[Gretl-users] It is easy to make program in Gretl

I found that it is easy to make program in Gretl system. It is more easy than SAS system. When I write Gretl script, I use help to find the right syntax. Here is the result of my program weightarea.gdt gretl version 1.9.11 Current session: 2012-12-31 12:38 ? open e:\weightarea.xls Listing 7 variables: 0) const 1) weight 2) area 3) length 4) width 5) hightf 6) hightb ? summary weight area Mean Median Minimum Maximum weight 212.50 212.50 25.500 399.50 area 4640.8 4640.8 550.60 8731.0 Std. Dev. C.V. Skewness Ex. kurtosis weight 110.34 0.51926 -5.0141e-021 -1.2005 area 2413.5 0.52006 2.2017e-015 -1.2005 5% perc. 95% perc. IQ range Missing obs. weight 39.250 385.75 192.50 0 area 851.35 8430.3 4210.5 0 ? print "weight area dataset" weight area dataset ? print weight area weight: Full data range: 1 - 69 (n = 69) 25.5000 31.0000 36.5000 42.0000 47.5000 53.0000 58.5000 64.0000 69.5000 75.0000 80.5000 86.0000 91.5000 97.0000 102.500 108.000 113.500 119.000 124.500 130.000 135.500 141.000 146.500 152.000 157.500 163.000 168.500 174.000 179.500 185.000 190.500 196.000 201.500 207.000 212.500 218.000 223.500 229.000 234.500 240.000 245.500 251.000 256.500 262.000 267.500 273.000 278.500 284.000 289.500 295.000 300.500 306.000 311.500 317.000 322.500 328.000 333.500 339.000 344.500 350.000 355.500 361.000 366.500 372.000 377.500 383.000 388.500 394.000 399.500 area: Full data range: 1 - 69 (n = 69) 550.600 670.900 791.200 911.500 1031.80 1152.10 1272.40 1392.70 1513.00 1633.30 1753.60 1873.90 1994.20 2114.50 2234.80 2355.10 2475.40 2595.70 2716.00 2836.30 2956.60 3076.90 3197.20 3317.50 3437.80 3558.10 3678.40 3798.70 3919.00 4039.30 4159.60 4279.90 4400.20 4520.50 4640.80 4761.10 4881.40 5001.70 5122.00 5242.30 5362.60 5482.90 5603.20 5723.50 5843.80 5964.10 6084.40 6204.70 6325.00 6445.30 6565.60 6685.90 6806.20 6926.50 7046.80 7167.10 7287.40 7407.70 7528.00 7648.30 7768.60 7888.90 8009.20 8129.50 8249.80 8370.10 8490.40 8610.70 8731.00 ? ols area 0 weight Model 1: OLS, using observations 1-69 Dependent variable: area coefficient std. error t-ratio p-value ------------------------------------------------------------- const −7.15455 7.27347e-013 −9.836e+012 0.0000 *** weight 21.8727 0.000000 7.189e+015 0.0000 *** Mean dependent var 4640.800 S.D. dependent var 2413.507 Sum squared resid 5.13e-22 S.E. of regression 2.77e-12 R-squared 1.000000 Adjusted R-squared 1.000000 Log-likelihood 1739.390 Akaike criterion −3474.781 Schwarz criterion −3470.312 Hannan-Quinn −3473.008 Nabil Brandl <http://nabilnabil.homestead.com/> http://nabilnabil.homestead.com