Use the International Stock Market database from “Excel Databases.xls” on Blackboard.  Use Excel to develop a multiple regression model to predict the Nikkei by the DJIA, the Nasdaq, the S&P 500, the Hang Seng, the FTSE 100, and the IPC.  Assume a 1% level of significance. What percent of residuals are within 1 standard error?  Write your answer as a number rounded to 1 decimal place.  Do not include the % sign in your answer   DATE   DJIA NASDAQ S&P 500 NIKKEI 225 HANG SENG FTSE 100 IPC 9-Feb   8270.87 1591.56 869.89 87.63 1658.5 5774.95 1351.73 9-Jan   8000.86 1476.42 825.88 97.73 1856.39 6375.22 1616.08 8-Dec   8776.39 1577.03 903.25 89.49 1820.22 6033.18 1436.55 8-Nov   8829.04 1535.57 896.24 86.68 1850.86 7041.92 1612.58 8-Oct   9336.93 1720.95 968.75 107.68 2319.76 8780.95 2284.53 8-Sep   10850.66 2091.88 1164.74 118.69 2678.44 10077.78 2554.66 8-Aug   11543.55 2367.52 1282.83 121.78 2929.61 10569.58 2700.02 8-Jul   11378.02 2325.55 1267.38 127.03 2834.06 10912.45 2814.32 8-Jun   11350.01 2292.98 1280 138.12 3182.05 11797.14 3059.82 8-May   12638.32 2522.66 1400.38 132.27 3304.82 12020.49 2886.28 8-Apr   12820.13 2412.80 1385.59 124.23 2970.75 11561.18 2996.44 8-Mar   12262.89 2279.10 1322.7 125.67 3029.8 11534.82 2757.18 8-Feb   12266.39 2271.48 1330.63 127.03 3094.02 11869.34 2721.47 8-Jan   12650.36 2389.86 1378.55 137.03 3566.91 12853.11 2706.25 7-Dec   13264.82 2652.28 1468.36 141.48 3680.01 13202.62 2744.68 7-Nov   13371.72 2660.96 1481.14 146.86 4059.75 13710.23 2887.75 7-Oct   13930.01 2859.12 1549.38 145.66 3493.38 13292.91 2824.73 7-Sep   13895.63 2701.50 1526.75 142.62 3066.91 12744.42 2795.17 7-Aug   13357.74 2596.36 1473.99 142.31 2868.1 12683.95 2738.32 7-Jul   13211.99 2546.27 1455.27 148.36 2785.83 13271.05 2918.1 7-Jun   13408.62 2603.23 1503.35 147.16 2638.3 13213.43 2979.65 7-May   13627.64 2604.52 1530.62 144.26 2597.52 12833.75 2650.22 7-Apr   13062.91 2525.09 1482.37 144.55 2534.49 12491.48 2643.28 7-Mar   12354.35 2421.64 1420.86 148.38 2476.25 11972.75 2381.06 7-Feb   12268.63 2416.15 1406.82 145.38 2617.79 12379.44 2533.66 7-Jan   12621.69 2463.93 1438.24 144.57 2567.06 12175.05 2442.83 6-Dec   12463.15 2415.29 1418.3 141.71 2403.98 11926.86 2268.27 6-Nov   12221.93 2431.77 1400.63 139.99 2372.8 11727.62 2145.5 6-Oct   12080.73 2366.71 1377.94 138.07 2251.33 11229.61 1969.79 6-Sep   11679.07 2258.43 1335.85 137.52 2240.08 11323.31 1942.88 6-Aug   11381.15 2183.75 1303.82 134.09 2175.71 10981.88 1810.7 6-Jul   11185.68 2091.47 1276.66 135.75 2101.99 10851.78 1804.9 6-Jun   11150.22 2172.09 1270.2 137.92 2016.67 10740.7 1699.2 6-May   11168.31 2178.88 1270.09 149.8 2148.93 11033.48 1870.23 6-Apr   11367.14 2322.57 1310.61 146.92 2070.09 10464.16 1805.45 6-Mar   11109.32 2339.79 1294.87 137.66 2039.02 10244.06 1820.84 6-Feb   10993.41 2281.39 1280.66 139.92 2029.29 10324.87 1837.52 6-Jan   10864.86 2305.82 1280.08 136.5 1918.66 9646 1685.12 5-Dec   10717.50 2205.32 1248.29 125.61 1942.99 9490.98 1627.2 5-Nov   10805.87 2232.82 1249.48 118.87 1879.79 9413.49 1479.4 5-Oct   10440.07 2120.30 1207.01 118.36 1984.42 9652.19 1490.35 5-Sep   10568.70 2151.69 1228.81 113.63 1949.17 9746.37 1353.07 5-Aug   10481.60 2152.09 1220.33 106.54 1927.06 9366.65 1376.06 5-Jul   10640.91 2184.83 1234.18 104.15 1827.1 9142.46 1259.04 5-Jun   10274.97 2056.96 1191.33 104.66 1782.11 9098.04 1211.94 5-May   10467.48 2068.22 1191.5 104.65 1783.94 9089.81 1127.03 5-Apr   10192.51 1921.65 1156.85 109.24 1729.81 9281.12 1140.06 5-Mar   10503.76 1999.23 1180.59 112.78 1802.76 9600.89 1243.08 5-Feb   10766.23 2051.72 1203.6 109.39 1740.82 9221.95 1193.78 5-Jan   10489.94 2062.41 1181.27 111.68 1830.55 9162.32 1164.34 4-Dec   10783.01 2175.44 1211.92 104.79 1821.7 9130.15 1094.63 4-Nov   10428.02 2096.81 1173.82 100.91 1683.15 8568.45 1007.95 4-Oct   10027.47 1974.99 1130.2 99.49 1682.63 8369.66 975.11 4-Sep   10080.27 1896.84 1114.58 101.64 1669.73 8074.64 906.4 4-Aug   10173.92 1838.10 1104.24 101.27 1564.33 8065.1 890.57 4-Jul   10139.71 1887.36 1101.72 109.97 1575.17 8032.19 894.2 4-Jun   10435.48 2047.79 1140.84 102.77 1552.7 8126.01 875.08 4-May   10188.45 1986.74 1120.68 106.54 1532.17 7967.62 891.59 4-Apr   10225.57 1920.15 1107.3 112.69 1627.42 8192.45 950.59 4-Mar   10357.70 1994.22 1126.21 103.45 1788.16 8479.88 924.09 4-Feb   10583.92 2029.82 1144.94 102.15 1671.89 7969.92 880.17 4-Jan   10488.07 2066.15 1131.13 99.62 1619.86 8014.26 782.69 9-Feb   8270.87 1591.56 869.89 87.63 1658.5 5774.95 1351.73

Use the Financial database from “Excel Databases.xls” on Blackboard.  Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company.  Use Excel to develop the multiple linear regression model.  Assume a 5% level of significance.   Which independent variable is the strongest predictor of the average P/E ratio of a company?     A. Total Revenues B. Average Yield C. Earnings Per Share D.Return on Equity E. Total Assets F.Dividends Per Share   Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287…

Give an example of how a nonrepresentative sample can severely affect a regression analysis.

Suppose your firm generated $50.0million$⁢50.0⁢million in sales. What would be the 95%95% prediction interval for your firm's net income? Write your answer using interval notation. Round your answer to 1 decimal place.