Examples Of Inferential Analysis

* Correlation coefficient (R-squared) – This represents how well the independent variables (X) explain the response variable (Y).

The area has highly been affected by the raising taxes and other property values. The homeowners have had difficulties in buying and living in these houses because of the increased taxes. This is because the property values have gone high due to the demand causing increase in taxes. This rise is usually extended to the tenants by the landlords and they find themselves vacating the houses due to high rents. The landlords might sometimes be unable to pay the tax leading to a sell of the houses.

This source’s intended audience might be geographers who research for the lifestyle of these contemporary people. Where they were settling down depends on various factors (weather, source of food and water, natural resource), therefore, can draw the attention to the real estate agents to exploit potential resource and make an investment in this land.

The analysis of the data was done by employing the following statistical techniques which were chosen only after the investigator found them to be most appropriate and compatible to the data. Each statistical method is based upon its own specific assumptions regarding the nature of the sample, its universe and research conditions. These factors were considered in advance. Following statistical measures were

Supply and demand play a major role in the value of real estate. The forces behind supply and demand include physical, economic, political, sociological, and location issues. The location of the subject property within St. Johns provides many positives with respect to value. First, the proximity to local highways not only provides for ingress and egress, but gives the subject neighborhood exposure to potential tenants or clients. It provides a quick means of transportation for employees or customers. Second, the location of St. Johns in proximity to Lansing and the rest of Michigan is an advantage. This proximity provides additional workforce, complementary businesses, and suppliers.

The textbook define the correlation coefficient as "a measure that is designed to indicate the strength of the relationship between two variables" (UBC Real Estate Division, 2009). The textbook also states "the correlation coefficient may be positive, negative, or zero" (UBC Real Estate Division, 2009). A strong relationship implies that there is a relationship between the two variables and "as one variable increases (decreases), the other the variable will increase (decrease)" (Estate Division, 2009). A strong relationship would have a correlation coefficient value of +1 or -1.

Real Estate provides individuals with a source of investment for his or her future. Owning a piece of real estate could be a business investment, or in the case of this research, a home for an individual or a family. When a person purchases a home there are many things to consider. The most common information to review is square footage, price, amount of bedrooms, bathrooms, and whether the house has a garage. Validating this information versus other statistical review is very important. The buyer must have the necessary information to make the best decision. The data needs to have the widest range of necessary

"Correlation is a measure of association that tests whether a relationship exists between two variables. It indicates both the strength of the association and its direction (direct or inverse). The Pearson product-moment correlation coefficient, written as r, can describe a linear relationship between two variables" Correlation (n.d). As a human service professional and completing research there are advantages and disadvantages to correlational research methods, such as using correlational research it allows us to collect data and determine the strength and direction of what it is we

In the case of the real estate data, the variables are the price, size, and the number of bedrooms in a residence. These variables have physical measurements that can be easily verified.

This support that Multiple Linear Regression model is better than the Simple Linear Regression, because it show the relationship with the variables more accurately and you can know which one to discard, and which one to

This case involves an investigation of the factors that affect the sale price of Oceanside condominium units. It represents an extension of an analysis of the same data by Herman Kelting (1979). Although condo sale prices have increased dramatically over the past 20 years, the relationship between these factors and sale price remain about the same. Consequently, the data provide valuable insight into today’s condominium sales market.

| Based on explicit knowledge and this can be easy and fast to capture and analyse.Results can be generalised to larger populationsCan be repeated – therefore good test re-test reliability and validityStatistical analyses and interpretation are

For example, in finding out whether home price of a city is related to factors such as household income, number of households, unemployment rate, and quality of education in that city, one could develop a linear model using the multiple regression analysis. First, collect the home price data as the dependent variable, and income data, unemployment data etc. as independent variables. Second, apply the regression in Minitab software. Third, analyze the output of the regression model for the R-square, the p-values of the coefficient of the variables, and the VIF values.

The data for the second test to be conducted by our group consists of lot sizes of the residential properties that are up for sale in Toronto and Vancouver. The samples are represented in m2 (metres squared; area of the land in which the residential properties are built on). The data taken are based on the properties that are up for

A difficult characteristic to understand about the housing market is how a price is given for a particular house. That price will be designated to that particular house alone. All houses have various pricing, so I can’t always assume that one will cost more or less than any other. The pricing for houses vary based on their characteristics. Each characteristic must be analyzed to determine its contribution or detraction toward the price. I have taken some of these characteristics and modeled the relationship between them and the price of real estate for a specific area.