library(wooldridge)
data(wage2)head(wage2)## wage hours IQ KWW educ exper tenure age married black south urban sibs
## 1 769 40 93 35 12 11 2 31 1 0 0 1 1
## 2 808 50 119 41 18 11 16 37 1 0 0 1 1
## 3 825 40 108 46 14 11 9 33 1 0 0 1 1
## 4 650 40 96 32 12 13 7 32 1 0 0 1 4
## 5 562 40 74 27 11 14 5 34 1 0 0 1 10
## 6 1400 40 116 43 16 14 2 35 1 1 0 1 1
## brthord meduc feduc lwage
## 1 2 8 8 6.645091
## 2 NA 14 14 6.694562
## 3 2 14 14 6.715384
## 4 3 12 12 6.476973
## 5 6 6 11 6.331502
## 6 2 8 NA 7.244227?wage2## httpd yardım sunucusu başlatılıyor ... tamamlandıwage2 Description Wooldridge Source: M. Blackburn and D. Neumark (1992), “Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials,” Quarterly Journal of Economics 107, 1421-1436. Professor Neumark kindly provided the data, of which I used just the data for 1980. Data loads lazily.
Usage data(‘wage2’) Format A data.frame with 935 observations on 17 variables:
wage: monthly earnings
hours: average weekly hours
IQ: IQ score
KWW: knowledge of world work score
educ: years of education
exper: years of work experience
tenure: years with current employer
age: age in years
married: =1 if married
black: =1 if black
south: =1 if live in south
urban: =1 if live in SMSA
sibs: number of siblings
brthord: birth order
meduc: mother’s education
feduc: father’s education
lwage: natural log of wage
Notes As with WAGE1.RAW, there are some clear inconsistencies among the variables tenure, exper, and age. I have not been able to track down the causes, and so any changes would be effectively arbitrary. Instead, I am using the data as provided by the authors of the above QJE article.
Used in Text: pages 64, 106, 111, 165, 218-219, 220-221, 262, 310-312, 338, 519-520, 534, 546-547, 549, 678
library(wooldridge)data("rdchem")help("rdchem")rdchem
Description Wooldridge Source: From Businessweek R&D Scoreboard, October 25, 1991. Data loads lazily.
Usage data(‘rdchem’) Format A data.frame with 32 observations on 8 variables:
rd: R&D spending, millions
sales: firm sales, millions
profits: profits, millions
rdintens: rd as percent of sales
profmarg: profits as percent of sales
salessq: sales^2
lsales: log(sales)
lrd: log(rd)
Notes It would be interesting to collect more recent data and see whether the R&D/firm size relationship has changed over time.
Used in Text: pages 64, 139-140, 159-160, 204, 218, 327-329, 339
head(rdchem, 1-20)## rd sales profits rdintens profmarg salessq lsales lrd
## 1 430.6 4570.2 186.9 9.421906 4.0895362 2.088673e+07 8.427312 6.0651798
## 2 59.0 2830.0 467.0 2.084806 16.5017662 8.008900e+06 7.948032 4.0775375
## 3 23.5 596.8 107.4 3.937668 17.9959793 3.561702e+05 6.391582 3.1570003
## 4 3.5 133.6 -4.3 2.619760 -3.2185628 1.784896e+04 4.894850 1.2527629
## 5 1.7 42.0 8.0 4.047619 19.0476189 1.764000e+03 3.737670 0.5306283
## 6 8.4 390.0 47.3 2.153846 12.1282053 1.521000e+05 5.966147 2.1282318
## 7 2.5 93.9 0.9 2.662407 0.9584664 8.817210e+03 4.542231 0.9162908
## 8 39.9 907.9 77.4 4.394757 8.5251675 8.242824e+05 6.811134 3.6863763
## 9 1136.0 19773.0 2563.0 5.745208 12.9621201 3.909715e+08 9.892073 7.0352688
## 10 1428.0 39709.0 4154.0 3.596162 10.4611044 1.576805e+09 10.589334 7.2640300
## 11 45.3 2936.5 93.7 1.542653 3.1908734 8.623032e+06 7.984974 3.8133070
## 12 65.2 2513.8 355.4 2.593683 14.1379576 6.319191e+06 7.829551 4.1774592
## 13 20.3 1124.8 45.3 1.804765 4.0273824 1.265175e+06 7.025361 3.0106208RDCHEM=β0+β1rd+β2sales+u
RDCHEM=430,6rd+4570,2sales+u
rd: Ar-Ge harcamaları, milyonlar ve satışlar: sabit satışlar, milyonlar arasında pozitif bir ilişki vardır, biri arttıkça diğeri de artmaktadır.
library(wooldridge)
data("hprice1")help("hprice1")hprice1 Description Wooldridge Source: Collected from the real estate pages of the Boston Globe during 1990. These are homes that sold in the Boston, MA area. Data loads lazily.
Usage data(‘hprice1’) Format A data.frame with 88 observations on 10 variables:
price: house price, $1000s
assess: assessed value, $1000s
bdrms: number of bdrms
lotsize: size of lot in square feet
sqrft: size of house in square feet
colonial: =1 if home is colonial style
lprice: log(price)
lassess: log(assess
llotsize: log(lotsize)
lsqrft: log(sqrft)
Notes Typically, it is very easy to obtain data on selling prices and characteristics of homes, using publicly available data bases. It is interesting to match the information on houses with other information – such as local crime rates, quality of the local schools, pollution levels, and so on – and estimate the effects of such variables on housing prices.
Used in Text: pages 110, 153-154, 160-161, 165, 211-212, 221, 222, 234, 278, 280, 299, 307
head(hprice1, 1-10)## price assess bdrms lotsize sqrft colonial lprice lassess llotsize
## 1 300.000 349.1 4 6126 2438 1 5.703783 5.855359 8.720297
## 2 370.000 351.5 3 9903 2076 1 5.913503 5.862210 9.200593
## 3 191.000 217.7 3 5200 1374 0 5.252274 5.383118 8.556414
## 4 195.000 231.8 3 4600 1448 1 5.273000 5.445875 8.433811
## 5 373.000 319.1 4 6095 2514 1 5.921578 5.765504 8.715224
## 6 466.275 414.5 5 8566 2754 1 6.144775 6.027073 9.055556
## 7 332.500 367.8 3 9000 2067 1 5.806640 5.907539 9.104980
## 8 315.000 300.2 3 6210 1731 1 5.752573 5.704449 8.733916
## 9 206.000 236.1 3 6000 1767 0 5.327876 5.464255 8.699514
## 10 240.000 256.3 3 2892 1890 0 5.480639 5.546349 7.969704
## 11 285.000 314.0 4 6000 2336 1 5.652489 5.749393 8.699514
## 12 300.000 416.5 5 7047 2634 1 5.703783 6.031887 8.860357
## 13 405.000 434.0 3 12237 3375 1 6.003887 6.073044 9.412219
## 14 212.000 279.3 3 6460 1899 0 5.356586 5.632287 8.773385
## 15 265.000 287.5 3 6519 2312 1 5.579730 5.661223 8.782476
## 16 227.400 232.9 4 3597 1760 1 5.426711 5.450609 8.187856
## 17 240.000 303.8 4 5922 2000 0 5.480639 5.716370 8.686430
## 18 285.000 305.6 3 7123 1774 1 5.652489 5.722277 8.871084
## 19 268.000 266.7 3 5642 1376 1 5.590987 5.586124 8.637994
## 20 310.000 326.0 4 8602 1835 1 5.736572 5.786897 9.059750
## 21 266.000 294.3 3 5494 2048 1 5.583496 5.684599 8.611412
## 22 270.000 318.8 3 7800 2124 1 5.598422 5.764564 8.961879
## 23 225.000 294.2 3 6003 1768 0 5.416101 5.684260 8.700015
## 24 150.000 208.0 4 5218 1732 0 5.010635 5.337538 8.559870
## 25 247.000 239.7 3 9425 1440 1 5.509388 5.479388 9.151121
## 26 275.000 294.1 3 6114 1932 0 5.616771 5.683920 8.718336
## 27 230.000 267.4 3 6710 1932 0 5.438079 5.588746 8.811355
## 28 343.000 359.9 3 8577 2106 1 5.837730 5.885826 9.056840
## 29 477.500 478.1 7 8400 3529 1 6.168564 6.169820 9.035987
## 30 350.000 355.3 4 9773 2051 1 5.857933 5.872962 9.187379
## 31 230.000 217.8 4 4806 1573 1 5.438079 5.383577 8.477620
## 32 335.000 385.0 4 15086 2829 0 5.814130 5.953243 9.621523
## 33 251.000 224.3 3 5763 1630 1 5.525453 5.412984 8.659213
## 34 235.000 251.9 4 6383 1840 1 5.459586 5.529032 8.761394
## 35 361.000 354.9 4 9000 2066 1 5.888878 5.871836 9.104980
## 36 190.000 212.5 4 3500 1702 0 5.247024 5.358942 8.160519
## 37 360.000 452.4 4 10892 2750 1 5.886104 6.114567 9.295784
## 38 575.000 518.1 5 15634 3880 1 6.354370 6.250168 9.657204
## 39 209.001 289.4 4 6400 1854 1 5.342339 5.667810 8.764053
## 40 225.000 268.1 2 8880 1421 0 5.416101 5.591360 9.091557
## 41 246.000 278.5 3 6314 1662 1 5.505332 5.629418 8.750525
## 42 713.500 655.4 5 28231 3331 1 6.570182 6.485246 10.248176
## 43 248.000 273.3 4 7050 1656 1 5.513429 5.610570 8.860783
## 44 230.000 212.1 3 5305 1171 0 5.438079 5.357058 8.576406
## 45 375.000 354.0 5 6637 2293 1 5.926926 5.869297 8.800415
## 46 265.000 252.1 3 7834 1764 1 5.579730 5.529826 8.966228
## 47 313.000 324.0 3 1000 2768 0 5.746203 5.780744 6.907755
## 48 417.500 475.5 4 8112 3733 0 6.034285 6.164367 9.001100
## 49 253.000 256.8 3 5850 1536 1 5.533390 5.548297 8.674197
## 50 315.000 279.2 4 6660 1638 1 5.752573 5.631928 8.803875
## 51 264.000 313.9 3 6637 1972 1 5.575949 5.749074 8.800415
## 52 255.000 279.8 2 15267 1478 0 5.541264 5.634075 9.633449
## 53 210.000 198.7 3 5146 1408 1 5.347107 5.291796 8.545975
## 54 180.000 221.5 3 6017 1812 1 5.192957 5.400423 8.702344
## 55 250.000 268.4 3 8410 1722 1 5.521461 5.592478 9.037177
## 56 250.000 282.3 4 5625 1780 1 5.521461 5.642970 8.634976
## 57 209.000 230.7 4 5600 1674 1 5.342334 5.441118 8.630522
## 58 258.000 287.0 4 6525 1850 1 5.552959 5.659482 8.783396
## 59 289.000 298.7 3 6060 1925 1 5.666427 5.699440 8.709465
## 60 316.000 314.6 4 5539 2343 0 5.755742 5.751302 8.619569
## 61 225.000 291.0 3 7566 1567 0 5.416101 5.673323 8.931419
## 62 266.000 286.4 4 5484 1664 1 5.583496 5.657390 8.609590
## 63 310.000 253.6 6 5348 1386 1 5.736572 5.535758 8.584478
## 64 471.250 482.0 5 15834 2617 1 6.155389 6.177944 9.669915
## 65 335.000 384.3 4 8022 2321 1 5.814130 5.951424 8.989944
## 66 495.000 543.6 4 11966 2638 1 6.204558 6.298213 9.389825
## 67 279.500 336.5 4 8460 1915 1 5.633002 5.818598 9.043104
## 68 380.000 515.1 4 15105 2589 1 5.940171 6.244361 9.622781
## 69 325.000 437.0 4 10859 2709 0 5.783825 6.079933 9.292749
## 70 220.000 263.4 3 6300 1587 1 5.393628 5.573674 8.748305
## 71 215.000 300.4 3 11554 1694 0 5.370638 5.705115 9.354787
## 72 240.000 250.7 3 6000 1536 1 5.480639 5.524257 8.699514
## 73 725.000 708.6 5 31000 3662 0 6.586172 6.563291 10.341743
## 74 230.000 276.3 3 4054 1736 1 5.438079 5.621487 8.307459
## 75 306.000 388.6 2 20700 2205 0 5.723585 5.962551 9.937889
## 76 425.000 252.5 3 5525 1502 0 6.052089 5.531411 8.617039
## 77 318.000 295.2 4 92681 1696 1 5.762052 5.687653 11.436919
## 78 330.000 359.5 3 8178 2186 1 5.799093 5.884714 9.009203
## 79 246.000 276.2 4 5944 1928 1 5.505332 5.621125 8.690138
## lsqrft
## 1 7.798934
## 2 7.638198
## 3 7.225482
## 4 7.277938
## 5 7.829630
## 6 7.920810
## 7 7.633853
## 8 7.456455
## 9 7.477038
## 10 7.544332
## 11 7.756196
## 12 7.876259
## 13 8.124150
## 14 7.549083
## 15 7.745868
## 16 7.473069
## 17 7.600903
## 18 7.480992
## 19 7.226936
## 20 7.514800
## 21 7.624619
## 22 7.661057
## 23 7.477604
## 24 7.457032
## 25 7.272398
## 26 7.566311
## 27 7.566311
## 28 7.652546
## 29 8.168770
## 30 7.626083
## 31 7.360740
## 32 7.947679
## 33 7.396335
## 34 7.517521
## 35 7.633369
## 36 7.439559
## 37 7.919356
## 38 8.263591
## 39 7.525101
## 40 7.259116
## 41 7.415777
## 42 8.111028
## 43 7.412160
## 44 7.065613
## 45 7.737616
## 46 7.475339
## 47 7.925880
## 48 8.224967
## 49 7.336937
## 50 7.401231
## 51 7.586803
## 52 7.298445
## 53 7.249926
## 54 7.502186
## 55 7.451241
## 56 7.484369
## 57 7.422971
## 58 7.522941
## 59 7.562681
## 60 7.759187
## 61 7.356918
## 62 7.416980
## 63 7.234177
## 64 7.869784
## 65 7.749753
## 66 7.877776
## 67 7.557473
## 68 7.859027
## 69 7.904335
## 70 7.369601
## 71 7.434848
## 72 7.336937
## 73 8.205765
## 74 7.459339
## 75 7.698483
## 76 7.314553
## 77 7.436028
## 78 7.689829
## 79 7.564239price=12438sqrf+4bdrms+U
Bir ev 6126 ve ayak kare 140 olduğunda tahmini fiyat 6126*140=857.640$
Fiyattaki yüzde bir birimlik artışı, yatak odalarının sayısı ve ayak kare de o kadar artmaktadır.
Bu ev için tahmin edilen satış değeri=1000$