was built are the independent variables. In this research, regression analysis is used to accomplish the intended task. Various models are developed to determine the one which is the most appropriate in determining the sale price of houses within Goleta. The following models are considered: In this model, the number of bathrooms is dropped. It considers the number of bedrooms, size of the house, lot size and the year built.

The codes used to obtain the summary of this model are as follows: This model considers the number of bathrooms, size of the house, the lot size and the year built as the only predictor variables. Thus, the number of bedrooms is dropped. The codes used to create this model are shown below: To identify the appropriate model, the analysis uses extra