Final answer:
To predict house prices, determine the independent and dependent variables, collect data, draw a scatter plot, calculate the least-squares regression line, and find the correlation coefficient to establish the model's validity. Once verified, the regression model can be used to make predictions about house prices using significant independent variables.
Step-by-step explanation:
To predict house prices using a dataset, one must first determine which variables are the independent variables (predictors) and which is the dependent variable (the house price). After collecting the data, for example from the real estate section of a local newspaper, we proceed to data analysis.
In the context of developing a regression model to predict house prices, a scatter plot can be drawn to visualize the relationship between the variables. Upon verifying a relationship, the least-squares regression line is calculated with the form ý = a + bx, where 'a' represents the y-intercept and 'b' represents the slope of the line. This equation allows us to make predictions about the house price based on the values of independent variables.
Further, the correlation coefficient is calculated to determine the strength and direction of the relationship between the independent and dependent variables. If the correlation coefficient is significant, it supports the use of the regression model for prediction. After ensuring the model is valid and checking for outliers, predictions can be made using the established model and by inputting the values of independent variables into the equation.
It's vital to select at least four independent variables that significantly influence house prices, such as square footage, number of bedrooms, location, and age of the house. This will help create a more accurate and robust model.