PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ. Příloha A. Metoda nejmenších čtverců Prodej bytů

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Příloha A Metoda nejmenších čtverců Prodej bytů i

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 2 3 TOT. 1 7 33 40 2 1 18 125 144 2.5 1 72 73 3.5 1 10 35 46 4 4 1 5 4.5 8 8 5 2 2 Cross-tabulation of Pokoj(rows) against Vlastnictvi(columns) Pearson chi-square test = 39.2872(18 df, p-value = 0.00260706) 1 2 TOT. 0.5 1 1 1 16 27 43 2 13 127 140 2.5 73 73 3 16 4 20 3.5 26 26 4 5 2 7 4.5 6 6 5 2 2 Cross-tabulation of Pokoj(rows) against Zdivo(columns) Pearson chi-square test = 121.441(9 df, p-value = 6.77312e-022) 1 2 TOT. 1 3 3 2 27 12 39 3 24 252 276 Cross-tabulation of Vlastnictvi(rows) against Zdivo(columns) Pearson chi-square test = 93.5716(2 df, p-value = 4.79941e-021) ii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 2 3 4 TOT. 1 6 2 15 27 50 2 9 4 24 97 134 3 5 3 17 79 104 4 2 2 3 12 19 4.5 9 9 5 1 1 2 Cross-tabulation of Pokoj(rows) against Stav(columns) Pearson chi-square test = 17.3328(27 df, p-value = 0.922591) 1 2 3 4 TOT. 1 1 2 3 2 10 5 21 3 39 3 11 6 37 222 276 Cross-tabulation of Vlastnictvi(rows) against Stav(columns) Pearson chi-square test = 98.5048(6 df, p-value = 5.14523e-019) 1 2 3 4 TOT. 1 13 7 29 2 51 2 9 4 31 223 267 Cross-tabulation of Zdivo(rows) against Stav(columns) Pearson chi-square test = 133.611(3 df, p-value = 9.01261e-029) iii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 25 19 44 1.5 1 1 2 43 96 139 2.5 20 53 73 3 9 9 3.5 12 13 25 4 1 1 4.5 4 20 24 5 1 1 2 Cross-tabulation of Pokoj(rows) against Balkon(columns) Pearson chi-square test = 37.1074(9 df, p-value = 2.5182e-005) 1 2 1 3 2 28 11 39 3 86 190 276 Cross-tabulation of Vlastnictvi(rows) against Balkon(columns) Pearson chi-square test = 25.5422(2 df, p-value = 2.84167e-006) 1 33 18 51 2 83 184 267 Cross-tabulation of Zdivo(rows) against Balkon(columns) Pearson chi-square test = 20.8876(1 df, p-value = 4.87045e-006) 1 12 10 22 2 6 5 11 3 41 19 60 4 57 168 225 Cross-tabulation of Stav(rows) against Balkon(columns) Pearson chi-square test = 42.9854(3 df, p-value = 2.47864e-009) iv

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0.5 1 1 1 49 49 1.5 1 1 2 5 5 2.5 128 35 163 3 24 45 69 3.5 3 3 4 8 16 24 5 1 2 3 Cross-tabulation of Pokoj(rows) against Terasa(columns) Pearson chi-square test = 87.5309(9 df, p-value = 5.08778e-015) 1 2 1 3 2 39 39 3 179 97 276 Cross-tabulation of Vlastnictvi(rows) against Terasa(columns) Pearson chi-square test = 19.8057(2 df, p-value = 5.00309e-005) 1 51 51 2 169 98 267 Cross-tabulation of Zdivo(rows) against Terasa(columns) Pearson chi-square test = 27.0576(1 df, p-value = 1.97481e-007) 1 22 22 2 11 11 3 56 4 60 4 131 94 225 Cross-tabulation of Stav(rows) against Terasa(columns) Pearson chi-square test = 43.7916(3 df, p-value = 1.67115e-009) v

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 80 36 116 1 140 62 202 Cross-tabulation of Balkon(rows) against Terasa(columns) Pearson chi-square test = 0.00402855(1 df, p-value = 0.949392) 1.5 142 7 149 2.5 90 17 107 3.5 35 12 47 4 6 6 4.5 3 4 7 5 1 1 2 Cross-tabulation of Pokoj(rows) against Lodzie(columns) Pearson chi-square test = 31.994(9 df, p-value = 0.000199603) 1 3 3 2 20 19 39 3 254 22 276 Cross-tabulation of Vlastnictvi(rows) against Lodzie(columns) Pearson chi-square test = 50.966(2 df, p-value = 8.56798e-012) 1 26 25 51 2 251 16 267 Cross-tabulation of Zdivo(rows) against Lodzie(columns) Pearson chi-square test = 70.5877(1 df, p-value = 4.40253e-017) vi

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 16 6 22 2 8 3 11 3 36 24 60 4 217 8 225 Cross-tabulation of Stav(rows) against Lodzie(columns) Pearson chi-square test = 62.7992(3 df, p-value = 1.48256e-013) 0 77 39 116 1 200 2 202 Cross-tabulation of Balkon(rows) against Lodzie(columns) Pearson chi-square test = 69.8591(1 df, p-value = 6.36969e-017) 0 180 40 220 1 97 1 98 Cross-tabulation of Terasa(rows) against Lodzie(columns) Pearson chi-square test = 17.7794(1 df, p-value = 2.48049e-005) 0.5 1 1 1 34 7 41 2 78 43 121 2.5 52 42 94 3 3 3 3.5 21 22 43 4 6 1 7 4.5 6 6 5 1 1 2 Cross-tabulation of Pokoj(rows) against Sklep(columns) Pearson chi-square test = 26.7938(9 df, p-value = 0.00151294) vii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 2 1 3 2 11 28 39 3 183 93 276 Cross-tabulation of Vlastnictvi(rows) against Sklep(columns) Pearson chi-square test = 21.009(2 df, p-value = 2.74135e-005) 1 19 32 51 2 177 90 267 Cross-tabulation of Zdivo(rows) against Sklep(columns) Pearson chi-square test = 15.2687(1 df, p-value = 9.32471e-005) 1 11 11 22 2 11 11 3 24 36 60 4 161 64 225 Cross-tabulation of Stav(rows) against Sklep(columns) Pearson chi-square test = 40.1731(3 df, p-value = 9.79192e-009) 0 62 54 116 1 134 68 202 Cross-tabulation of Balkon(rows) against Sklep(columns) Pearson chi-square test = 5.17626(1 df, p-value = 0.0228976) viii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 111 109 220 1 85 13 98 Cross-tabulation of Terasa(rows) against Sklep(columns) Pearson chi-square test = 37.7397(1 df, p-value = 8.08437e-010) 0 184 93 277 1 12 29 41 Cross-tabulation of Lodzie(rows) against Sklep(columns) Pearson chi-square test = 20.8532(1 df, p-value = 4.95866e-006) 0.5 1 1 1 49 49 1.5 4 4 2 127 3 130 2.5 5 5 3 92 2 94 4 25 7 32 4.5 1 1 5 2 2 Cross-tabulation of Pokoj(rows) against Garaz(columns) Pearson chi-square test = 71.915(9 df, p-value = 6.40621e-012) 1 3 3 2 39 39 3 262 14 276 Cross-tabulation of Vlastnictvi(rows) against Garaz(columns) Pearson chi-square test = 2.22855(2 df, p-value = 0.328154) ix

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 50 1 51 2 254 13 267 Cross-tabulation of Zdivo(rows) against Garaz(columns) Pearson chi-square test = 0.860467(1 df, p-value = 0.353608) 1 21 1 22 2 11 11 3 56 4 60 4 216 9 225 Cross-tabulation of Stav(rows) against Garaz(columns) Pearson chi-square test = 1.32509(3 df, p-value = 0.723184) 0 111 5 116 1 193 9 202 Cross-tabulation of Balkon(rows) against Garaz(columns) Pearson chi-square test = 0.00368616(1 df, p-value = 0.951587) 0 210 10 220 1 94 4 98 Cross-tabulation of Terasa(rows) against Garaz(columns) Pearson chi-square test = 0.0346559(1 df, p-value = 0.852319) 0 266 11 277 1 38 3 41 Cross-tabulation of Lodzie(rows) against Garaz(columns) Pearson chi-square test = 0.950012(1 df, p-value = 0.329716) x

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 192 4 196 1 112 10 122 Cross-tabulation of Sklep(rows) against Garaz(columns) Pearson chi-square test = 6.77057(1 df, p-value = 0.00926732) 0.5 1 1 1 44 1 45 2 124 4 128 2.5 71 10 81 3.5 40 8 48 4 10 10 4.5 3 3 5 2 2 Cross-tabulation of Pokoj(rows) against Parking(columns) Pearson chi-square test = 47.8093(9 df, p-value = 2.77242e-007) 1 3 3 2 30 9 39 3 259 17 276 Cross-tabulation of Vlastnictvi(rows) against Parking(columns) Pearson chi-square test = 13.2963(2 df, p-value = 0.00129639) 1 37 14 51 2 255 12 267 Cross-tabulation of Zdivo(rows) against Parking(columns) Pearson chi-square test = 30.0585(1 df, p-value = 4.19205e-008) xi

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 14 8 22 2 6 5 11 3 52 8 60 4 220 5 225 Cross-tabulation of Stav(rows) against Parking(columns) Pearson chi-square test = 56.3935(3 df, p-value = 3.46225e-012) 0 102 14 116 1 190 12 202 Cross-tabulation of Balkon(rows) against Parking(columns) Pearson chi-square test = 3.68613(1 df, p-value = 0.0548668) 0 199 21 220 1 93 5 98 Cross-tabulation of Terasa(rows) against Parking(columns) Pearson chi-square test = 1.78301(1 df, p-value = 0.18178) 0 257 20 277 1 35 6 41 Cross-tabulation of Lodzie(rows) against Parking(columns) Pearson chi-square test = 2.61476(1 df, p-value = 0.105874) 0 185 11 196 1 107 15 122 Cross-tabulation of Sklep(rows) against Parking(columns) Pearson chi-square test = 4.4731(1 df, p-value = 0.0344325) xii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 279 25 304 1 13 1 14 Cross-tabulation of Garaz(rows) against Parking(columns) Pearson chi-square test = 0.020825(1 df, p-value = 0.885257) 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 1 4 5 25 7 5 46 1.5 1 1 2 6 5 4 6 7 59 37 7 1 132 2.5 3 3 3 12 8 1 34 25 13 9 3 1 106 4 1 2 20 1 2 1 27 5 1 1 1 3 Cross-tabulation of Pokoj(rows) against Vybaveni(columns) Pearson chi-square test = 200.16(81 df, p-value = 4.50719e-012) 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 1 x x x x 1 x 1 1 x x 3 2 4 3 6 2 2 6 10 6 x x 39 3 19 6 13 5 34 130 54 9 5 1 276 Cross-tabulation of Vlastnictvi(rows) against Vybaveni(columns) Pearson chi-square test = 41.6483(18 df, p-value = 0.00123803) 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 1 1 3 7 7 3 15 8 7 x x 51 2 11 17 6 6 69 91 52 9 5 1 267 Cross-tabulation of Zdivo(rows) against Vybaveni(columns) Pearson chi-square test = 46.5582(9 df, p-value = 4.74283e-007) xiii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 1 3 10 9 22 2 3 4 1 3 11 3 7 1 5 15 4 10 11 7 60 4 16 136 13 48 6 5 1 225 Cross-tabulation of Stav(rows) against Vybaveni(columns) Pearson chi-square test = 264.722(27 df, p-value = 7.04617e-041) 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 0 2 9 11 11 20 25 22 11 4 1 116 1 13 7 1 5 14 118 38 5 1 202 Cross-tabulation of Balkon(rows) against Vybaveni(columns) Pearson chi-square test = 71.7476(9 df, p-value = 6.91043e-012) 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 0 1 27 1 25 2 98 49 13 3 1 220 1 4 1 77 11 3 2 98 Cross-tabulation of Terasa(rows) against Vybaveni(columns) Pearson chi-square test = 243.563(9 df, p-value = 2.27709e-047) 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 0 1 41 14 151 54 10 5 1 277 1 1 1 13 11 1 8 6 41 Cross-tabulation of Lodzie(rows) against Vybaveni(columns) Pearson chi-square test = 150.181(9 df, p-value = 8.08991e-028) 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 0 1 32 10 129 16 4 3 1 196 1 3 4 19 3 35 44 12 2 122 Cross-tabulation of Sklep(rows) against Vybaveni(columns) Pearson chi-square test = 121.26(9 df, p-value = 7.37592e-022) xiv

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 0 1 2 50 3 172 56 15 4 1 304 1 1 1 6 4 1 1 14 Cross-tabulation of Garaz(rows) against Vybaveni(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0 0.5 1 1.5 2 2.5 3 4 5 6 TOT. 0 1 2 43 1 9 162 55 13 5 1 292 1 2 1 7 7 6 3 26 Cross-tabulation of Parking(rows) against Vybaveni(columns) Pearson chi-square test = 127.476(9 df, p-value = 3.91494e-023) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 1 5 11 12 5 3 1 1 38 1.5 2 2 2 2 29 41 50 9 2 1 2 1 137 2.5 1 1 3 23 19 28 7 3 3 1 84 3.5 4 3 4 10 11 2 34 4 2 4 3 1 1 4 1 1 17 4.5 1 1 1 3 5 1 1 2 Cross-tabulation of Pokoj(rows) against Patro(columns) Pearson chi-square test = 528.772(99 df, p-value = 6.41099e-060) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 1 1 1 1 3 2 1 7 1 11 3 4 2 5 2 3 39 3 1 70 76 67 31 21 7 2 1 276 Cross-tabulation of Vlastnictvi(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 xv

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 1 1 7 6 10 3 5 7 6 2 3 1 51 2 2 8 62 71 69 31 21 2 1 267 Cross-tabulation of Zdivo(rows) against Patro(columns) Pearson chi-square test = 90.5186(11 df, p-value = 1.31927e-014) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 1 1 3 3 1 5 1 3 2 1 2 22 2 3 3 3 1 1 11 3 6 5 8 17 5 7 4 5 1 1 1 60 4 4 58 63 58 24 17 1 225 Cross-tabulation of Stav(rows) against Patro(columns) Pearson chi-square test = 119.554(33 df, p-value = 9.71605e-012) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 0 5 14 16 41 13 14 4 4 2 2 1 116 1 6 55 61 38 21 12 5 3 1 202 Cross-tabulation of Balkon(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 0 1 51 56 44 23 24 8 7 2 3 1 220 1 1 27 21 35 11 2 1 98 Cross-tabulation of Terasa(rows) against Patro(columns) Pearson chi-square test = 24.4005(11 df, p-value = 0.0111463) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 0 8 67 71 65 31 20 7 5 1 2 277 1 5 6 14 3 6 2 2 1 1 1 41 Cross-tabulation of Lodzie(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 xvi

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 0 3 54 54 48 16 9 5 3 3 1 196 1 2 6 15 23 31 18 17 4 4 2 122 Cross-tabulation of Sklep(rows) against Patro(columns) Pearson chi-square test = 34.6728(11 df, p-value = 0.000280537) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 0 8 70 75 75 30 25 8 7 2 3 1 304 1 1 1 2 4 4 1 1 14 Cross-tabulation of Garaz(rows) against Patro(columns) Pearson chi-square test = 30.232(11 df, p-value = 0.00145693) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 0 8 70 73 73 31 22 6 4 2 2 1 292 1 1 1 4 6 3 4 3 3 1 26 Cross-tabulation of Parking(rows) against Patro(columns) Pearson chi-square test = 40.6377(11 df, p-value = 2.7816e-005) 0 0.5 1 2 3 4 5 6 7 8 9 12 TOT. 0 1 1 2 0.5 1 2 7 6 9 1 1 27 1 2 1 4 4 3 7 2 2 1 26 2 4 49 53 2.5 1 11 53 44 8 1 3 2 2 1 126 3 2 11 18 12 14 2 2 1 62 4 1 4 2 7 1 1 16 5 1 3 1 5 6 1 1 Cross-tabulation of Vybaveni(rows) against Patro(columns) Pearson chi-square test = 413.933(99 df, p-value = 4.11199e-040) xvii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 2 3 4 TOT. 0.5 9 9 1 11 18 29 2 2 14 42 88 146 2.5 1 2 3 3 10 30 61 101 3.5 1 1 4 3 15 18 4.5 9 9 5 2 2 Cross-tabulation of Pokoj(rows) against MHD(columns) Pearson chi-square test = 96.455(27 df, p-value = 9.76001e-010) 1 2 3 4 TOT. 1 3 3 2 1 16 22 39 3 3 26 79 168 276 Cross-tabulation of Vlastnictvi(rows) against MHD(columns) Pearson chi-square test = 6.15276(6 df, p-value = 0.406297) 1 2 3 4 TOT. 1 20 31 51 2 3 27 75 162 267 Cross-tabulation of Zdivo(rows) against MHD(columns) Pearson chi-square test = 7.50469(3 df, p-value = 0.0574379) 1 2 3 4 TOT. 1 3 7 12 22 2 2 9 11 3 2 2 25 31 60 4 1 22 61 141 225 Cross-tabulation of Stav(rows) against MHD(columns) Pearson chi-square test = 14.2713(9 df, p-value = 0.112995) xviii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 2 3 4 TOT. 0 3 9 38 66 116 1 18 57 127 202 Cross-tabulation of Balkon(rows) against MHD(columns) Pearson chi-square test = 6.28133(3 df, p-value = 0.0986967) 1 2 3 4 TOT. 0 2 21 83 114 220 1 1 6 12 79 98 Cross-tabulation of Terasa(rows) against MHD(columns) Pearson chi-square test = 24.9432(3 df, p-value = 1.58683e-005) 1 2 3 4 TOT. 0 2 26 80 169 277 1 1 1 15 24 41 Cross-tabulation of Lodzie(rows) against MHD(columns) Pearson chi-square test = 3.89184(3 df, p-value = 0.273382) 1 2 3 4 TOT. 0 1 22 70 103 196 1 2 5 25 90 122 Cross-tabulation of Sklep(rows) against MHD(columns) Pearson chi-square test = 16.9249(3 df, p-value = 0.000732343) 1 2 3 4 TOT. 0 2 25 93 184 304 1 1 2 2 9 14 Cross-tabulation of Garaz(rows) against MHD(columns) Pearson chi-square test = 7.76782(3 df, p-value = 0.051062) xix

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ 1 2 3 4 TOT. 0 1 23 89 179 292 1 2 4 6 14 26 Cross-tabulation of Parking(rows) against MHD(columns) Pearson chi-square test = 15.9123(3 df, p-value = 0.00118193) 1 2 3 4 TOT. 0 17 2 19 0.5 4 4 1 2 10 20 32 2 4 64 108 176 3 6 5 54 65 4 7 9 16 5 1 1 3 5 6 1 1 Cross-tabulation of Vybaveni(rows) against MHD(columns) Pearson chi-square test = 236.365(27 df, p-value = 2.48774e-035) 1 2 3 4 TOT. 0 1 6 7 1 5 27 41 73 2 1 7 27 42 77 3 8 21 50 79 4 1 5 5 23 34 5 1 1 6 18 26 6 1 4 4 9 7 3 4 7 8 1 1 2 9 3 3 12 1 1 Cross-tabulation of Patro(rows) against MHD(columns) Pearson chi-square test = 21.9519(33 df, p-value = 0.928664) Tabulka A.1: Kontingenční tabulky pro prodej bytů xx

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ m2 Pokoj Vlastnictvi Zdivo Stav 1.0000 0.8205 0.1073 0.2685 0.2361 m2 1.0000 0.1002 0.0186 0.0598 Pokoj 1.0000 0.4525 0.4907 Vlastnictvi 1.0000 0.5832 Zdivo 1.0000 Stav Balkon Terasa Lodzie Sklep Garaz 0.1346 0.5816 0.1104 0.1421 0.1782 m2 0.0042 0.2977 0.1123 0.1069 0.2538 Pokoj 0.2718 0.2157 0.3306 0.2198 0.0811 Vlastnictvi 0.2563 0.2917 0.4711 0.2191 0.0520 Zdivo 0.2671 0.3310 0.3169 0.2585 0.0087 Stav 1.0000 0.0036 0.4687 0.1276 0.0034 Balkon 1.0000 0.2365 0.3445 0.0104 Terasa 1.0000 0.2561 0.0547 Lodzie 1.0000 0.1459 Sklep 1.0000 Garaz Parking Vybaveni Patro MHD 0.0850 0.0045 0.0354 0.1622 m2 0.0650 0.0475 0.1416 0.0539 Pokoj 0.1630 0.0021 0.2663 0.0530 Vlastnictvi 0.3074 0.0105 0.4013 0.0663 Zdivo 0.4008 0.2449 0.3025 0.0238 Stav 0.1077 0.0664 0.2437 0.0697 Balkon 0.0749 0.0391 0.1487 0.2140 Terasa 0.0907 0.0054 0.2216 0.0050 Lodzie 0.1186 0.2214 0.1476 0.1838 Sklep 0.0081 0.0728 0.0435 0.0454 Garaz 1.0000 0.0301 0.2241 0.1178 Parking 1.0000 0.0623 0.2941 Vybaveni 1.0000 0.0365 Patro 1.0000 MHD Tabulka A.2: Korelační matice pro prodej bytů xxi

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. Error t-ratio p-value const 2.58197e+006 391582. 6.5937 0.0000 m2 27676.4 2806.81 9.8605 0.0000 Pokoj 10187.5 78301.7 0.1301 0.8966 Vlastnictvi 679306. 101901. 6.6663 0.0000 Zdivo 81851.9 124621. 0.6568 0.5118 Stav 143641. 55338.3 2.5957 0.0099 Balkon 16853.9 80087.0 0.2104 0.8335 Terasa 150141. 99356.4 1.5111 0.1318 Lodzie 89600.9 121139. 0.7397 0.4601 Sklep 594300. 79248.4 7.4992 0.0000 Garaz 162045. 161752. 1.0018 0.3172 Parking 27642.6 129075. 0.2142 0.8306 Vybaveni 105155. 35612.3 2.9528 0.0034 Patro 103868. 19989.4 5.1961 0.0000 MHD 105554. 53178.7 1.9849 0.0481 Mean dependent var 2349384 S.D. dependent var 1034754 Sum squared resid 9.32e+13 S.E. of regression 554574.3 R 2 0.725445 Adjusted R 2 0.712760 F(14, 303) 57.18612 P-value(F) 2.55e 76 Log-likelihood 4649.394 Akaikecriterion 9328.788 Schwarzcriterion 9385.218 Hannan Quinn 9351.326 Tabulka A.3: Odhad metodou OLS pro prodej bytů xxii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. error t-ratio p-value const 704164. 1.25857e+06 0.5595 0.5762 m2 5733.88 11445.5 0.5010 0.6168 Pokoj 128373. 83582.7 1.536 0.1256 Vlastnictvi 23706.6 270495. 0.08764 0.9302 Zdivo 74408.7 122668. 0.6066 0.5446 Stav 61539.9 66749.1 0.9220 0.3573 Balkon 52896.6 78737.3 0.6718 0.5022 Terasa 92178.1 117721. 0.7830 0.4342 Lodzie 11847.9 127622. 0.09284 0.9261 Sklep 27539.0 229197. 0.1202 0.9044 Garaz 114805. 169283. 0.6782 0.4982 Parking 18531.3 129210. 0.1434 0.8861 Vybaveni 6568.10 54784.9 0.1199 0.9047 Patro 7964.95 41552.5 0.1917 0.8481 Test statistic: F = 8.538707, with p-value=p(f(2,301) >8.53871)=0.000247 Tabulka A.4: RESET test pro prodej bytů xxiii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ m2 6.936 Pokoj 5.462 Vlastnictvi 1.507 Zdivo 2.162 Stav 2.321 Balkon 1.537 Terasa 2.176 Lodzie 1.704 Sklep 1.536 Garaz 1.139 Parking 1.293 Vybaveni 1.357 Patro 1.300 MHD 1.393 V IF(j)=1/(1 R(j) 2 ),where R(j)isthemultiplecorrelationcoefficientbetween variable j and the other independent variables Propertiesofmatrix X X: 1-norm = 2511758.7 Determinant = 1.6501702e+031 Reciprocal condition number = 4.7183614e-007 Tabulka A.5: Test multikolinearity pro prodej bytů xxiv

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. error t-ratio p-value const 1.70698e+012 5.71251e+012 0.2988 0.7654 m2 4.67109e+010 5.83612e+010 0.8004 0.4244 Pokoj 5.83566e+011 1.36409e+012 0.4278 0.6692 Vlastnictvi 5.66790e+012 2.54371e+012 2.228 0.0269 Zdivo 3.76780e+012 1.95462e+012 1.928 0.0553 Stav 8.66427e+011 6.50910e+011 1.331 0.1846 Balkon 1.41719e+012 1.67227e+012 0.8475 0.3977 Terasa 4.11607e+012 3.02184e+012 1.362 0.1746 Lodzie 6.25412e+011 1.58649e+012 0.3942 0.6938 Sklep 3.47317e+010 1.41470e+012 0.02455 0.9804 Garaz 1.85933e+013 8.75900e+012 2.123 0.0350 Parking 3.47380e+012 1.76540e+012 1.968 0.0504 Vybaveni 1.08899e+012 5.37410e+011 2.026 0.0440 Patro 5.73401e+011 2.99345e+011 1.916 0.0568 MHD 2.35827e+012 1.43527e+012 1.643 0.1019 sq.m2 2.29497e+08 1.60914e+08 1.426 0.1553 X2.X3 1.09111e+010 8.85978e+09 1.232 0.2195 X2.X4 2.75367e+010 2.12389e+010 1.297 0.1962 X2.X5 3.78999e+010 2.80914e+010 1.349 0.1788 X2.X6 4.81556e+09 7.93829e+09 0.6066 0.5448 X2.X7 3.81908e+09 9.84282e+09 0.3880 0.6984 X2.X8 1.35448e+010 1.22090e+010 1.109 0.2685 X2.X9 2.18470e+010 2.01439e+010 1.085 0.2794 X2.X10 2.01856e+09 8.74833e+09 0.2307 0.8177 X2.X11 1.57194e+09 1.79435e+010 0.08761 0.9303 X2.X12 3.26463e+010 1.99987e+010 1.632 0.1041 X2.X13 6.12419e+09 4.39850e+09 1.392 0.1653 X2.X14 7.64227e+09 2.23211e+09 3.424 0.0007 X2.X15 2.88287e+09 7.40656e+09 0.3892 0.6975 sq.pokoj 6.59704e+010 1.36592e+011 0.4830 0.6296 X3.X4 8.30000e+011 4.81118e+011 1.725 0.0860 X3.X5 3.22696e+011 5.93652e+011 0.5436 0.5873 X3.X6 2.23868e+011 2.02807e+011 1.104 0.2709 X3.X7 5.04008e+011 2.80900e+011 1.794 0.0742 X3.X8 6.28076e+011 3.54563e+011 1.771 0.0780 X3.X9 5.89336e+011 5.13402e+011 1.148 0.2523 xxv

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. error t-ratio p-value X3.X10 1.80766e+011 2.26438e+011 0.7983 0.4256 X3.X11 1.32866e+012 8.16217e+011 1.628 0.1051 X3.X12 5.73886e+010 5.02825e+011 0.1141 0.9092 X3.X13 8.86973e+010 1.24100e+011 0.7147 0.4756 X3.X14 1.07501e+011 5.90116e+010 1.822 0.0699 X3.X15 3.68539e+010 2.10490e+011 0.1751 0.8612 sq.vlastnictvi 8.95716e+011 3.37793e+011 2.652 0.0086 X4.X5 1.00549e+010 3.02394e+011 0.03325 0.9735 X4.X6 1.97350e+011 2.17152e+011 0.9088 0.3645 X4.X7 1.07363e+012 5.43780e+011 1.974 0.0497 X4.X8 1.50794e+012 7.30615e+011 2.064 0.0403 X4.X9 4.83259e+011 3.93531e+011 1.228 0.2208 X4.X10 3.42634e+011 3.69189e+011 0.9281 0.3545 X4.X12 3.86425e+011 5.51731e+011 0.7004 0.4845 X4.X13 1.08919e+011 1.13636e+011 0.9585 0.3389 X4.X14 1.09533e+011 8.01848e+010 1.366 0.1734 X4.X15 3.43708e+011 2.47726e+011 1.387 0.1668 X5.X6 2.82734e+010 1.99232e+011 0.1419 0.8873 X5.X7 1.01243e+012 4.50055e+011 2.250 0.0255 X5.X9 3.26899e+011 4.94747e+011 0.6607 0.5095 X5.X10 3.95523e+010 4.01044e+011 0.09862 0.9215 X5.X11 4.00545e+012 2.05795e+012 1.946 0.0530 X5.X12 1.17028e+012 7.44520e+011 1.572 0.1175 X5.X13 1.47079e+010 1.31950e+011 0.1115 0.9114 X5.X14 7.05459e+010 9.60574e+010 0.7344 0.4635 X5.X15 5.33781e+011 2.66162e+011 2.005 0.0462 sq.stav 7.01460e+09 1.12149e+011 0.06255 0.9502 X6.X7 2.57908e+011 1.81293e+011 1.423 0.1564 X6.X8 3.16210e+011 6.01042e+011 0.5261 0.5994 X6.X9 2.77997e+011 1.93568e+011 1.436 0.1525 X6.X10 1.68276e+011 2.70077e+011 0.6231 0.5339 X6.X11 6.38785e+011 6.41022e+011 0.9965 0.3202 X6.X12 1.84840e+011 2.04430e+011 0.9042 0.3670 X6.X13 8.58492e+09 9.69688e+010 0.08853 0.9295 X6.X14 4.10075e+010 4.42951e+010 0.9258 0.3556 X6.X15 2.11410e+010 1.21824e+011 0.1735 0.8624 xxvi

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. error t-ratio p-value X7.X8 3.17941e+011 5.26206e+011 0.6042 0.5464 X7.X9 1.44073e+010 7.19612e+011 0.02002 0.9840 X7.X10 7.15330e+011 3.05658e+011 2.340 0.0202 X7.X11 1.94738e+012 3.14039e+012 0.6201 0.5359 X7.X12 9.80360e+011 7.53611e+011 1.301 0.1947 X7.X13 1.06346e+010 1.12421e+011 0.09460 0.9247 X7.X14 1.42132e+011 6.08071e+010 2.337 0.0204 X7.X15 1.84315e+011 1.90370e+011 0.9682 0.3341 X8.X9 1.40032e+012 1.13825e+012 1.230 0.2200 X8.X10 2.41581e+010 3.98684e+011 0.06059 0.9517 X8.X11 5.77419e+012 3.98763e+012 1.448 0.1491 X8.X12 5.50716e+011 8.67588e+011 0.6348 0.5263 X8.X13 6.28796e+011 1.88440e+011 3.337 0.0010 X8.X14 6.26828e+010 8.81352e+010 0.7112 0.4778 X8.X15 2.67442e+011 3.51070e+011 0.7618 0.4471 X9.X10 2.06642e+011 4.78511e+011 0.4318 0.6663 X9.X11 3.13628e+012 3.12103e+012 1.005 0.3161 X9.X12 3.94804e+011 5.14640e+011 0.7671 0.4439 X9.X13 1.06016e+011 1.37342e+011 0.7719 0.4410 X9.X14 3.17829e+010 9.56461e+010 0.3323 0.7400 X9.X15 2.54997e+010 2.65826e+011 0.09593 0.9237 X10.X11 1.90392e+012 7.44000e+011 2.559 0.0112 X10.X12 1.60014e+010 4.30790e+011 0.03714 0.9704 X10.X13 9.00066e+09 1.01353e+011 0.08880 0.9293 X10.X14 7.26663e+010 5.84204e+010 1.244 0.2150 X10.X15 3.76859e+011 2.09428e+011 1.799 0.0734 X11.X12 6.54937e+012 4.07408e+012 1.608 0.1095 X11.X13 4.96805e+011 4.19497e+011 1.184 0.2377 X11.X14 1.06137e+012 4.65742e+011 2.279 0.0237 X11.X15 1.02497e+012 5.86691e+011 1.747 0.0821 X12.X13 2.09447e+011 1.80640e+011 1.159 0.2476 X12.X14 1.27841e+011 1.03487e+011 1.235 0.2181 X12.X15 5.01548e+011 2.61778e+011 1.916 0.0568 sq.vybaveni 7.80429e+010 3.09933e+010 2.518 0.0126 X13.X14 4.00002e+010 2.62949e+010 1.521 0.1297 X13.X15 2.09200e+011 7.41066e+010 2.823 0.0052 xxvii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. error t-ratio p-value sq.patro 1.83565e+010 1.01889e+010 1.802 0.0731 X14.X15 4.93777e+010 4.69379e+010 1.052 0.2940 sq.mhd 3.94483e+011 1.20121e+011 3.284 0.0012 Unadjusted R-squared = 0.740195 Teststatistic: TR 2 =235.382158, with p-value = P(Chi-square(110) > 235.382158) = 0.000000 Tabulka A.6: Whiteův test pro prodej bytů Coefficient Std. error t-ratio p-value const 1.62819 1.30325 1.249 0.2125 m2 0.0304529 0.00934157 3.260 0.0012 Pokoj 0.196355 0.260602 0.7535 0.4518 Vlastnictvi 0.430376 0.339146 1.269 0.2054 Zdivo 0.345316 0.414761 0.8326 0.4057 Stav 0.0670375 0.184176 0.3640 0.7161 Balkon 0.692068 0.266544 2.596 0.0099 Terasa 0.666145 0.330676 2.014 0.0448 Lodzie 0.523126 0.403173 1.298 0.1954 Sklep 0.829959 0.263753 3.147 0.0018 Garaz 0.549153 0.538341 1.020 0.3085 Parking 0.220700 0.429586 0.5137 0.6078 Vybaveni 0.127204 0.118524 1.073 0.2840 Patro 0.206045 0.0665284 3.097 0.0021 MHD 0.358185 0.176988 2.024 0.0439 Explained sum of squares = 220.476 Test statistic: LM = 110.237879, with p-value = P(Chi-square(14) > 110.237879) = 0.000000 Tabulka A.7: Breusch-Paganův test pro prodej bytů xxviii

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. Error t-ratio p-value const 2.55083e+006 158702. 16.0731 0.0000 m2 26092.5 1400.69 18.6284 0.0000 Pokoj 17573.1 33553.9 0.5237 0.6009 Vlastnictvi 680975. 44992.3 15.1354 0.0000 Zdivo 103178. 50759.7 2.0327 0.0430 Stav 145789. 30528.5 4.7755 0.0000 Balkon 33781.3 35782.6 0.9441 0.3459 Terasa 118324. 34684.3 3.4114 0.0007 Lodzie 62131.1 53695.6 1.1571 0.2481 Sklep 619951. 32750.3 18.9296 0.0000 Garaz 228096. 99244.0 2.2983 0.0222 Parking 28349.6 39576.0 0.7163 0.4743 Vybaveni 94585.9 16465.4 5.7445 0.0000 Patro 99387.6 9807.78 10.1336 0.0000 MHD 119063. 25726.5 4.6280 0.0000 Statistics based on the weighted data: Sum squared resid 1.21e+08 S.E. of regression 633.0303 R 2 0.955927 Adjusted R 2 0.953890 F(14, 303) 469.4210 P-value(F) 5.8e 196 Log-likelihood 2494.805 Akaike criterion 5019.609 Schwarz criterion 5076.040 Hannan Quinn 5042.148 Statistics based on the original data: Mean dependent var 2349384 S.D. dependent var 1034754 Sum squared resid 9.38e+13 S.E. of regression 556451.9 Tabulka A.8: Odhad metodou WLS pro prodej bytů xxix

PŘÍLOHA A. METODA NEJMENŠÍCH ČTVERCŮ PRODEJ BYTŮ Coefficient Std. Error t-ratio p-value const 2.60023e+006 182937. 14.2138 0.0000 m2 26824.9 548.349 48.9194 0.0000 Vlastnictvi 689017. 52429.3 13.1418 0.0000 Zdivo 115218. 49549.9 2.3253 0.0207 Stav 146996. 31146.3 4.7196 0.0000 Terasa 124365. 31329.7 3.9696 0.0001 Sklep 616922. 36941.8 16.6998 0.0000 Garaz 222331. 96559.1 2.3025 0.0220 Vybaveni 89643.6 17964.0 4.9902 0.0000 Patro 94493.1 9391.73 10.0613 0.0000 MHD 113704. 24727.3 4.5983 0.0000 Statistics based on the weighted data: Sum squared resid 1.21e+08 S.E. of regression 628.2071 R 2 0.917076 Adjusted R 2 0.914375 F(10, 307) 339.5198 P-value(F) 1.8e 159 Log-likelihood 2494.458 Akaike criterion 5010.915 Schwarz criterion 5052.298 Hannan Quinn 5027.444 Statistics based on the original data: Mean dependent var 2349384 S.D. dependent var 1034754 Sum squared resid 9.41e+13 S.E. of regression 553552.4 Tabulka A.9: Odhad metodou WLS pro prodej bytů, zúžený model xxx

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ Příloha B Metoda nejmenších čtverců Pronájem bytů xxxi

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 2 3 TOT. 0.5 2 2 1 3 29 32 1.5 1 1 2 5 31 36 3 2 17 19 3.5 3 3 4 1 1 4.5 3 3 Cross-tabulation of Pokoj(rows) against Vlastnictvi(columns) Pearson chi-square test = 25.6327(8 df, p-value = 0.00121356) 1 2 TOT. 0.5 1 1 1 5 28 33 1.5 3 3 2 3 31 34 3 1 18 19 3.5 3 3 4 1 1 4.5 3 3 Cross-tabulation of Pokoj(rows) against Zdivo(columns) Pearson chi-square test = 32.8286(8 df, p-value = 6.61243e-005) 1 2 TOT. 2 8 6 14 3 6 77 83 Cross-tabulation of Vlastnictvi(rows) against Zdivo(columns) Pearson chi-square test = 24.1666(1 df, p-value = 8.83515e-007) xxxii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 1 2 3 4 TOT. 0.5 1 1 1 1 2 19 11 33 1.5 1 1 2 6 2 14 14 36 3 2 2 3.5 1 2 9 8 20 4 1 1 2 4.5 1 1 2 Cross-tabulation of Pokoj(rows) against Stav(columns) Pearson chi-square test = 41.3292(24 df, p-value = 0.0153235) 1 2 3 4 TOT. 2 5 1 6 2 14 3 8 6 37 32 83 Cross-tabulation of Vlastnictvi(rows) against Stav(columns) Pearson chi-square test = 8.09864(3 df, p-value = 0.0440165) 1 2 3 4 TOT. 1 5 1 7 1 14 2 8 6 36 33 83 Cross-tabulation of Zdivo(rows) against Stav(columns) Pearson chi-square test = 9.83218(3 df, p-value = 0.0200479) 0.5 2 2 1 23 5 28 2 29 6 35 3 16 11 27 3.5 1 1 4.5 2 2 4 Cross-tabulation of Pokoj(rows) against Balkon(columns) Pearson chi-square test = 7.86643(8 df, p-value = 0.446626) xxxiii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 2 10 4 14 3 63 20 83 Cross-tabulation of Vlastnictvi(rows) against Balkon(columns) Pearson chi-square test = 0.128836(1 df, p-value = 0.719642) 1 10 4 14 2 63 20 83 Cross-tabulation of Zdivo(rows) against Balkon(columns) Pearson chi-square test = 0.128836(1 df, p-value = 0.719642) 1 10 3 13 2 4 3 7 3 32 11 43 4 27 7 34 Cross-tabulation of Stav(rows) against Balkon(columns) Pearson chi-square test = 1.58432(3 df, p-value = 0.662951) 0.5 1 1 1 2 2 1.5 44 2 46 2 16 5 21 2.5 1 1 3 10 8 18 3.5 4 4 4 1 1 4.5 3 3 Cross-tabulation of Pokoj(rows) against Terasa(columns) Pearson chi-square test = 39.0234(8 df, p-value = 4.86652e-006) xxxiv

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 2 12 2 14 3 66 17 83 Cross-tabulation of Vlastnictvi(rows) against Terasa(columns) Pearson chi-square test = 0.292(1 df, p-value = 0.588942) 1 14 14 2 64 19 83 Cross-tabulation of Zdivo(rows) against Terasa(columns) Pearson chi-square test = 3.98548(1 df, p-value = 0.045894) 1 10 3 13 2 7 7 3 36 7 43 4 25 9 34 Cross-tabulation of Stav(rows) against Terasa(columns) Pearson chi-square test = 3.1271(3 df, p-value = 0.372441) 0 55 18 73 1 23 1 24 Cross-tabulation of Balkon(rows) against Terasa(columns) Pearson chi-square test = 4.8148(1 df, p-value = 0.0282163) xxxv

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0.5 1 1 1 29 2 31 2 32 7 39 3 20 1 21 3.5 1 1 4.5 4 4 Cross-tabulation of Pokoj(rows) against Lodzie(columns) Pearson chi-square test = 4.34905(8 df, p-value = 0.824338) 2 11 3 14 3 76 7 83 Cross-tabulation of Vlastnictvi(rows) against Lodzie(columns) Pearson chi-square test = 2.18776(1 df, p-value = 0.139111) 1 10 4 14 2 77 6 83 Cross-tabulation of Zdivo(rows) against Lodzie(columns) Pearson chi-square test = 5.90133(1 df, p-value = 0.0151295) 1 12 1 13 2 7 7 3 40 3 43 4 28 6 34 Cross-tabulation of Stav(rows) against Lodzie(columns) Pearson chi-square test = 3.3972(3 df, p-value = 0.334341) xxxvi

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0 63 10 73 1 24 24 Cross-tabulation of Balkon(rows) against Lodzie(columns) Pearson chi-square test = 3.66556(1 df, p-value = 0.0555478) 0 69 9 78 1 18 1 19 Cross-tabulation of Terasa(rows) against Lodzie(columns) Pearson chi-square test = 0.650684(1 df, p-value = 0.419868) 0.5 1 1 1 22 5 27 2 25 18 43 2.5 1 1 3 15 4 19 4 2 1 3 4.5 1 2 3 Cross-tabulation of Pokoj(rows) against Sklep(columns) Pearson chi-square test = 7.91681(8 df, p-value = 0.441638) 2 7 7 14 3 60 23 83 Cross-tabulation of Vlastnictvi(rows) against Sklep(columns) Pearson chi-square test = 2.78592(1 df, p-value = 0.0950959) xxxvii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 1 4 10 14 2 63 20 83 Cross-tabulation of Zdivo(rows) against Sklep(columns) Pearson chi-square test = 12.563(1 df, p-value = 0.00039345) 1 10 3 13 2 2 5 7 3 33 10 43 4 22 12 34 Cross-tabulation of Stav(rows) against Sklep(columns) Pearson chi-square test = 7.2382(3 df, p-value = 0.064681) 0 55 18 73 1 12 12 24 Cross-tabulation of Balkon(rows) against Sklep(columns) Pearson chi-square test = 5.4301(1 df, p-value = 0.0197926) 0 56 22 78 1 11 8 19 Cross-tabulation of Terasa(rows) against Sklep(columns) Pearson chi-square test = 1.38185(1 df, p-value = 0.239786) 0 61 26 87 1 6 4 10 Cross-tabulation of Lodzie(rows) against Sklep(columns) Pearson chi-square test = 0.429558(1 df, p-value = 0.512206) xxxviii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0.5 1 1 1 1 1 1.5 48 1 49 2 14 6 20 3 11 11 3.5 9 2 11 4.5 2 2 4 Cross-tabulation of Pokoj(rows) against Garaz(columns) Pearson chi-square test = 19.2618(8 df, p-value = 0.0135205) 2 14 14 3 72 11 83 Cross-tabulation of Vlastnictvi(rows) against Garaz(columns) Pearson chi-square test = 2.09274(1 df, p-value = 0.148) 1 14 14 2 72 11 83 Cross-tabulation of Zdivo(rows) against Garaz(columns) Pearson chi-square test = 2.09274(1 df, p-value = 0.148) 1 12 1 13 2 7 7 3 40 3 43 4 27 7 34 Cross-tabulation of Stav(rows) against Garaz(columns) Pearson chi-square test = 4.77392(3 df, p-value = 0.189121) xxxix

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0 66 7 73 1 20 4 24 Cross-tabulation of Balkon(rows) against Garaz(columns) Pearson chi-square test = 0.899891(1 df, p-value = 0.342811) 0 72 6 78 1 14 5 19 Cross-tabulation of Terasa(rows) against Garaz(columns) Pearson chi-square test = 5.27048(1 df, p-value = 0.02169) 0 77 10 87 1 9 1 10 Cross-tabulation of Lodzie(rows) against Garaz(columns) Pearson chi-square test = 0.0199181(1 df, p-value = 0.887766) 0 61 6 67 1 25 5 30 Cross-tabulation of Sklep(rows) against Garaz(columns) Pearson chi-square test = 1.2256(1 df, p-value = 0.268265) xl

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0.5 3 3 1 28 1 29 1.5 15 7 22 2 10 7 17 3 17 2 19 3.5 1 2 3 4 1 1 4.5 2 1 3 Cross-tabulation of Pokoj(rows) against Parking(columns) Pearson chi-square test = 17.7035(8 df, p-value = 0.0235624) 2 13 1 14 3 64 19 83 Cross-tabulation of Vlastnictvi(rows) against Parking(columns) Pearson chi-square test = 1.81529(1 df, p-value = 0.177874) 1 11 3 14 2 66 17 83 Cross-tabulation of Zdivo(rows) against Parking(columns) Pearson chi-square test = 0.00655889(1 df, p-value = 0.935452) 1 11 2 13 2 7 7 3 36 7 43 4 23 11 34 Cross-tabulation of Stav(rows) against Parking(columns) Pearson chi-square test = 5.39086(3 df, p-value = 0.145314) xli

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0 60 13 73 1 17 7 24 Cross-tabulation of Balkon(rows) against Parking(columns) Pearson chi-square test = 1.42372(1 df, p-value = 0.232793) 0 63 15 78 1 14 5 19 Cross-tabulation of Terasa(rows) against Parking(columns) Pearson chi-square test = 0.468577(1 df, p-value = 0.493642) 0 69 18 87 1 8 2 10 Cross-tabulation of Lodzie(rows) against Parking(columns) Pearson chi-square test = 0.00260636(1 df, p-value = 0.959284) 0 55 12 67 1 22 8 30 Cross-tabulation of Sklep(rows) against Parking(columns) Pearson chi-square test = 0.970689(1 df, p-value = 0.324508) 0 68 18 86 1 9 2 11 Cross-tabulation of Garaz(rows) against Parking(columns) Pearson chi-square test = 0.0450097(1 df, p-value = 0.831986) xlii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0 1 2 3 4 5 6 TOT. 1 2 5 6 12 5 2 32 2 4 2 6 11 7 8 38 2.5 1 1 2 4 1 9 3 1 2 3 3.5 3 5 2 1 11 4.5 2 1 1 4 Cross-tabulation of Pokoj(rows) against Vybaveni(columns) Pearson chi-square test = 43.9855(48 df, p-value = 0.638013) 0 1 2 3 4 5 6 TOT. 2 3 3 3 2 3 14 3 3 8 15 26 16 12 3 83 Cross-tabulation of Vlastnictvi(rows) against Vybaveni(columns) Pearson chi-square test = 9.00372(6 df, p-value = 0.173369) 0 1 2 3 4 5 6 TOT. 1 4 2 5 3 14 2 2 8 16 24 18 12 3 83 Cross-tabulation of Zdivo(rows) against Vybaveni(columns) Pearson chi-square test = 18.8693(6 df, p-value = 0.0043901) 0 1 2 3 4 5 6 TOT. 1 2 5 5 1 13 2 2 4 1 7 3 3 8 8 15 7 2 43 4 1 3 5 10 12 3 34 Cross-tabulation of Stav(rows) against Vybaveni(columns) Pearson chi-square test = 46.6864(18 df, p-value = 0.000235401) xliii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0 1 2 3 4 5 6 TOT. 0 3 6 16 21 13 11 3 73 1 3 2 2 8 5 4 24 Cross-tabulation of Balkon(rows) against Vybaveni(columns) Pearson chi-square test = 5.08328(6 df, p-value = 0.533178) 0 1 2 3 4 5 6 TOT. 0 6 7 15 25 12 12 1 78 1 1 3 4 6 3 2 19 Cross-tabulation of Terasa(rows) against Vybaveni(columns) Pearson chi-square test = 8.8148(6 df, p-value = 0.184265) 0 1 2 3 4 5 6 TOT. 0 5 8 17 27 15 12 3 87 1 1 1 2 3 3 10 Cross-tabulation of Lodzie(rows) against Vybaveni(columns) Pearson chi-square test = 4.64205(6 df, p-value = 0.590471) 0 1 2 3 4 5 6 TOT. 0 2 7 14 20 16 7 1 67 1 4 1 4 9 2 8 2 30 Cross-tabulation of Sklep(rows) against Vybaveni(columns) Pearson chi-square test = 14.1253(6 df, p-value = 0.0282669) 0 1 2 3 4 5 6 TOT. 0 6 8 18 25 14 13 2 86 1 4 4 2 1 11 Cross-tabulation of Garaz(rows) against Vybaveni(columns) Pearson chi-square test = 7.88914(6 df, p-value = 0.246338) xliv

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0 1 2 3 4 5 6 TOT. 0 5 7 17 22 15 9 2 77 1 1 1 1 7 3 6 1 20 Cross-tabulation of Parking(rows) against Vybaveni(columns) Pearson chi-square test = 7.00481(6 df, p-value = 0.320402) 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 1 1 11 11 1 2 1 27 1.5 1 1 2 1 8 1 4 1 1 1 17 2.5 2 2 8 4 5 6 2 2 1 32 3 3 3 3.5 4 4 2 1 2 13 4.5 2 1 1 4 Cross-tabulation of Pokoj(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 2 1 1 3 1 2 2 2 2 14 3 6 6 19 23 11 5 10 2 1 83 Cross-tabulation of Vlastnictvi(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 1 1 4 1 1 2 1 1 2 1 14 2 5 8 19 22 11 6 10 1 1 83 Cross-tabulation of Zdivo(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 xlv

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 1 2 3 3 1 2 1 1 13 2 5 2 7 3 1 5 8 9 10 3 4 1 2 43 4 4 5 14 2 3 4 1 1 34 Cross-tabulation of Stav(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 0 8 5 14 20 8 6 8 1 2 1 73 1 6 6 4 1 4 2 1 24 Cross-tabulation of Balkon(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 0 10 18 19 6 7 11 2 2 2 1 78 1 1 1 3 7 6 1 19 Cross-tabulation of Terasa(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 0 4 9 19 23 12 5 9 2 2 1 1 87 1 1 3 2 3 1 10 Cross-tabulation of Lodzie(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 0 7 6 14 16 7 6 7 1 1 1 1 67 1 6 10 5 1 5 1 1 1 30 Cross-tabulation of Sklep(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 xlvi

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 0 6 7 18 21 10 5 12 2 2 2 1 86 1 2 5 2 2 11 Cross-tabulation of Garaz(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 0 4 8 16 20 9 6 10 1 1 2 77 1 1 4 6 3 1 2 1 1 1 20 Cross-tabulation of Parking(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 0.5 0 0.5 1 2 3 4 5 6 7 8 12 TOT. 0 1 1 1 1 1 1 6 1 3 3 2 8 2 4 3 5 1 1 3 1 18 3 1 2 11 5 5 1 2 1 1 29 4 2 3 6 2 1 3 1 18 5 1 2 4 1 2 3 1 1 15 6 2 1 3 Cross-tabulation of Vybaveni(rows) against Patro(columns) Pearson chi-square test not computed: some expected frequencies were less than 1e-007 1 2 3 4 TOT. 1 1 6 27 34 2 8 29 37 2.5 2 1 3 3 4 4 8 3.5 11 11 4.5 3 1 4 Cross-tabulation of Pokoj(rows) against MHD(columns) Pearson chi-square test = 80.3816(24 df, p-value = 5.2887e-008) xlvii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 1 2 3 4 TOT. 2 5 9 14 3 1 2 17 63 83 Cross-tabulation of Vlastnictvi(rows) against MHD(columns) Pearson chi-square test = 1.94937(3 df, p-value = 0.582981) 1 2 3 4 TOT. 1 5 9 14 2 1 2 17 63 83 Cross-tabulation of Zdivo(rows) against MHD(columns) Pearson chi-square test = 1.94937(3 df, p-value = 0.582981) 1 2 3 4 TOT. 1 3 10 13 2 5 2 7 3 1 8 34 43 4 2 6 26 34 Cross-tabulation of Stav(rows) against MHD(columns) Pearson chi-square test = 15.1291(9 df, p-value = 0.0874504) 1 2 3 4 TOT. 0 1 2 12 58 73 1 10 14 24 Cross-tabulation of Balkon(rows) against MHD(columns) Pearson chi-square test = 7.14017(3 df, p-value = 0.0675619) 1 2 3 4 TOT. 0 18 60 78 1 1 2 4 12 19 Cross-tabulation of Terasa(rows) against MHD(columns) Pearson chi-square test = 12.7334(3 df, p-value = 0.00525007) xlviii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 1 2 3 4 TOT. 0 1 2 19 65 87 1 3 7 10 Cross-tabulation of Lodzie(rows) against MHD(columns) Pearson chi-square test = 0.635039(3 df, p-value = 0.888366) 1 2 3 4 TOT. 0 1 12 54 67 1 1 1 10 18 30 Cross-tabulation of Sklep(rows) against MHD(columns) Pearson chi-square test = 5.93143(3 df, p-value = 0.114994) 1 2 3 4 TOT. 0 2 21 63 86 1 1 1 9 11 Cross-tabulation of Garaz(rows) against MHD(columns) Pearson chi-square test = 9.18056(3 df, p-value = 0.0269842) 1 2 3 4 TOT. 0 1 16 60 77 1 2 6 12 20 Cross-tabulation of Parking(rows) against MHD(columns) Pearson chi-square test = 9.24191(3 df, p-value = 0.0262416) xlix

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ 1 2 3 4 TOT. 0 3 3 6 1 8 8 2 2 16 18 3 1 1 8 19 29 4 3 15 18 5 1 5 9 15 6 1 2 3 Cross-tabulation of Vybaveni(rows) against MHD(columns) Pearson chi-square test = 14.2384(18 df, p-value = 0.713422) 1 2 3 4 TOT. 0 6 6 0.5 7 7 1 1 5 14 20 2 5 21 26 3 1 1 4 6 12 4 2 5 7 5 3 9 12 6 1 1 2 7 2 2 8 2 2 12 1 1 Cross-tabulation of Patro(rows) against MHD(columns) Pearson chi-square test = 26.2585(33 df, p-value = 0.791183) Tabulka B.1: Kontingenční tabulky pro pronájem bytů l

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ m2 Pokoj Vlastnictvi Zdivo Stav 1.0000 0.7977 0.1061 0.2429 0.0260 m2 1.0000 0.0009 0.0901 0.1784 Pokoj 1.0000 0.4991 0.2740 Vlastnictvi 1.0000 0.3039 Zdivo 1.0000 Stav Balkon Terasa Lodzie Sklep Garaz 0.0554 0.5136 0.1169 0.0043 0.3355 m2 0.0679 0.3129 0.1431 0.0112 0.1686 Pokoj 0.0364 0.0549 0.1502 0.1695 0.1469 Vlastnictvi 0.0364 0.2027 0.2467 0.3599 0.1469 Zdivo 0.0548 0.0744 0.1349 0.0157 0.1623 Stav 1.0000 0.2228 0.1944 0.2366 0.0963 Balkon 1.0000 0.0819 0.1194 0.2331 Terasa 1.0000 0.0665 0.0143 Lodzie 1.0000 0.1124 Sklep 1.0000 Garaz Parking Vybaveni Patro MHD 0.1015 0.2414 0.0205 0.3648 m2 0.1206 0.1881 0.0031 0.1816 Pokoj 0.1368 0.0944 0.3011 0.0427 Vlastnictvi 0.0082 0.1744 0.4248 0.0427 Zdivo 0.1768 0.4088 0.0867 0.0245 Stav 0.1212 0.0527 0.1247 0.1207 Balkon 0.0695 0.2129 0.1152 0.2471 Terasa 0.0052 0.1036 0.1942 0.0006 Lodzie 0.1000 0.0677 0.1429 0.2406 Sklep 0.0215 0.2312 0.0358 0.0414 Garaz 1.0000 0.1905 0.1486 0.1832 Parking 1.0000 0.0761 0.0943 Vybaveni 1.0000 0.1058 Patro 1.0000 MHD Tabulka B.2: Korelační matice pro pronájem bytů li

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ Coefficient Std. Error t-ratio p-value const 6906.74 4268.91 1.6179 0.1095 m2 47.0334 24.0632 1.9546 0.0540 Pokoj 1622.05 637.328 2.5451 0.0128 Vlastnictvi 1637.29 1095.72 1.4943 0.1389 Zdivo 350.927 1313.50 0.2672 0.7900 Stav 931.826 413.871 2.2515 0.0270 Balkon 1982.46 869.563 2.2798 0.0252 Terasa 1758.17 1022.82 1.7189 0.0894 Lodzie 2018.95 1158.23 1.7431 0.0851 Sklep 881.699 823.264 1.0710 0.2873 Garaz 2490.00 1153.15 2.1593 0.0337 Parking 409.902 862.399 0.4753 0.6358 Vybaveni 409.509 260.016 1.5749 0.1191 Patro 105.402 175.288 0.6013 0.5493 MHD 2087.84 681.598 3.0632 0.0030 Mean dependent var 10367.11 S.D. dependent var 4714.400 Sum squared resid 8.09e+08 S.E. of regression 3140.314 R 2 0.621003 Adjusted R 2 0.556296 F(14, 82) 9.597179 P-value(F) 3.57e 12 Log-likelihood 910.5410 Akaikecriterion 1851.082 Schwarzcriterion 1889.703 Hannan Quinn 1866.698 Tabulka B.3: Odhad metodou OLS pro pronájem bytů lii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ Coefficient Std. error t-ratio p-value const 29169.6 9057.40 3.221 0.0019 m2 134.954 39.7028 3.399 0.0011 Pokoj 6183.53 1434.94 4.309 4.62e-05 Vlastnictvi 5239.56 1493.11 3.509 0.0007 Zdivo 224.302 878.912 0.2552 0.7992 Stav 2406.49 774.589 3.107 0.0026 Balkon 5137.88 1647.19 3.119 0.0025 Terasa 4498.42 1668.46 2.696 0.0085 Lodzie 6382.53 1679.41 3.800 0.0003 Sklep 2137.47 845.110 2.529 0.0134 Garaz 7261.05 2233.42 3.251 0.0017 Parking 1416.65 569.531 2.487 0.0149 Vybaveni 1211.94 368.781 3.286 0.0015 Patro 470.930 122.431 3.846 0.0002 MHD 6439.10 1779.95 3.618 0.0005 yhat 2 0.000264937 6.30540e-05 4.202 6.84e-05 yhat 3 8.43061e-09 1.44634e-09 5.829 1.13e-07 Test statistic: F = 80.803487, with p-value=p(f(2,80) >80.8035)=6.3e-020 Tabulka B.4: RESET test pro pronájem bytů liii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ m2 5.118 Pokoj 3.890 Vlastnictvi 1.458 Zdivo 2.096 Stav 1.615 Balkon 1.385 Terasa 1.621 Lodzie 1.220 Sklep 1.424 Garaz 1.315 Parking 1.197 Vybaveni 1.431 Patro 1.378 MHD 1.429 V IF(j)=1/(1 R(j) 2 ),where R(j)isthemultiplecorrelationcoefficientbetween variable j and the other independent variables Propertiesofmatrix X X: 1-norm = 580528.56 Determinant = 4.9731518e+024 Reciprocal condition number = 6.088285e-007 Tabulka B.5: Test multikolinearity pro pronájem bytů liv

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ Coefficient Std. error t-ratio p-value const 1.44793e+08 1.20628e+08 1.200 0.2337 m2 708398. 1.00169e+06 0.7072 0.4816 Pokoj 984759. 2.42722e+07 0.04057 0.9677 Vlastnictvi 4.91391e+06 1.17400e+07 0.4186 0.6767 Zdivo 8.78282e+06 1.48697e+07 0.5907 0.5565 Stav 4.08791e+07 1.98242e+07 2.062 0.0426 Balkon 2.46004e+07 9.42102e+06 2.611 0.0109 Terasa 1.23546e+07 1.11620e+07 1.107 0.2719 Lodzie 8.45101e+06 1.26371e+07 0.6687 0.5057 Sklep 1.05127e+07 9.12845e+06 1.152 0.2531 Garaz 2.07304e+07 1.24363e+07 1.667 0.0996 Parking 1.36434e+06 9.45781e+06 0.1443 0.8857 Vybaveni 4.30220e+06 8.83182e+06 0.4871 0.6276 Patro 1.00980e+06 4.41410e+06 0.2288 0.8197 MHD 1.18688e+08 7.52622e+07 1.577 0.1190 sq.m2 8457.33 5561.57 1.521 0.1325 sq.pokoj 1.94844e+06 4.92357e+06 0.3957 0.6934 sq.stav 5.72514e+06 3.96344e+06 1.444 0.1527 sq.vybaveni 485526. 1.55123e+06 0.3130 0.7551 sq.patro 40312.1 499384. 0.08072 0.9359 sq.mhd 1.57673e+07 1.14850e+07 1.373 0.1738 Unadjusted R-squared = 0.382442 Teststatistic: TR 2 =37.096919, with p-value = P(Chi-square(20) > 37.096919) = 0.011393 Tabulka B.6: Whiteův test pro pronájem bytů lv

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ Coefficient Std. error t-ratio p-value const 3.86172 5.37681 0.7182 0.4747 m2 0.0589666 0.0303083 1.946 0.0551 Pokoj 0.959470 0.802733 1.195 0.2354 Vlastnictvi 0.816236 1.38009 0.5914 0.5559 Zdivo 1.53068 1.65439 0.9252 0.3576 Stav 1.70066 0.521282 3.262 0.0016 Balkon 3.24940 1.09524 2.967 0.0039 Terasa 2.18362 1.28827 1.695 0.0939 Lodzie 1.20856 1.45883 0.8284 0.4098 Sklep 1.65671 1.03693 1.598 0.1140 Garaz 2.67097 1.45243 1.839 0.0695 Parking 0.129838 1.08622 0.1195 0.9051 Vybaveni 0.428380 0.327498 1.308 0.1945 Patro 0.156445 0.220780 0.7086 0.4806 MHD 1.75548 0.858492 2.045 0.0441 Explained sum of squares = 637.789 Test statistic: LM = 318.894644, with p-value = P(Chi-square(14) > 318.894644) = 0.000000 Tabulka B.7: Breusch-Paganův test pro pronájem bytů lvi

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ Coefficient Std. Error t-ratio p-value const 4445.76 1978.15 2.2474 0.0273 m2 23.6140 12.4366 1.8987 0.0611 Pokoj 2066.87 334.934 6.1710 0.0000 Vlastnictvi 1167.72 416.489 2.8037 0.0063 Zdivo 433.750 496.740 0.8732 0.3851 Stav 583.356 207.783 2.8075 0.0062 Balkon 953.559 420.328 2.2686 0.0259 Terasa 832.797 510.941 1.6299 0.1070 Lodzie 1881.05 409.613 4.5923 0.0000 Sklep 218.163 379.492 0.5749 0.5669 Garaz 2629.15 386.013 6.8110 0.0000 Parking 388.270 345.118 1.1250 0.2639 Vybaveni 320.837 100.586 3.1897 0.0020 Patro 143.220 86.8019 1.6500 0.1028 MHD 1345.51 350.678 3.8369 0.0002 Statistics based on the weighted data: Sum squared resid 158491.6 S.E. of regression 43.96390 R 2 0.966967 Adjusted R 2 0.961327 F(14, 82) 171.4549 P-value(F) 1.67e 54 Log-likelihood 496.4762 Akaike criterion 1022.952 Schwarz criterion 1061.573 Hannan Quinn 1038.569 Statistics based on the original data: Mean dependent var 10367.11 S.D. dependent var 4714.400 Sum squared resid 8.60e+08 S.E. of regression 3239.380 Tabulka B.8: Odhad metodou WLS pro pronájem bytů lvii

PŘÍLOHA B. METODA NEJMENŠÍCH ČTVERCŮ PRONÁJEM BYTŮ Coefficient Std. Error t-ratio p-value const 2511.32 1517.06 1.6554 0.1014 Pokoj 2896.44 134.865 21.4766 0.0000 Vlastnictvi 1317.88 417.763 3.1546 0.0022 Stav 510.735 150.001 3.4049 0.0010 Balkon 749.050 283.419 2.6429 0.0097 Lodzie 1816.25 442.912 4.1007 0.0001 Garaz 2300.82 509.070 4.5197 0.0000 Vybaveni 432.752 82.3677 5.2539 0.0000 MHD 956.719 228.747 4.1824 0.0001 Statistics based on the weighted data: Sum squared resid 160069.2 S.E. of regression 42.64936 R 2 0.934586 Adjusted R 2 0.928639 F(8, 88) 157.1590 P-value(F) 1.03e 48 Log-likelihood 496.9566 Akaike criterion 1011.913 Schwarz criterion 1035.086 Hannan Quinn 1021.283 Statistics based on the original data: Mean dependent var 10367.11 S.D. dependent var 4714.400 Sum squared resid 9.46e+08 S.E. of regression 3277.942 Tabulka B.9: Odhad metodou WLS pro pronájem bytů, zúžený model lviii

PŘÍLOHA C. INSTRUMENTÁLNÍ PROMĚNNÉ PRODEJ BYTŮ Příloha C Instrumentální proměnné Prodej bytů lix

PŘÍLOHA C. INSTRUMENTÁLNÍ PROMĚNNÉ PRODEJ BYTŮ Coefficient Std. Error t-ratio p-value const 19.5547 2.05470 9.5171 0.0000 Pokoj 23.2431 0.821742 28.2852 0.0000 Balkon 5.97209 1.66068 3.5962 0.0004 Terasa 20.2691 1.78076 11.3823 0.0000 Lodzie 4.31710 2.50783 1.7214 0.0862 Sklep 5.12603 1.58439 3.2353 0.0013 Mean dependent var 73.77701 S.D. dependent var 29.22626 Sum squared resid 47634.59 S.E. of regression 12.35617 R 2 0.824079 Adjusted R 2 0.821260 F(5, 312) 292.3056 P-value(F) 2.1e 115 Log-likelihood 1247.695 Akaikecriterion 2507.391 Schwarzcriterion 2529.963 Hannan Quinn 2516.406 Tabulka C.1: Odhad metodou OLS pro Podlahovou plochu bytu pro prodej bytů lx

PŘÍLOHA C. INSTRUMENTÁLNÍ PROMĚNNÉ PRODEJ BYTŮ Coefficient Std. Error t-stat p-value const 2.11732e+006 379111. 5.5850 0.0000 m2 24263.1 1431.55 16.9488 0.0000 Vlastnictvi 606345. 111266. 5.4495 0.0000 Zdivo 126697. 128408. 0.9867 0.3238 Stav 63355.7 59234.8 1.0696 0.2848 Garaz 103214. 175785. 0.5872 0.5571 Parking 10130.3 141895. 0.0714 0.9431 Vybaveni 165040. 38401.0 4.2978 0.0000 Patro 109096. 21787.2 5.0073 0.0000 MHD 47032.3 55064.0 0.8541 0.3930 Mean dependent var 2349384 S.D. dependent var 1034754 Sum squared resid 1.17e+14 S.E. of regression 617065.9 R 2 0.654617 Adjusted R 2 0.644525 F(9, 308) 56.22855 P-value(F) 7.80e 60 Hausman test Null hypothesis: OLS estimates are consistent Asymptoticteststatistic: χ 2 (1)=1.02308 with p-value = 0.311789 Sargan over-identification test Null hypothesis: all instruments are valid Test statistic: LM = 64.0841 withp-value=p(χ 2 (4) >64.0841)=4.01215e-013 Weak instrument test First-stage F(5, 304) = 289.373 Tabulka C.2: Odhad metodou 2SLS pro prodej bytů lxi