national homebuildet builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price. Lot cost, and type of home for closings during one month. (See picture) A. Develop a multiple model for sale
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
national homebuildet builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price. Lot cost, and type of home for closings during one month. (See picture) A. Develop a multiple model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "townhouse", where it is equal to 1 ehn type= "townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sale price = __ + (__) • Lot cost + (__) • townhouse. (Round the consant and coefficient of townhouse to the nearest intergar as needed. Round all other values to two decimal places as needed).
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A national homebuildet builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price. Lot cost, and type of home for closings during one month. (See picture)
A. Develop a multiple model for sales price as a function of lot cost and type of home without any interaction term.
Create a dummy variable named "townhouse", where it is equal to 1 ehn type= "townhouse" and 0 otherwise. Determine the coefficients of the regression equation.
Sale price = __ + (__) • Lot cost + (__) • townhouse. (Round the consant and coefficient of townhouse to the nearest intergar as needed. Round all other values to two decimal places as needed).
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