Perform a comprehensive analysis to understand what the data tell you about the impact of the number of bedrooms, the number of bathrooms, and the size of the house in terms of square feet on the price of the property. Provide answers to the questions below:

1. Obtain the summary statistics for the variables and comment on them.

2. Comment on the distributions of data for the variables? Are they symmetrical? Skewed?

3. Obtain the correlations between variables and comment on them?

4. Perform a multiple linear regression analysis with selling price as the response variable and number of square feet, number of bed rooms, and number of bath rooms as the predictor variables.

5. State the regression equation and interpret the regression coefficients.

6. Comment on the goodness of fit of the regression equation for the data.

7. Explain how you will use the regression equation to predict home prices given the values of the predictor variables.

M N L E F G H J K L A E Price Beds Baths Square Feet 330 2 1771 Mean 391.19111 3.2888889 2.3333333 1900.7111 400 2 1213 Standard Error 19.766924 0.1729781 0.1480087 101.82229 2.5 1884 Median 878 1858 416 420 2 1922 Mode 530 3 2000 496 2.5 1858 Standard Deviation 132.60056 1.1603726 0.9928723 683.04471 690 3 2.25 1948 Sample Variance 17582.307! 1.3464646 0.3857955 466550.07 230 2 1200 Kurtosis -0.480922 0.5110023 0.1191923 0.907362 448 2 2002 Skewness 0.3583098 0.4958097 0.7462209 0.8665094 3107 160 768 Range 530 3.75 930 Minimum 160 768 250 252 2 1160 Maximum 690 6 4.75 3875 372.6 1300 Sum 17603.6 148 105 85532 378 2.75 1816 Count 45 45 45 45 430 2.5 1774 474 2.5 1700 SUMMARY OUTPUT 165 1440 228 1598 278 1400 Multiple F 0.8131721 290 2 3250 R Square 0.6612489 290 1.75 1040 Adjusted R Square 0.6364623 312 1348 Standard Error 79.950295 335 2. 2083 Observations 45 320 2 2000 384 2 2160 ANOVA 420 2.5 2240 F 515 2.5 2340 Regression 31 511573.85 170524.62 26.677609 18-09 235 2 1590 Residual 41 262074.06 6392.0503 300 2 1456 Total 44 773647.92 321 4 2.5 1600 340 368 Coefficients Renderd En 2100 Intercept 105.26109 38.410131 2.7404511 0.0090457 27.69 182.83 27.63 182.83 364 2.75 50.862 -15.19 50.862 518 4 2016 eds 17.835406 16.353644 1.0906074 0.2818138 -15.19 36.743 36. 743 388 Baths 79.300956 21.370357 3.7388685 0.0005654 123.06 123.06 2464 -0.041 0.0837 -0.041 0.0837 530 900 Square Feet 0.0214845 0.0308233 0.6970216 0.4897228 190 1058 300 1500 330 2000 530 4 2525 470 2.5 1800 530 4 2700 610 4 3570 660 4.5 2950 540 4 2560 524 3.75 2755 640 4.75 3875