A company has 50,000 shares of common stock outstanding. The stockholders' equity applicable to common shares is $1,470,000, and the par value per common share is $5. The book value per share is:
A) $4.75.
B) $14.70.
C) $10.00.
D) $29.40.
E) $47.50.
D) $29.40.
Explanation: Book Value per Share = Stockholders' Equity Applicable to Common/Common
Shares Outstanding
Book Value per Share = $1,470,000/50,000 = $29.40 per share
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Articles 2 and 2(
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To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below:
Land Value ($) Building Value ($) Acres Baths Toilets Fireplaces Bedrooms Age Sale Price ($) 18,100 92,500 0.5 1 1 1 4 53.9 114,885 23,600 152,700 0.22 2 1 1 3 19.7 180,895 25,900 134,300 0.3 2 1 1 3 15.9 162,038 22,100 129,600 0.23 2 2 1 4 41 154,496 23,900 168,700 0.32 2 1 1 4 39.9 196,973 22,400 118,300 0.25 2 1 1 3 41.8 145,075 24,100 123,300 0.26 1 1 1 4 70.9 151,480 26,300 133,800 0.26 2 1 1 3 37.8 164,762 24,900 139,400 0.24 2 1 1 4 33 166,528 13,600 87,200 0.17 1 1 0 3 34.7 105,762 36,100 210,400 0.6 2 1 2 2 52.9 250,170 19,500 101,300 0.16 1 1 1 2 67.8 125,082 38,800 224,700 0.44 2 1 1 4 21.7 265,066 23,500 139,000 0.22 1 1 0 3 10.8 166,697 26,300 164,200 0.35 2 1 0 3 3.9 194,881 21,900 122,400 0.17 2 1 1 3 15.7 146,818 23,400 149,600 0.22 2 1 1 3 15.7 176,048 15,000 102,200 0.12 1 1 0 3 97.8 119,584 15,000 102,200 0.12 1 1 0 3 97.7 121,759 9,200 22,000 0.17 1 1 0 4 120.9 34,947 9,200 22,000 0.17 1 1 0 4 120.9 35,214 5,600 48,000 0.12 1 1 0 3 103.9 57,142 9,000 58,800 0.24 1 1 0 3 88 72,192 21,000 109,600 0.21 2 1 0 3 36.7 133,848 23,500 165,900 0.15 2 1 1 4 5.7 194,079 36,000 262,500 0.22 3 1 1 4 2.9 300,407 23,700 114,900 0.22 1 1 0 4 37.7 141,700 22,000 102,700 0.2 1 1 0 3 48.9 128,866 19,900 95,800 0.23 2 1 1 2 78.9 119,189 22,100 116,300 0.18 1 1 0 3 30.8 141,018 24,600 165,500 0.29 1 1 1 4 43 193,661 21,500 113,400 0.17 1 1 1 3 44.9 137,308 15,000 81,100 0.16 1 1 0 2 62.9 99,817 15,700 129,200 0.23 2 1 0 3 46.7 148,909 14,200 81,600 0.15 1 1 0 3 57.9 100,701 10,700 49,700 0.15 1 1 0 2 99.8 65,082 16,600 72,700 0.18 1 1 1 4 91.8 92,614 25,500 110700 0.21 1 1 1 3 48 137,889 15,100 74,300 0.23 1 1 1 3 71.8 91,180 7,400 55,500 0.15 1 1 0 2 96.8 64,119 28,500 129,400 0.25 1 1 0 4 49.9 160,139 25,100 83,900 0.2 1 1 0 3 45.8 113,043 50,100 164,600 0.23 2 1 0 3 44 217,684 83,300 276,000 0.61 3 1 1 2 47.9 360,936 124,500 552,300 1.05 4 1 2 4 5.7 679,795 47,000 214,400 0.22 2 1 2 4 92.9 264,115 64,600 185,000 0.58 2 1 1 4 91 254,075 33,900 138,800 0.22 1 1 1 4 97.9 173,987 41,100 156,300 0.18 1 1 1 3 76 200,251 29,100 96,400 0.28 1 1 0 1 57.8 130,214 56,400 256,400 0.4 1 1 1 3 56.8 316,874 45,400 219,200 0.21 1 1 1 3 79.8 267,672 23,800 92,100 0.15 1 1 1 4 91.9 119,769 52,800 172,800 0.27 2 1 2 2 74.8 229,499 25,100 99,200 0.19 1 1 0 3 36.7 128,456 27,200 152,600 0.18 2 1 1 3 16.7 181,102 28,100 102,900 0.18 1 1 1 3 75.8 132,977 28,800 98,800 0.19 1 2 0 3 53.9 131,411 33,400 103,900 0.45 2 1 1 4 84.9 139,697 20,700 95,600 0.14 1 1 1 3 89.8 120,046 25,600 101,900 0.2 1 1 0 2 57.8 131,026 25,800 110,700 0.18 1 1 0 3 51.9 141,202 29,300 147,700 0.2 1 1 1 4 90.9 181,575 26,000 116,000 0.18 1 1 1 3 44 144,513 25,900 73,500 0.16 1 1 0 2 81.8 100,953 32,800 125,000 0.35 1 1 1 3 68.7 160,546 31,100 166,800 0.2 1 1 2 2 57.7 199,970 25,800 105,300 0.17 1 1 0 3 58.8 134,647 27,200 94,800 0.17 1 1 0 3 42.9 124,311 25,000 105,900 0.16 1 1 1 3 82 133,543 29,200 117,500 0.2 1 1 1 3 53.8 151,392 30,000 93,300 0.26 1 1 0 2 55.7 124,476 20,400 112,000 0.13 2 1 1 3 80.9 136,599 23,600 83,400 0.16 1 1 0 2 57.7 110,399 16,200 85,800 0.1 1 1 1 2 67 105,027 29,300 123,900 0.22 1 1 1 4 44.8 157,819 27,000 97,800 0.18 1 1 0 3 46.8 129,675 25,600 86,300 0.16 1 1 0 3 61.7 115,952 46,200 220,500 0.57 2 1 1 4 50.8 268,552 22,900 160,000 0.15 3 1 1 3 20.7 187,870 27,100 105,200 0.21 1 1 0 3 51.8 135,549 30,700 107,100 0.3 1 1 0 3 70 142,738 29,100 102,400 0.23 2 1 0 2 58 135,284 34,700 150,400 0.28 1 1 2 3 68.9 189,790 20,000 80,400 0.24 1 1 0 3 66.9 105,302 35,700 159,400 0.28 2 1 1 4 1.7 196,936 35,100 161,500 0.25 2 1 1 3 8.8 201,349 33,700 162,500 0.21 2 1 1 4 8.8 198,580 33,700 162,500 0.21 2 1 1 4 8.8 200,228 36,400 176,100 0.29 2 1 1 4 8.9 215,634 33,200 122,300 0.2 2 1 0 3 4.9 157,208 39,200 169,200 0.36 2 1 1 3 5.9 212,662 33,100 180,100 0.2 2 1 1 4 5.8 217,543 16,000 98,400 0.19 1 1 0 4 49.9 118,491 24,900 63,800 0.45 2 1 1 2 83.9 91,539 22,000 121,300 0.27 1 2 0 4 34.9 147,802 20,000 107,600 0.23 1 1 1 3 36.7 131,948 33,900 230,800 0.27 2 1 1 3 10 268,444 22,100 153,800 0.3 1 1 1 3 46.8 180,464 22,800 111,100 0.23 1 1 0 3 52 137,326 24,700 117,800 0.32 1 1 0 3 48.7 145,115 38,700 118,700 0.81 1 1 1 3 47.8 159,644 25,800 108,000 0.26 2 1 0 2 53.3 135,049 31,700 140,500 0.34 1 1 1 3 40.6 174,475 82,200 171,700 1.23 2 1 1 3 56.4 257,467 19,500 147,600 0.53 2 1 1 2 28.2 169,311 24,400 132,000 0.25 2 1 1 3 14.2 157,570 22,500 119,800 0.18 2 1 1 3 15.5 143,676 25,900 117,100 0.29 2 1 0 3 17.7 146,960 22,700 95,000 0.25 1 1 0 3 55.6 121,175 21,200 56,700 0.23 1 1 0 2 96.6 81,869 34,000 163,800 0.26 2 1 1 4 15.2 199,361 18,900 118,000 0.17 1 1 0 3 45.5 139,981 33,900 151,600 0.26 2 1 1 3 25.3 186,637 23,800 133,500 0.21 2 1 1 3 13.6 161,123 23,900 119,000 0.21 2 1 1 3 14.3 146,054 18,500 110,500 0.19 2 1 2 4 32.2 130,575 36,300 122,500 0.61 1 1 2 3 56.2 162,270 47,300 298,800 0.36 3 1 1 4 31.4 348,138 36,600 238,700 0.28 2 1 2 3 25.5 278,839 Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price using a regression tree. Use Sale Price as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Regression Tree procedure, be sure to Normalize input data, set the Maximum #splits for input variables to 59, set the Minimum #records in a terminal node to 1, and specify Using Best pruned tree as the scoring option. In Step 3 of XLMiner's Regression Tree procedure, set the maximum number of levels to 7. Generate the Full tree and Best pruned tree. a. In terms of number of decision nodes, compare the size of the full tree to the size of the best pruned tree.b. What is the root mean squared error (RMSE) of the best pruned tree on the validation data and on the test data?c. What is the average error on the validation data and test data? What does this suggest?d. By examining the best pruned tree, what are the critical variables in predicting the sale price of a home? What will be an ideal response?