The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1x1) is the square footage, the second independent variable (x2x2) is the number of bedrooms, and the third independent variable (x3x3) is the age of the house. Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 27502750 55 1414 296600296600 26962696 55 1111 294400294400 25232523 44 77 281400281400 20572057 44 77 240600240600 17971797 44 55 208600208600 17671767 44 55 196400196400 16841684 44 44 171900171900 15541554 33 44 162800162800 15211521 33 33 144900144900 Copy Data Step 2 of 2 : Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.010.01 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant.
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.
The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1x1) is the square footage, the second independent variable (x2x2) is the number of bedrooms, and the third independent variable (x3x3) is the age of the house.
| Square Feet | Number of Bedrooms | Age | Selling Price |
|---|---|---|---|
| 27502750 | 55 | 1414 | 296600296600 |
| 26962696 | 55 | 1111 | 294400294400 |
| 25232523 | 44 | 77 | 281400281400 |
| 20572057 | 44 | 77 | 240600240600 |
| 17971797 | 44 | 55 | 208600208600 |
| 17671767 | 44 | 55 | 196400196400 |
| 16841684 | 44 | 44 | 171900171900 |
| 15541554 | 33 | 44 | 162800162800 |
| 15211521 | 33 | 33 | 144900144900 |
Copy Data
Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.010.01 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant.
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