Hello! I am working on regression using initially 6 variables (regression 1). I then dropped a few of those variables that were not significant at all, based upon their P value, and generated the second regression.

Identify 2 reasons why Regression #2 is better than Regression #1.

Thank you!

Regression #1:

SUMMARY OUTPUT Regression Statistics Multiple R 0.77789293 R Square 0.60511741 Adjusted R Sq 0.5790812 Standard Erro 146.266766 Observations 98 ANOVA of 55 MS F Significance F Regression 6 2983351.16 497225.1933 23.2413744 1.8125E-16 Residua 91 1946850.981 21393.96683 Total 97 4930202.141 Coefficients Standard Error Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 69.271221 80.53080886 -0.86018286 0.39194882 -229.23578 90.6933378 -229.23578 90.6933378 bath 46.7720149 24.03167672 1.946265152 0.05470793 -0.9639587 94.5079886 -0.9639587 94.5079886 area 0.10573473 0.023557872 4.488297422 2.0964E-05 0.05893992 0.15252955 0.05893992 0.15252955 lot 330.750364 74.50296912 4.439425278 2.52778-05 182.759369 478.74136 182.759369 478.74136 Central_cooli 88.3546274 30.42575108 2.903942359 0.00462415 27.9176105 148.791644 27.9176105 148.791644 bed -12.403365 26.12717026 -0.4747305 0.6361164 -64.301779 39.4950496 -64.301779 39.4950496 Heat_pump 39.5217054 39.89929043 0.990536549 0.32453847 -39.733334 118.776744 -39.733334 118.776744 RESIDUAL OUTPUT Observation cted Price ($14 Residuals andard Residuals 1 1128.42702 391.5729753 2.763962987 2 1073.75308 -43.75308485 -0.30883619 3 487.78252 -67.78252025 -0.47845073 4 648.229335 31.77066489 0.224256901 5 350.068001 78.43199854 0.553621303 6 477.209754 -21.20975449 -0.1497115 7 915.14908 354.8509205 2.50475613 8 710.992868 -153.5428681 -1.08380003 9 793.530816 -96.03081569 -0.67784458 10 458.27206 191.7279399 1.353333766 11 522.712535 17.28746507 0.122025565 12 496.43422 49.56578015 0.349865773 13 523.48846 148.0115399 1.044756517 14 704.194691 -124.1946913 -0.87664255 15 679.990752 -48.49075217 -0.34227756 16 593.685339 16.31466081 0.115158914SUMMARY OUTPUT Regression Statistics Multiple R 0.77441668 R Square 0.59972119 Adjusted RSq 0.5825049 Standard Erro 145.670693 Observations 98 ANOVA of MS F Significance F Regression 4 2956746.71 739186.677 34.834514 9.3447E-18 Residual 93 1973455.43 21219.9509 Total 97 4930202.14 Coefficients itandard Error t Stat P-value Lower 95% Upper 95%% Lower 95.0% Upper 95.0% Intercept -86.522306 58.6063411 -1.4763301 0.14323337 -202.90288 29.8582728 -202.90288 29.8582728 bath 44.8457544 21.9883898 2.03951971 0.04423522 1.18117008 88.5103387 1.18117008 88.5103387 area 0.09930414 0.02246411 4.42056814 2.6642E-05 0.05469486 0.14391341 0.05469486 0.14391341 lot 331.292997 74.183152 4.46587923 2.23958-05 183.97996 478.606035 183.97996 478.606035 Central_cooli 90.9788164 30.1599276 3.01654625 0.00329719 31.0871775 150.870455 31.0871775 150.870455 RESIDUAL OUTPUT Observation cted Price ($1. Residuals indard Residuals 1 1104.99072 415.009278 2.90957805 2 1085.40988 -55.409877 -0.3884717 3 481.238921 -61.238921 -0.4293384 4 624.924842 55.0751581 0.38612503 5 367.967584 60.5324159 0.42438518 6 481.71869 -25.71869 -0.1803105 7 928.280571 341.719429 2.3957521 8 682.838295 -125.38829 -0.8790816 9 793.940004 -96.440004 -0.6761288 10 460.800981 189.199019 1.32645062 11 538.788663 1.21133742 0.00849253 12 467.753225 78.2467748 0.54857833 13 526.422304 145.077696 1.01712155 14 719.115165 -139.11517 -0.975319 15 647.616187 -16.116187 -0.1129886 16 615.946543 -5.9465435 -0.0416905 17 548.830387 -96.330387 - -0.6753603 18 683.81315 216.18685 1.51565892