### Linear Regression Analysis on Housing DataThe table below presents data on house characteristics and their selling prices. The goal is to determine if there is a statistically significant linear relationship between the independent variables (Square Feet, Number of Bedrooms, Age) and the dependent variable (Selling Price) at the 0.05 level of significance. If significant, we will identify the multiple regression equation that best fits the data.| Square Feet | Number of Bedrooms | Age | Selling Price ||-------------|---------------------|-----|---------------|| 2848 | 4 | 6 | 242100 || 1270 | 4 | 7 | 113600 || 1825 | 4 | 8 | 281700 || 2235 | 5 | 5 | 199100 || 2072 | 4 | 2 | 307500 || 2197 | 4 | 14 | 278800 || 2184 | 4 | 5 | 275300 || 1764 | 4 | 7 | 107200 || 2276 | 4 | 14 | 103000 |#### Step 2 of 2Determine whether there is a statistically significant linear relationship at the 0.05 significance level.If the relationship is confirmed as statistically significant, you will need to compute the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise, indicate that there is not enough evidence for statistical significance.Use the regression equation form:\[ \hat{y} = b_0 + b_1 x_1 + b_2 x_2 + b_3 x_3 \]- $ b_0 $: Intercept- $ b_1 $, $ b_2 $, $ b_3 $: Coefficients for Square Feet, Number of Bedrooms, and Age respectivelyTo proceed, select the appropriate checkbox if there is enough evidence, otherwise note that there isn't. ### Exploring the Factors Affecting the Selling Price of HousesThe following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable ($x_1$) is the square footage, the second independent variable ($x_2$) is the number of bedrooms, and the third independent variable ($x_3$) is the age of the house.#### Effects on Selling Price of Houses| Square Feet | Number of Bedrooms | Age | Selling Price ||-------------|---------------------|-----|---------------|| 2848 | 4 | 6 | $242,100 || 1270 | 5 | 7 | $113,600 || 1825 | 4 | 8 | $281,700 || 2235 | 5 | 5 | $199,100 || 2072 | 4 | 2 | $307,500 || 2197 | 4 | 14 | $278,800 || 2184 | 4 | 5 | $275,300 || 1764 | 4 | 7 | $107,200 || 2276 | 4 | 14 | $103,000 |**Instructions:**Step 1 of 2: Find the $p$-value for the regression equation that fits the given data. Round your answer to four decimal places.

The independent variables are square feet, number of bedrooms, and age. The dependent variable is selling price. This is multiple regression model. We have to find the p-value and multiple regression equation.EXCEL (Data Analysis) Procedure: Step 1: In EXCEL sheet, enter Square feet, number of bedrooms, age and selling price in different columns. Step 2: In Data, select Data Analysis and choose Regression. Step 3: In Input Variable Y Range: select Selling price. Step 4: In Input Variable X Range: select Square feet, number of bedrooms, age. Step 5: Select Lables. Click Ok. EXCEL (Data Analysis) Output:Step 1 of 2: The p-value for the regression equation is 0.5045. Step 2 of 2: The multiple regression equation is