Predicting Prices of New HomesHere is some output for fitting a model to predict the price of a home (in $1000s) using size (in square feet, SizeSqFt, different units than the variable Size in HomesForSale), number of bedrooms, and number of bathrooms. (The data are based indirectly on information in the HomesForSale dataset.) The regression equation is Price = -217 + 0.331SizeSqFt - 135Beds + 200Baths Predictor Coef SE Coef T P Constant -217.0 145.9 -1.49 0.140 SizeSqFt 0.33058 0.07262 4.55 0.000 Beds -134.52 57.03 -2.36 0.020 Baths 200.03 78.94 2.53 0.013 S = 507.706 R - Sq = 46.7% R - Sq (adj) = 45.3% Analysis of Variance Source DF SS MS F P Regression 3 26203954 8734651 33.89 0.000 Residual Error 116 29900797 257765 Total 119 56104751 (a) What is the predicted price for a 2100 square foot, four bedroom home with 3.0 baths? Round your answer to the nearest hundred dollars. (b) Which predictor has the largest coefficient (in magnitude)? Which predictor appears to be the most important in this model? (c) Which of the variables are significant at the 5% level?
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.
Predicting Prices of New Homes
Here is some output for fitting a model to predict the price of a home (in $1000s) using size (in square feet, SizeSqFt, different units than the variable Size in HomesForSale), number of bedrooms, and number of bathrooms. (The data are based indirectly on information in the HomesForSale dataset.)
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S = 507.706 R - Sq = 46.7% R - Sq (adj) = 45.3%
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