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 24332433 3 22 277600277600 20602060 4 99 250900250900 19121912 3 99 114800114800 24782478 3 55 290000290000 18841884 4 1414 287300287300 25982598 3 1313 123700123700 30743074 4 22 193900193900 27382738 3 88 146900146900 13981398 5 55 272200272200 Step 1 of 2: Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places. 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 |
|---|---|---|---|
| 24332433 | 3 | 22 | 277600277600 |
| 20602060 | 4 | 99 | 250900250900 |
| 19121912 | 3 | 99 | 114800114800 |
| 24782478 | 3 | 55 | 290000290000 |
| 18841884 | 4 | 1414 | 287300287300 |
| 25982598 | 3 | 1313 | 123700123700 |
| 30743074 | 4 | 22 | 193900193900 |
| 27382738 | 3 | 88 | 146900146900 |
| 13981398 | 5 | 55 | 272200272200 |
Step 1 of 2:
Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places.
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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