This file is indexed.

/usr/share/gretl/data/data7-24.gdt is in gretl-data 1.9.9-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
<?xml version="1.0"?>
<!DOCTYPE gretldata SYSTEM "gretldata.dtd">

<gretldata name="data7-24" frequency="1" startobs="1" endobs="224" type="cross-section">
<description>
DATA7-24
 Data on the sale price and characteristics of homes in the Dove Canyon
 and Coto de Caza areas of Orange County, California, compiled by Jody
 Schimmel.  Dove Canyon is a neighborhod built around a golf course
 with single family tract homes with relatively small lots.  Coto de
 Caza is a more upscale area.  It is more rural with large custom
 homes, some with horse corrals.

    salepric = sale price in thousands of dollars
    sqft     = living area in square feet
    bedrms   = number of bed rooms
    baths    = number of baths
    garage   = number of car spaces
    age      = age of house in years
    city     = 1 for Coto de Caza and 0 for Dove Canyon
</description>
<variables count="7">
<variable name="salepric"
 label="sale price in thousands of dollars"/>
<variable name="sqft"
 label="living area in square feet"/>
<variable name="bedrms"
 label="number of bedrooms"/>
<variable name="baths"
 label="number of baths"/>
<variable name="garage"
 label="number of car spaces"/>
<variable name="age"
 label="age of house in years"/>
<variable name="city"
 label="1 for Coto de Caza and 0 for Dove Canyon"/>
</variables>
<observations count="224" labels="false">
<obs>350.000 2583 4 4.00 3 5 0 </obs>
<obs>360.000 3308 4 3.00 3 3 0 </obs>
<obs>365.000 2926 5 3.00 3 2 0 </obs>
<obs>372.000 3050 4 4.00 3 8 0 </obs>
<obs>373.000 3528 4 3.50 3 3 0 </obs>
<obs>373.000 2830 4 2.75 3 4 0 </obs>
<obs>375.000 3521 4 4.50 3 7 0 </obs>
<obs>349.000 3003 5 3.50 3 4 0 </obs>
<obs>380.000 3230 4 3.50 3 8 0 </obs>
<obs>380.000 3230 4 3.50 3 7 0 </obs>
<obs>380.000 3230 4 3.50 3 7 0 </obs>
<obs>380.000 2900 4 3.00 3 7 0 </obs>
<obs>380.000 3080 5 4.00 3 3 0 </obs>
<obs>370.000 3080 4 4.00 3 3 0 </obs>
<obs>380.000 3525 5 3.50 3 4 0 </obs>
<obs>385.000 3050 4 4.00 3 7 0 </obs>
<obs>385.000 3050 4 4.00 3 8 0 </obs>
<obs>389.000 3528 4 3.50 3 4 0 </obs>
<obs>390.000 2680 4 3.00 3 3 0 </obs>
<obs>390.000 3500 4 3.50 3 8 0 </obs>
<obs>390.000 3521 4 4.50 3 7 0 </obs>
<obs>390.000 2700 4 3.00 3 2 0 </obs>
<obs>392.000 2662 4 3.00 3 4 0 </obs>
<obs>392.000 3371 5 4.50 3 3 0 </obs>
<obs>392.000 3371 5 4.50 3 4 0 </obs>
<obs>393.000 3371 4 4.50 3 3 0 </obs>
<obs>395.000 2900 5 3.00 3 4 0 </obs>
<obs>395.000 3275 4 3.50 3 8 0 </obs>
<obs>399.000 3080 5 4.00 3 2 0 </obs>
<obs>400.000 3155 4 4.50 3 3 0 </obs>
<obs>400.000 3155 4 4.50 3 3 0 </obs>
<obs>400.000 3308 4 3.50 3 7 0 </obs>
<obs>399.900 3371 4 4.50 3 2 0 </obs>
<obs>400.000 3050 4 4.00 3 7 0 </obs>
<obs>401.000 2789 4 3.50 3 4 0 </obs>
<obs>402.500 3275 4 3.50 3 7 0 </obs>
<obs>405.000 3180 4 4.00 3 8 0 </obs>
<obs>405.000 3512 4 3.50 3 8 0 </obs>
<obs>407.000 3275 4 3.50 3 6 0 </obs>
<obs>410.000 3512 4 3.50 3 8 0 </obs>
<obs>410.000 2789 4 3.50 3 4 0 </obs>
<obs>412.000 3371 4 4.50 3 3 0 </obs>
<obs>412.000 3275 4 4.50 3 6 0 </obs>
<obs>415.984 3155 4 3.50 3 3 0 </obs>
<obs>416.000 3757 5 4.50 3 2 0 </obs>
<obs>418.000 3275 4 3.00 3 7 0 </obs>
<obs>419.950 3879 5 3.50 3 2 0 </obs>
<obs>425.000 3275 4 4.50 3 5 0 </obs>
<obs>425.000 3515 4 3.50 3 2 0 </obs>
<obs>426.000 3700 4 4.50 3 5 0 </obs>
<obs>430.000 3110 4 3.50 3 9 0 </obs>
<obs>430.000 3770 4 2.75 3 9 0 </obs>
<obs>432.000 3512 4 3.50 3 7 0 </obs>
<obs>432.000 3371 5 4.50 3 2 0 </obs>
<obs>434.000 3367 4 4.50 3 8 0 </obs>
<obs>435.000 3700 4 3.50 3 5 0 </obs>
<obs>439.402 3515 4 3.50 3 2 0 </obs>
<obs>440.000 3770 4 4.50 3 7 0 </obs>
<obs>440.000 3413 5 3.50 3 2 0 </obs>
<obs>565.000 3500 5 3.50 3 3 0 </obs>
<obs>605.000 3757 5 4.50 3 2 0 </obs>
<obs>609.900 3757 4 3.00 3 7 0 </obs>
<obs>620.000 3879 5 4.50 3 3 0 </obs>
<obs>653.000 4035 6 4.50 3 2 0 </obs>
<obs>670.000 4035 5 4.50 3 2 0 </obs>
<obs>440.000 3525 5 3.50 3 4 0 </obs>
<obs>445.000 3308 4 3.50 3 6 0 </obs>
<obs>459.900 3528 4 3.50 3 4 0 </obs>
<obs>449.960 3515 4 4.50 3 2 0 </obs>
<obs>450.000 3371 4 4.50 3 4 0 </obs>
<obs>450.000 3528 4 3.50 3 4 0 </obs>
<obs>459.500 3757 5 3.00 3 2 0 </obs>
<obs>460.000 2600 2 2.50 2 3 0 </obs>
<obs>549.950 2879 5 4.50 3 3 0 </obs>
<obs>460.000 4000 4 4.00 3 5 0 </obs>
<obs>462.000 3757 5 3.00 3 2 0 </obs>
<obs>449.900 3500 5 4.50 4 3 0 </obs>
<obs>464.820 3515 4 4.50 3 2 0 </obs>
<obs>464.900 3308 4 3.50 3 6 0 </obs>
<obs>465.000 3100 4 4.00 3 8 0 </obs>
<obs>457.325 3879 5 4.50 3 2 0 </obs>
<obs>449.950 3515 4 4.50 3 3 0 </obs>
<obs>475.000 3929 4 3.50 3 5 0 </obs>
<obs>475.000 4000 4 4.00 3 6 0 </obs>
<obs>419.950 3879 5 4.50 3 2 0 </obs>
<obs>479.950 4136 5 4.50 3 2 0 </obs>
<obs>480.000 3512 4 4.50 3 9 0 </obs>
<obs>482.750 3879 5 4.50 3 2 0 </obs>
<obs>489.950 3879 5 4.50 3 2 0 </obs>
<obs>490.000 4035 6 4.50 3 2 0 </obs>
<obs>495.000 3500 5 4.50 4 4 0 </obs>
<obs>497.500 3770 4 3.50 3 8 0 </obs>
<obs>499.900 4035 6 4.50 3 2 0 </obs>
<obs>500.000 3800 4 3.50 3 8 0 </obs>
<obs>510.000 4035 6 4.50 3 2 0 </obs>
<obs>510.000 3500 4 4.50 4 4 0 </obs>
<obs>514.900 4018 4 3.50 3 8 0 </obs>
<obs>514.900 3308 4 3.50 3 8 0 </obs>
<obs>527.500 3757 5 3.00 3 2 0 </obs>
<obs>535.000 4035 6 4.50 3 2 0 </obs>
<obs>535.000 3879 5 4.50 3 3 0 </obs>
<obs>539.000 3854 4 4.50 3 3 0 </obs>
<obs>539.000 3500 5 4.50 3 4 0 </obs>
<obs>547.000 4035 6 4.50 3 2 0 </obs>
<obs>552.000 4136 5 4.50 3 3 0 </obs>
<obs>556.700 3700 5 4.50 4 3 0 </obs>
<obs>480.000 2865 4 2.50 3 11 1 </obs>
<obs>485.000 3384 5 4.00 3 5 1 </obs>
<obs>485.000 3568 5 4.50 3 8 1 </obs>
<obs>487.000 3384 5 4.50 3 4 1 </obs>
<obs>490.000 3305 5 4.50 3 9 1 </obs>
<obs>492.000 3227 4 3.50 3 4 1 </obs>
<obs>495.000 3295 4 4.00 3 8 1 </obs>
<obs>504.000 3259 4 3.50 3 5 1 </obs>
<obs>505.000 3668 4 3.50 3 7 1 </obs>
<obs>517.000 3685 4 4.50 3 9 1 </obs>
<obs>520.000 3350 5 2.75 3 3 1 </obs>
<obs>525.000 2800 4 2.50 3 11 1 </obs>
<obs>526.000 3170 4 3.00 3 8 1 </obs>
<obs>529.000 3300 4 3.00 3 9 1 </obs>
<obs>530.000 3475 4 3.50 3 11 1 </obs>
<obs>530.000 3380 5 4.50 3 9 1 </obs>
<obs>531.050 3620 5 4.50 3 1 1 </obs>
<obs>532.500 3305 5 4.50 3 9 1 </obs>
<obs>535.000 3475 4 2.50 2 19 1 </obs>
<obs>535.000 3305 5 4.50 3 8 1 </obs>
<obs>535.000 3900 4 3.50 3 8 1 </obs>
<obs>540.000 4389 5 4.50 3 8 1 </obs>
<obs>540.000 3305 5 4.00 3 9 1 </obs>
<obs>545.000 3500 4 3.50 3 11 1 </obs>
<obs>547.500 3369 4 3.50 3 10 1 </obs>
<obs>571.000 3485 4 3.50 3 11 1 </obs>
<obs>550.000 3920 5 3.50 3 6 1 </obs>
<obs>555.000 3475 4 3.50 3 10 1 </obs>
<obs>555.000 3781 4 3.50 3 8 1 </obs>
<obs>560.000 2735 4 2.75 3 11 1 </obs>
<obs>560.000 3390 4 4.50 3 8 1 </obs>
<obs>560.000 3700 4 4.50 3 9 1 </obs>
<obs>562.000 3668 5 4.50 3 7 1 </obs>
<obs>565.000 4089 5 3.50 2 8 1 </obs>
<obs>565.000 4170 5 4.50 3 1 1 </obs>
<obs>570.000 2812 4 3.00 3 10 1 </obs>
<obs>570.000 4010 4 4.50 3 9 1 </obs>
<obs>570.000 3379 4 4.50 3 9 1 </obs>
<obs>575.000 3920 5 4.50 3 5 1 </obs>
<obs>575.000 3865 4 4.50 3 12 1 </obs>
<obs>575.000 4579 5 3.50 3 8 1 </obs>
<obs>580.000 3968 4 4.50 4 2 1 </obs>
<obs>580.000 3750 4 3.00 4 8 1 </obs>
<obs>583.000 4000 5 3.50 3 8 1 </obs>
<obs>585.000 3457 4 3.00 3 9 1 </obs>
<obs>589.000 3400 5 4.50 3 9 1 </obs>
<obs>590.000 3427 4 3.50 3 2 1 </obs>
<obs>591.000 4500 4 3.50 3 8 1 </obs>
<obs>597.500 3970 4 4.50 3 9 1 </obs>
<obs>600.000 4818 5 4.00 3 10 1 </obs>
<obs>600.000 4600 6 5.00 3 6 1 </obs>
<obs>600.000 3685 4 4.50 3 8 1 </obs>
<obs>600.000 3457 4 3.50 3 11 1 </obs>
<obs>610.000 3700 4 3.50 3 8 1 </obs>
<obs>620.000 4100 5 4.00 3 1 1 </obs>
<obs>625.000 4300 4 3.50 3 2 1 </obs>
<obs>625.000 3820 5 4.50 3 5 1 </obs>
<obs>627.500 4160 4 4.50 3 1 1 </obs>
<obs>629.900 3712 6 5.50 3 1 1 </obs>
<obs>640.000 4200 5 4.50 3 7 1 </obs>
<obs>645.000 4000 4 3.50 3 7 1 </obs>
<obs>651.000 4500 5 3.50 3 9 1 </obs>
<obs>657.000 3818 4 4.00 3 13 1 </obs>
<obs>663.000 3885 3 4.50 4 2 1 </obs>
<obs>675.000 3968 4 4.50 3 2 1 </obs>
<obs>690.000 4839 5 3.50 3 9 1 </obs>
<obs>695.000 3637 5 4.50 3 2 1 </obs>
<obs>700.000 4335 4 4.50 3 2 1 </obs>
<obs>700.000 4300 4 3.50 3 3 1 </obs>
<obs>710.000 4870 5 4.50 3 7 1 </obs>
<obs>712.950 4459 5 4.50 4 0 1 </obs>
<obs>720.000 3741 4 3.50 3 10 1 </obs>
<obs>730.000 4400 5 4.50 3 8 1 </obs>
<obs>730.000 4500 5 4.50 3 2 1 </obs>
<obs>740.000 4579 5 3.50 3 8 1 </obs>
<obs>749.000 3450 4 3.50 3 8 1 </obs>
<obs>750.000 4402 4 4.50 3 9 1 </obs>
<obs>750.000 4350 4 3.50 3 6 1 </obs>
<obs>760.000 4400 5 4.00 3 8 1 </obs>
<obs>765.000 4600 5 4.00 3 2 1 </obs>
<obs>774.950 4024 4 3.50 4 0 1 </obs>
<obs>780.000 3900 5 4.50 3 2 1 </obs>
<obs>795.000 3900 4 4.50 3 4 1 </obs>
<obs>814.000 4000 5 4.50 3 7 1 </obs>
<obs>842.000 4569 5 4.50 3 7 1 </obs>
<obs>880.000 4581 4 4.50 3 2 1 </obs>
<obs>885.000 5000 5 5.50 3 6 1 </obs>
<obs>920.000 5000 5 4.50 5 8 1 </obs>
<obs>925.000 4650 4 5.50 4 2 1 </obs>
<obs>925.000 4300 5 3.50 3 9 1 </obs>
<obs>925.000 4400 5 4.00 3 7 1 </obs>
<obs>944.000 4387 5 4.50 4 1 1 </obs>
<obs>981.000 4970 5 4.50 4 1 1 </obs>
<obs>985.000 5126 4 3.50 3 9 1 </obs>
<obs>994.000 5076 5 4.50 4 1 1 </obs>
<obs>2600.000 8685 6 5.00 5 16 1 </obs>
<obs>2900.000 11000 5 7.50 3 9 1 </obs>
<obs>1010.000 5517 5 5.50 4 1 1 </obs>
<obs>1100.000 5500 5 5.00 3 2 1 </obs>
<obs>1100.000 4900 4 4.50 3 2 1 </obs>
<obs>1112.000 5800 5 4.50 3 2 1 </obs>
<obs>1120.000 8300 6 5.50 3 7 1 </obs>
<obs>1135.000 5506 6 5.50 4 1 1 </obs>
<obs>1235.000 6000 6 5.50 5 5 1 </obs>
<obs>1350.000 5475 5 5.50 4 2 1 </obs>
<obs>1380.000 6649 7 5.50 3 5 1 </obs>
<obs>1395.000 5400 3 4.50 4 9 1 </obs>
<obs>1400.000 10000 6 8.00 4 3 1 </obs>
<obs>1400.000 5862 5 6.50 4 2 1 </obs>
<obs>1425.000 7000 6 4.50 3 9 1 </obs>
<obs>1475.000 6338 6 6.50 4 8 1 </obs>
<obs>1520.000 6593 6 7.50 5 2 1 </obs>
<obs>1600.000 7000 6 7.50 5 5 1 </obs>
<obs>1625.000 8300 6 6.50 4 8 1 </obs>
<obs>1750.000 7338 6 7.50 4 1 1 </obs>
<obs>1775.000 9500 6 5.50 4 8 1 </obs>
<obs>1800.000 7948 7 7.40 5 1 1 </obs>
<obs>2500.500 9000 7 7.50 7 11 1 </obs>
</observations>
</gretldata>