Below, you can see that the x variable refers to square footage and the y variable the listing price. The square footage of a home is used as the predictor variable to determine its listing price. The variable may help determine a property’s asking price. Houses are typically sold according to their square footage. A scatterplot is a form with a linear shape.
It is evident that the predictor variable accounts for 90.2%. A correlation value of 0.950 indicates that there is a strong and positive relationship between predictor variables. The square footage of a house increases with its price. Larger houses are likely to cost more. A few exceptions to the data show homes with smaller footprints or larger values than average. This scatter plot shows the regression equation as: y = 115.058 + 111.146.739
Regression equation
Module 2 involved the creation of a model to accurately determine the price for a house’s listing. Based on scatterplot, the regression equation to determine the line with the greatest fitting is y = 115.058 + 111.146.739
Find r. This will give you the coefficient of determination = 0.902%. Denoted by r, the square root of this coefficient of determination yields r-squared = 0.902%. This indicates a positive correlation between listing price and square footage. You can identify the direction of the scatterplot lines that indicate the relationship between these two variables. From left to right, the scatterplot points rise. The correlation suggests that square footage grows with the increase in home prices.
Take into account slopes and intercepts
Although the slope of the regression lines is 115.058, its intercept is 111.146.739. The projected value of Y for which X is zero is called the intercept. This research found that there was no home with square footage below zero. Therefore, intercept = 111,146.739 is the proportion of house prices not explained by square footage. In this instance, the value of this number is not important. The slope indicates that the home’s listing price will fluctuate between $115,058 and $115,058/square foot.
The R-squared coefficient
The scatterplot yielded a coefficient of determination = r-squared = 0.9902. This shows that the square footage of land is responsible for 90% of variances in listing prices. Other variables account for 9.8%.
Conclusions
The link between price and square footage is visually and quantitatively shown in this research. The relationship between these variables has shown a strong positive correlation. A random sample of data was collected to determine population parameters. These results are representative of all population parameters. In this location, the correlation between square footage and listing prices is strong. Accordingly, the square footage of US properties is expected to be strongly correlated with listing price. Square footage is a measure of how much a property’s price rises. A model’s slope must be constant at the rate that each unit of square feet increases.
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