Input: > model summary(model) Output: Call: lm(formula = price ~ bedrooms + bathrooms + sqft_living + waterfront + grade, data = kc_house_data) Residuals: Min 1Q

Input: > model <- lm(price ~ bedrooms + bathrooms + sqft_living + waterfront + grade, data = kc_house_data) > summary(model) Output: Call: lm(formula = price ~ bedrooms + bathrooms + sqft_living + waterfront + grade, data = kc_house_data) Residuals: Min 1Q Median 3Q Max -1263684 -130226 -20778 98943 4755524 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -4.883e+05 1.433e+04 -34.09 < 2e-16 *** bedrooms -3.198e+04 2.210e+03 -14.47 < 2e-16 *** bathrooms -2.554e+04 3.347e+03 -7.63 2.44e-14 *** sqft_living 2.134e+02 3.455e+00 61.78 < 2e-16 *** waterfront 7.991e+05 1.893e+04 42.22 < 2e-16 *** grade 9.669e+04 2.227e+03 43.42 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 238600 on 21607 degrees of freedom Multiple R-squared: 0.5782, Adjusted R-squared: 0.5781 F-statistic: 5923 on 5 and 21607 DF, p-value: < 2.2e-16 Knowing this, how do I write the corresponding math formula