Hi tutor! Interpret each of the estimated regression coefficients, the standard error of the estimate, and R squared.

Here is the data:

BUSINESS AND INDUSTRY (Source: PSA) 2019 ASPBI P Total Revenue Industry Description (All establishments) Establishment Employment (In thousand Php) Agriculture, forestry and fishing 2, 691 131,516 249,400,402 Mining and quarrying 260 33,941 176,650,807 Manufacturing 23,861 1,376,025 5,963, 163,411 Electricity, gas, steam and air conditioning supply 405 55,645 1, 176, 121, 059 Water supply; sewerage, waste management and remediation activities 1, 125 44,595 122,352,222 Construction 2,286 343,866 619,793,841 Wholesale and retail trade; repair of motor vehicles, and motorcycles 116,220 1,584,579 8,959,490,268 Transportation and storage 2,950 224,063 734,998,709 Accommodation and food service activities 34,248 611,271 918,010,929 Information and communication 4, 112 192,600 709,919,834 Financial and insurance activities 38, 698 646, 866 2,758,468,398 Real estate activities 5,179 101,976 1,099,541,822 Professional, scientific and technical activities 5,435 164,793 279,861,962 Administrative and support service activities 6,481 1,310, 113 910,581,611 Education 13,624 409,241 222,647,989 Human health and social work activities 7,278 233,094 263,352,519 Arts, entertainment and recreation 3,307 75,635 241,453,752 Other service activities 10,022 82,575 49,432,041AutoSave O OFF M A P ? ~ C ... 3 NQS May2022 issue (as of 25th).xIs - Compatibility Mode Home Insert Draw Page Layout Formulas Data Review View DADM_Tools Tell me Share Comments xX Summary Histograms Scatterplots, Chi-Sq Test Regression Time Series Create Run About Unload Stats Correlations for Normality Analysis Analysis Decision Tree Simulation DADM_Tools DADM_Tools E41 X fx A B C D E G H K M N O P Q Multiple regression results for Establishment Regression summary Correlation matrix DUI AWN Multiple R R-square Adj R-Square Std Error Establishment Employment 0.714 0.510 0.479 19913.684 Establishment 1.000 0.714 Employment 0.714 1 000 7 ANOVA table 8 SS MS p-value Regression 1 6590739767 6590739767 16.620 0.001 10 Error 16 6344877080 396554817.5 11 Total 17 12935616846 12 Regression equation 95% conf intervals Multicollinearity checks 14 Coefficient Std Error t-stat o-value Lower Upper VIA R-square 15 Intercept 1348.477 5246.512 -0.216 .832 -14590.491 11893.53 16 Employment 0.040 0.010 4.077 0.001 0.019 0.060 1.000 0.000 17 18 Predicted values, residuals 19 Observation Observed Y Predicted Y Residual 20 269 3870.043 1 179.043 Residual vs Predicted Y 70000.000 Predicted Y vs Actual Y 21 260 -1.707 261.707 60000.000 22 23861 3251.833 -29390.833 60000.000 23 405 859.502 -454.502 50000.000 24 1125 421.041 703.959 40000.000 40000.000 25 O L DO YOUIA W N 2286 12296.035 -10010.035 26 16220 61527.201 4692.79 27 20000.000 30000.000 295 7542.284 -4592.284 28 34248 22906.595 1 1341.405 20000.000 29 4112 6293.840 -2181.840 0.000 10000.000- 30 11 38698 24318.996 14379.004 -10000.000 0.000 10600.000 20000.000 30000.000 40000.000 50000.000 60000.000 70000.000 31 12 5179 2697.904 2481.096 0.000 32 13 -20000.000 5435 5190.466 244.534 40000 60000 80000 100000 120000 140000 10000.000 33 14 6481 50636.462 44155.462 34 15 13624 14890.098 -1266.098 -40000.000 35 16 727 7900.631 622.631 36 17 3307 1652.700 1654.300 60000.000 37 18 10022 1928.077 8093.923 38 39 40 41 42 43 44 45 46 47 48 May2022 Business and Industry Business and Industry_Regr1 + Ready - - 100%