please if there anyone can do this need your help or if you guys done before,need help thank you so much in advance

below are data

Case 1: Housing Price Structure In Mid City Sales of single-family houses have been brisk in Mid City this year. This has especially been true in older, more established neighborhoods, where housing is relatively inexpensive compared to the new homes being built in the newer neighborhoods. Nevertheless, there are also many families who are willing to pay a higher price for the prestige of living in one of the newer neighborhoods. The worksheet Q1.xls contains data on 128 recent sales in Mid City. For each sale, the file shows the neighborhood (1, 2, or 3) in which the house is located, the number of offers made on the house, the square footage, whether the house is made primarily of brick, the number of bathrooms, the number of bedrooms, and the selling price. Neighborhoods 1 and 2 are more traditional neighborhoods, whereas neighborhood 3 is a newer, more prestigious, neighborhood. Use regression to estimate and interpret the pricing structure of houses in Mid City. Here are some considerations. 1 Please draw the plots about Brick vs Price, Nbhd vs Price, Offers vs Price, respectively. Could you judge how Brick, Nbnd and Offers influence the house price? (5 points) 2. Please fill the table below (Hint: You could use the sample selection function in STATA or in the filter function in Excel) (5 points) Price Median (sum. Standard devia astheile Is there a "premium" for a brick house, everything else being equal? (5 points) 4. Is there a premium for a house in neighborhood 3, everything else being equal? (5 points) 5. Is there an extra premium for a brick house in neighborhood 3, in addition to the usual premium for a brick house? (Hint: You need to regard an interaction effect) (5 points) 6. For purposes of estimation and prediction, could neighborhoods 1 and 2 be collapsed into a single "older" neighborhood? (5 points) 7. In your opinion, which factors are significant to affect the housing price? (5 points) (Hint: You would use the regression models to solve the questions 3-7. You need to create dummies for the characteristics of house by using STATA, such as brick, neighborhood, etc.) Homes sold recently in Mid City Data used for your work Home 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 13 19 21 22 23 24 25 25 28 30 31 32 33 34 35 36 Nbhd Offers Soft Brick Bedrooms Bathrooms Price 275 110002 20306 NO 41 1142008 21 1741.7 Ne 31 1148000 21 No 31 94700.3 21 3 2130 Ne 3 1198006 11 Ne 3 1516003 31 227613 Nel 4 1507016 20 21152 No 4 2119200.5 2 17514 NO 3 2 2030.71 Yes 132503.9 21 2137315 Yes 1230012 4 114 NO 31 102600.8 S 2153 31 126303.6 4282 NO 4 176806.1 31 NO 4 145800,6 21 Yes 31 1471013 T01 No 60274 17015 2 1114042 3 2792031 31 1672019 2 17937 Ne 1162010 1 20010 31 1138018 1 1693 Ne 91704.5 3 1824.4 Yes 31 1061041 222152 41 156400.8 220 NO 4 149306.3 31 20052 4 1370019 21 2 1745 No 3 99301.3 16033 NO 21 69100.0 3 020411 Yes 1880038 3 4 1820019 1 2 1932.5 2 1123032 2 22550 31 13500227 2 4 22815 5 139600.3 No 2 1178014 1 320552 No 31 11/100.9 2 15810 No 2 1175041 31 4241 No 41 147000.5 No 31 1313007 1 31 1082047 2 115628 Ne 1066021 3 NO 4 133603.5 21 319921 105600.6 21 19214 3 154002.5 31 166500 2 20 18146 No 31 10320221 1 21 12980278 62051 NO 31 903025 2 2 1852 NO 21 1150011 17032 Yes 3 21075040 2 21010 3 1511007 1 3186351 Nel 2 91101.6 4 21:14 NO 21 117401.7 321039 Ne 210009 316511 No 3 813045 21 217233 Yes 21 125701.9 2 210 31 1409009 31 3 22420 No 1523060 3 No 31 138100.0 31 NO 41 1554049 NO 31 1800037 216120 NO 2 21000019 31 2 22241 NO 4 161304.5 4 1915 21 1205013 2 1860 No 130302.6 1111003 22122 No 126203.3 Brick Yes 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 00 0.0 1,0 1.0 0.0 1.0 0.0 0.0 1.0 00 1.0 1,0 00 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 39 40 41 42 44 45 47 49 50 51 52 53 54 55 56 38 59 60 61 62 63 64 65 66 68 69 3 4 3 70 71 72 73 74 75 76 77 4 3 31 3 2 3 4 3 4 3 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 4 2 3 2 4 4 3 3 2 1 3 3 3 1 2 3 1 3 3 1 3 2 3 3 2 1 3 1 3 1 1 3 2 3 2 3 3 2 2 3 3 1 2 2. 3 2 3 1 2 2 2 1 2 4 3 3 3 4 98 99 3 20428 No 421432 No 3 2083.5 No 3 1953.7 Yes 121638 No 3 1653.8 2 2043.61 No 3 21434 No 21903.0 2 19326 No 3 2282.0 Yes 321314 No 1 1780.7 No 4 2195.1 Yes 2 21442 12053.2 Yes 2 24 12.11 No 3|1520.91 No 222540 Yes 4 1903.5 No 118821 Yes 2 1930.5 4 2014 21 No 21922.5 No 2 2150.7 No 2] 2114.2 No 220823 No 3 2150 31 Yes 1197375 Yes 3 24414 No 120044 Yes 1 2062.2 No 22085.1 Yes 5 2012.7 No 52260.2 No 4 24 1291 No 3 2440.3 Yes 41912.9 No 4 2530.21 4 2132.7 No 11895.11 Yes 3 19922 Yes 3 21145 No 11711.5 21743.7 No 2 1940.5 Yes 3 20026 Yes 2 20140 No 31901.3 No 1229321 Yes 2 1925.1 No 31951,61 Yes 4 1923.4 No 3 1935.1 No 31 1931.4 No 12063.1 Yes 31904 G Yes 3 21608 Yes 220723 No 1 2023.7 4 2255.1 No 2 3 2 3 3 3 3 3 151902.7 21 93601,5 3 165804.9 31166702.5 2157604.8 2107301.3 311257024 3144203.1 2106903.2 2 1298028 3 176501.9 2 1213004 2. 143603.8 31 1434014 3 1843038 2164800.5 3 147702.0 21 90503.0 3 188303.7 2 102703.8 31 172503.6 31 1277020 297801.8 2 1431003 2 116503.8 2142601.5 3157104.1 3160601.1 2 1525030 3 133304,5 21 126800.3 2 145501.1 3 171001.6 2 103201.7 31231014 31 136800.9 3211205.2 21 82304.0 3146902.5 2 1085006 2 134003.7 31170012 2 108703.7 2 111600.6 2 114902.5 2 1236041 2 115700.2 3 1245012 31 1025020 4 199502.5 2 117802.7 21 150202.6 2 1097023 3 110401.7 31 105600.9 2144805.1 3 119703.7 3147902.0 2113500.3 31 149903.5 3124601.1 0.0 00 0.0 1,0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1,0 1,0 1.0 0.0 0.0 1.0 00 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1,0 1.0 0.0 1.0 0.0 1.0 00 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 00 10 10 0.0 0.0 1.0 0.0 1.0 00 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 4 3 4 31 3 3 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 128 127 128 1 2 1 2 1 3 1 1 3 2 2 2 3 4 3 5 3 3 2 2 3 2 3 4 2 3 3 1 1 2 2 2 2 1 3 1 Case 1: Housing Price Structure In Mid City Sales of single-family houses have been brisk in Mid City this year. This has especially been true in older, more established neighborhoods, where housing is relatively inexpensive compared to the new homes being built in the newer neighborhoods. Nevertheless, there are also many families who are willing to pay a higher price for the prestige of living in one of the newer neighborhoods. The worksheet Q1.xls contains data on 128 recent sales in Mid City. For each sale, the file shows the neighborhood (1, 2, or 3) in which the house is located, the number of offers made on the house, the square footage, whether the house is made primarily of brick, the number of bathrooms, the number of bedrooms, and the selling price. Neighborhoods 1 and 2 are more traditional neighborhoods, whereas neighborhood 3 is a newer, more prestigious, neighborhood. Use regression to estimate and interpret the pricing structure of houses in Mid City. Here are some considerations. 1 Please draw the plots about Brick vs Price, Nbhd vs Price, Offers vs Price, respectively. Could you judge how Brick, Nbnd and Offers influence the house price? (5 points) 2. Please fill the table below (Hint: You could use the sample selection function in STATA or in the filter function in Excel) (5 points) Price Median (sum. Standard devia astheile Is there a "premium" for a brick house, everything else being equal? (5 points) 4. Is there a premium for a house in neighborhood 3, everything else being equal? (5 points) 5. Is there an extra premium for a brick house in neighborhood 3, in addition to the usual premium for a brick house? (Hint: You need to regard an interaction effect) (5 points) 6. For purposes of estimation and prediction, could neighborhoods 1 and 2 be collapsed into a single "older" neighborhood? (5 points) 7. In your opinion, which factors are significant to affect the housing price? (5 points) (Hint: You would use the regression models to solve the questions 3-7. You need to create dummies for the characteristics of house by using STATA, such as brick, neighborhood, etc.) Homes sold recently in Mid City Data used for your work Home 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 13 19 21 22 23 24 25 25 28 30 31 32 33 34 35 36 Nbhd Offers Soft Brick Bedrooms Bathrooms Price 275 110002 20306 NO 41 1142008 21 1741.7 Ne 31 1148000 21 No 31 94700.3 21 3 2130 Ne 3 1198006 11 Ne 3 1516003 31 227613 Nel 4 1507016 20 21152 No 4 2119200.5 2 17514 NO 3 2 2030.71 Yes 132503.9 21 2137315 Yes 1230012 4 114 NO 31 102600.8 S 2153 31 126303.6 4282 NO 4 176806.1 31 NO 4 145800,6 21 Yes 31 1471013 T01 No 60274 17015 2 1114042 3 2792031 31 1672019 2 17937 Ne 1162010 1 20010 31 1138018 1 1693 Ne 91704.5 3 1824.4 Yes 31 1061041 222152 41 156400.8 220 NO 4 149306.3 31 20052 4 1370019 21 2 1745 No 3 99301.3 16033 NO 21 69100.0 3 020411 Yes 1880038 3 4 1820019 1 2 1932.5 2 1123032 2 22550 31 13500227 2 4 22815 5 139600.3 No 2 1178014 1 320552 No 31 11/100.9 2 15810 No 2 1175041 31 4241 No 41 147000.5 No 31 1313007 1 31 1082047 2 115628 Ne 1066021 3 NO 4 133603.5 21 319921 105600.6 21 19214 3 154002.5 31 166500 2 20 18146 No 31 10320221 1 21 12980278 62051 NO 31 903025 2 2 1852 NO 21 1150011 17032 Yes 3 21075040 2 21010 3 1511007 1 3186351 Nel 2 91101.6 4 21:14 NO 21 117401.7 321039 Ne 210009 316511 No 3 813045 21 217233 Yes 21 125701.9 2 210 31 1409009 31 3 22420 No 1523060 3 No 31 138100.0 31 NO 41 1554049 NO 31 1800037 216120 NO 2 21000019 31 2 22241 NO 4 161304.5 4 1915 21 1205013 2 1860 No 130302.6 1111003 22122 No 126203.3 Brick Yes 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 00 0.0 1,0 1.0 0.0 1.0 0.0 0.0 1.0 00 1.0 1,0 00 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 39 40 41 42 44 45 47 49 50 51 52 53 54 55 56 38 59 60 61 62 63 64 65 66 68 69 3 4 3 70 71 72 73 74 75 76 77 4 3 31 3 2 3 4 3 4 3 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 4 2 3 2 4 4 3 3 2 1 3 3 3 1 2 3 1 3 3 1 3 2 3 3 2 1 3 1 3 1 1 3 2 3 2 3 3 2 2 3 3 1 2 2. 3 2 3 1 2 2 2 1 2 4 3 3 3 4 98 99 3 20428 No 421432 No 3 2083.5 No 3 1953.7 Yes 121638 No 3 1653.8 2 2043.61 No 3 21434 No 21903.0 2 19326 No 3 2282.0 Yes 321314 No 1 1780.7 No 4 2195.1 Yes 2 21442 12053.2 Yes 2 24 12.11 No 3|1520.91 No 222540 Yes 4 1903.5 No 118821 Yes 2 1930.5 4 2014 21 No 21922.5 No 2 2150.7 No 2] 2114.2 No 220823 No 3 2150 31 Yes 1197375 Yes 3 24414 No 120044 Yes 1 2062.2 No 22085.1 Yes 5 2012.7 No 52260.2 No 4 24 1291 No 3 2440.3 Yes 41912.9 No 4 2530.21 4 2132.7 No 11895.11 Yes 3 19922 Yes 3 21145 No 11711.5 21743.7 No 2 1940.5 Yes 3 20026 Yes 2 20140 No 31901.3 No 1229321 Yes 2 1925.1 No 31951,61 Yes 4 1923.4 No 3 1935.1 No 31 1931.4 No 12063.1 Yes 31904 G Yes 3 21608 Yes 220723 No 1 2023.7 4 2255.1 No 2 3 2 3 3 3 3 3 151902.7 21 93601,5 3 165804.9 31166702.5 2157604.8 2107301.3 311257024 3144203.1 2106903.2 2 1298028 3 176501.9 2 1213004 2. 143603.8 31 1434014 3 1843038 2164800.5 3 147702.0 21 90503.0 3 188303.7 2 102703.8 31 172503.6 31 1277020 297801.8 2 1431003 2 116503.8 2142601.5 3157104.1 3160601.1 2 1525030 3 133304,5 21 126800.3 2 145501.1 3 171001.6 2 103201.7 31231014 31 136800.9 3211205.2 21 82304.0 3146902.5 2 1085006 2 134003.7 31170012 2 108703.7 2 111600.6 2 114902.5 2 1236041 2 115700.2 3 1245012 31 1025020 4 199502.5 2 117802.7 21 150202.6 2 1097023 3 110401.7 31 105600.9 2144805.1 3 119703.7 3147902.0 2113500.3 31 149903.5 3124601.1 0.0 00 0.0 1,0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1,0 1,0 1.0 0.0 0.0 1.0 00 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1,0 1.0 0.0 1.0 0.0 1.0 00 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 00 10 10 0.0 0.0 1.0 0.0 1.0 00 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 4 3 4 31 3 3 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 128 127 128 1 2 1 2 1 3 1 1 3 2 2 2 3 4 3 5 3 3 2 2 3 2 3 4 2 3 3 1 1 2 2 2 2 1 3 1