shoplifting: Shoplifting dataset
In SimultAnR: Correspondence and Simultaneous Analysis
Description
Usage
Format
References
Examples
Description
This dataset contains data from the CBS Statistics on shoplifting (Israels,
1987).
The data concerns persons suspected of shoplifting in 1977 and 1978 in Dutch
stores and big textile shops, classified according to the sex and age of the
person and the kind of stolen goods.
The categories of stolen goods are the following: clothing, clothing accesory,
provisions and/or tobacco, writing materials, books, records, household goods,
sweets, toys, jewelry, perfume, hobby and/or tools and other.
The age categories envisaged in the study are the following: less than 12,
12 to 14, 15 to 17, 18 to 20, 21 to 29, 30 to 39, 40 to 49, 50 to 64, 65 or
over.
Usage
1
Format
A data frame (13x18).
Rows represent the kinds of stolen goods.
The first 9 columns correspond to the ages of male suspected of shoplifting.
Columns 10 to 18 correspond to the ages of female suspected of shoplifting.
References
Israels, A. (1987).
Eigenvalue Techniques for Qualitative Data.
DSWO Press, Leiden.
Zarraga, A. & Goitisolo, B. (2002).
Methode factorielle pour l analyse simultanee de tableaux de contingence.
Revue de Statistique Appliquee,
L, 47–70
Zarraga, A. & Goitisolo, B. (2003).
Etude de la structure inter-tableaux a travers l Analyse Simultanee,
Revue de Statistique Appliquee,
LI, 39–60.
Examples
1
2
Example output
Mless12 M12to14 M15to17 M18to20 M21to29 M30to39 M40to49
clothing 81 138 304 384 942 359 178
clothing_accesory 66 204 193 149 297 109 53
provisions_tobacco 150 340 229 151 313 136 121
writing_materials 667 1409 527 84 92 36 36
books 67 259 258 146 251 96 48
records 24 272 368 141 167 67 29
household_goods 47 117 98 61 193 75 50
sweets 430 637 246 40 30 11 5
toys 743 684 116 13 16 16 6
jewelry 132 408 298 71 130 31 14
perfume 32 57 61 52 111 54 41
hobby_tools 197 547 402 138 280 200 152
other 209 550 454 252 624 195 88
M50to64 M65plus Fless12 F12to14 F15to17 F18to20 F21to29
clothing 137 45 71 241 477 436 1180
clothing_accesory 68 28 19 98 114 108 207
provisions_tobacco 171 145 59 111 58 76 132
writing_materials 37 17 224 346 91 18 30
books 56 41 19 60 50 32 61
records 27 7 7 32 27 12 12
household_goods 55 29 22 29 41 32 65
sweets 17 28 137 240 80 12 16
toys 3 8 113 98 14 10 21
jewelry 11 10 162 548 303 74 100
perfume 50 28 70 178 141 70 104
hobby_tools 211 111 15 29 9 14 30
other 90 34 24 58 72 67 157
F30to39 F40to49 F50to64 F65plus
clothing 1009 517 488 173
clothing_accesory 165 102 127 64
provisions_tobacco 121 93 214 215
writing_materials 27 23 27 13
books 43 31 57 44
records 9 7 13 0
household_goods 74 51 79 39
sweets 14 10 23 42
toys 31 8 17 6
jewelry 48 22 26 12
perfume 81 46 69 41
hobby_tools 36 24 35 11
other 107 66 64 55
SimultAnR documentation built on May 29, 2017, 10:59 a.m.
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Description Usage Format References Examples
Description
This dataset contains data from the CBS Statistics on shoplifting (Israels, 1987).
The data concerns persons suspected of shoplifting in 1977 and 1978 in Dutch stores and big textile shops, classified according to the sex and age of the person and the kind of stolen goods.
The categories of stolen goods are the following: clothing, clothing accesory, provisions and/or tobacco, writing materials, books, records, household goods, sweets, toys, jewelry, perfume, hobby and/or tools and other.
The age categories envisaged in the study are the following: less than 12, 12 to 14, 15 to 17, 18 to 20, 21 to 29, 30 to 39, 40 to 49, 50 to 64, 65 or over.
Usage
1 |
Format
A data frame (13x18). Rows represent the kinds of stolen goods. The first 9 columns correspond to the ages of male suspected of shoplifting. Columns 10 to 18 correspond to the ages of female suspected of shoplifting.
References
Israels, A. (1987). Eigenvalue Techniques for Qualitative Data. DSWO Press, Leiden.
Zarraga, A. & Goitisolo, B. (2002). Methode factorielle pour l analyse simultanee de tableaux de contingence. Revue de Statistique Appliquee, L, 47–70
Zarraga, A. & Goitisolo, B. (2003). Etude de la structure inter-tableaux a travers l Analyse Simultanee, Revue de Statistique Appliquee, LI, 39–60.
Examples
1 2 |
Example output
Mless12 M12to14 M15to17 M18to20 M21to29 M30to39 M40to49
clothing 81 138 304 384 942 359 178
clothing_accesory 66 204 193 149 297 109 53
provisions_tobacco 150 340 229 151 313 136 121
writing_materials 667 1409 527 84 92 36 36
books 67 259 258 146 251 96 48
records 24 272 368 141 167 67 29
household_goods 47 117 98 61 193 75 50
sweets 430 637 246 40 30 11 5
toys 743 684 116 13 16 16 6
jewelry 132 408 298 71 130 31 14
perfume 32 57 61 52 111 54 41
hobby_tools 197 547 402 138 280 200 152
other 209 550 454 252 624 195 88
M50to64 M65plus Fless12 F12to14 F15to17 F18to20 F21to29
clothing 137 45 71 241 477 436 1180
clothing_accesory 68 28 19 98 114 108 207
provisions_tobacco 171 145 59 111 58 76 132
writing_materials 37 17 224 346 91 18 30
books 56 41 19 60 50 32 61
records 27 7 7 32 27 12 12
household_goods 55 29 22 29 41 32 65
sweets 17 28 137 240 80 12 16
toys 3 8 113 98 14 10 21
jewelry 11 10 162 548 303 74 100
perfume 50 28 70 178 141 70 104
hobby_tools 211 111 15 29 9 14 30
other 90 34 24 58 72 67 157
F30to39 F40to49 F50to64 F65plus
clothing 1009 517 488 173
clothing_accesory 165 102 127 64
provisions_tobacco 121 93 214 215
writing_materials 27 23 27 13
books 43 31 57 44
records 9 7 13 0
household_goods 74 51 79 39
sweets 14 10 23 42
toys 31 8 17 6
jewelry 48 22 26 12
perfume 81 46 69 41
hobby_tools 36 24 35 11
other 107 66 64 55
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