Using the International Stock Market data in below, conduct a stepwise multiple regression procedure to predict...

Using the International Stock Market data in below, conduct a stepwise multiple regression procedure to predict DJIA by the Nasdaq, the S&P 500, the Nikkei, the Hang Seng, the FTSE 100, and the IPC.

Develop a correlation matrix and discuss the correlations among all variables. ( please show step by step about doing correlation in excel and discuss)
Perform a stepwise regression procedure (use α = 0.1) to select predictor variables for the model. Summarize the results (with t-values and R²) for every step of the procedure in a table (similar to the ones shown on the Model-Building Search Procedures slides #9 – 10)
Write the equation of the regression model obtained from this procedure.

Using the International Stock Market data in conduct a stepwise multiple regression procedure to predict DJIA by the Nasdaq, the S&P 500, the Nikkei, the Hang Seng, the FTSE 100, and the IPC.

DJIA	S&P 500	NASDAQ	NIKKEI 225	HANG SENG	FTSE 100	IPC
8270.87	869.89	1591.56	87.63	1658.5	5774.95	1351.73
8000.86	825.88	1476.42	97.73	1856.39	6375.22	1616.08
8776.39	903.25	1577.03	89.49	1820.22	6033.18	1436.55
8829.04	896.24	1535.57	86.68	1850.86	7041.92	1612.58
9336.93	968.75	1720.95	107.68	2319.76	8780.95	2284.53
10850.66	1164.74	2091.88	118.69	2678.44	10077.78	2554.66
11543.55	1282.83	2367.52	121.78	2929.61	10569.58	2700.02
11378.02	1267.38	2325.55	127.03	2834.06	10912.45	2814.32
11350.01	1280	2292.98	138.12	3182.05	11797.14	3059.82
12638.32	1400.38	2522.66	132.27	3304.82	12020.49	2886.28
12820.13	1385.59	2412.80	124.23	2970.75	11561.18	2996.44
12262.89	1322.7	2279.10	125.67	3029.8	11534.82	2757.18
12266.39	1330.63	2271.48	127.03	3094.02	11869.34	2721.47
12650.36	1378.55	2389.86	137.03	3566.91	12853.11	2706.25
13264.82	1468.36	2652.28	141.48	3680.01	13202.62	2744.68
13371.72	1481.14	2660.96	146.86	4059.75	13710.23	2887.75
13930.01	1549.38	2859.12	145.66	3493.38	13292.91	2824.73
13895.63	1526.75	2701.50	142.62	3066.91	12744.42	2795.17
13357.74	1473.99	2596.36	142.31	2868.1	12683.95	2738.32
13211.99	1455.27	2546.27	148.36	2785.83	13271.05	2918.1
13408.62	1503.35	2603.23	147.16	2638.3	13213.43	2979.65
13627.64	1530.62	2604.52	144.26	2597.52	12833.75	2650.22
13062.91	1482.37	2525.09	144.55	2534.49	12491.48	2643.28
12354.35	1420.86	2421.64	148.38	2476.25	11972.75	2381.06
12268.63	1406.82	2416.15	145.38	2617.79	12379.44	2533.66
12621.69	1438.24	2463.93	144.57	2567.06	12175.05	2442.83
12463.15	1418.3	2415.29	141.71	2403.98	11926.86	2268.27
12221.93	1400.63	2431.77	139.99	2372.8	11727.62	2145.5
12080.73	1377.94	2366.71	138.07	2251.33	11229.61	1969.79
11679.07	1335.85	2258.43	137.52	2240.08	11323.31	1942.88
11381.15	1303.82	2183.75	134.09	2175.71	10981.88	1810.7
11185.68	1276.66	2091.47	135.75	2101.99	10851.78	1804.9
11150.22	1270.2	2172.09	137.92	2016.67	10740.7	1699.2
11168.31	1270.09	2178.88	149.8	2148.93	11033.48	1870.23
11367.14	1310.61	2322.57	146.92	2070.09	10464.16	1805.45
11109.32	1294.87	2339.79	137.66	2039.02	10244.06	1820.84
10993.41	1280.66	2281.39	139.92	2029.29	10324.87	1837.52
10864.86	1280.08	2305.82	136.5	1918.66	9646	1685.12
10717.50	1248.29	2205.32	125.61	1942.99	9490.98	1627.2
10805.87	1249.48	2232.82	118.87	1879.79	9413.49	1479.4
10440.07	1207.01	2120.30	118.36	1984.42	9652.19	1490.35
10568.70	1228.81	2151.69	113.63	1949.17	9746.37	1353.07
10481.60	1220.33	2152.09	106.54	1927.06	9366.65	1376.06
10640.91	1234.18	2184.83	104.15	1827.1	9142.46	1259.04
10274.97	1191.33	2056.96	104.66	1782.11	9098.04	1211.94
10467.48	1191.5	2068.22	104.65	1783.94	9089.81	1127.03
10192.51	1156.85	1921.65	109.24	1729.81	9281.12	1140.06
10503.76	1180.59	1999.23	112.78	1802.76	9600.89	1243.08
10766.23	1203.6	2051.72	109.39	1740.82	9221.95	1193.78
10489.94	1181.27	2062.41	111.68	1830.55	9162.32	1164.34
10783.01	1211.92	2175.44	104.79	1821.7	9130.15	1094.63
10428.02	1173.82	2096.81	100.91	1683.15	8568.45	1007.95
10027.47	1130.2	1974.99	99.49	1682.63	8369.66	975.11
10080.27	1114.58	1896.84	101.64	1669.73	8074.64	906.4
10173.92	1104.24	1838.10	101.27	1564.33	8065.1	890.57
10139.71	1101.72	1887.36	109.97	1575.17	8032.19	894.2
10435.48	1140.84	2047.79	102.77	1552.7	8126.01	875.08
10188.45	1120.68	1986.74	106.54	1532.17	7967.62	891.59
10225.57	1107.3	1920.15	112.69	1627.42	8192.45	950.59
10357.70	1126.21	1994.22	103.45	1788.16	8479.88	924.09
10583.92	1144.94	2029.82	102.15	1671.89	7969.92	880.17
10488.07	1131.13	2066.15	99.62	1619.86	8014.26	782.69
8270.87	869.89	1591.56	87.63	1658.5	5774.95	1351.73

about summarizing the result for every step, just show me the dependent variable and show predictor variables for the model .

Expert Solution

Type in data in the same way as it is mentioned in the question > Go to the Data Tab > Select Data Analysis > Select Regrssion from the list > Select the DJIA column for Y Range and the other ranges for the X range > Type in the confidence Level as 90% for alpha = 0.1 > Click on OK

The following Sheet appears

NOTE: The above image shows only the 1st 30 rows but the output summary incorporates all the 63 inputs of each exchange.

jeff jeffy answered 1 year ago

1) You conduct a stepwise regression according to the following procedure: Step 1: HS_SCI only Step 2: HS_SCI...

1) You conduct a stepwise regression according to the following procedure: Step 1: HS_SCI only Step 2: HS_SCI and HS_ENG Step 3: HS_SCI, HS_ENG and HS_MATH Step 4: HS_SCI, HS_ENG, HS_MATH and PARENT EDUC Step 5: HS_SCI, HS_ENG and HS_MATH, PARENT EDUC AND GENDER Step 6: HS_SCI, HS_ENG and HS_MATH, PARENT EDUC, GENDER and ATAR Present the regression output for each of the six steps. Choice of reference category: It is recommended that you choose U (undergraduate education) as the reference category for the categorical variable:...

Using the Instructor ranking data conduct a simple linear regression to predict a student’s scores based...

Using the Instructor ranking data conduct a simple linear regression to predict a student’s scores based on the number of hours the student studies and answer the following questions: What is the value of the intercept of this regression equation? What is the value of the slope of this regression equation and what is its interpretation within the context of this problem? Write the equation for this logistic regression model R2 = What does this number mean? F= Explain how F (F...

Using the data in the Excel file Home Market Value, develop a multiple regression model for...

Using the data in the Excel file Home Market Value, develop a multiple regression model for estimating the market value as a function of house age and house size. Predict the value of a house that is 30 years old and has 1800 square feet, and also predict the value of a house that is 5 years old and has 2800 square feet. Conduct your analysis using the following Multiple Regression Model Building and Interpretation Rubric: Identify the dependent variable...

Use the following data to develop a multiple regression model to predict from and . Discuss...

Use the following data to develop a multiple regression model to predict from and . Discuss the output, including comments about the overall strength of the model, the significance of the regression coefficients, and other indicators of model fit. y x1 x2 198 29 1.64 214 71 2.81 211 54 2.22 219 73 2.70 184 67 1.57 167 32 1.63 201 47 1.99 204 43 2.14 190 60 2.04 222 32 2.93 197 34 2.15 Appendix A Statistical Tables *(Round...

Based on the various stepwise approaches for selecting an excellent (if not optimal) fitted multiple regression...

Based on the various stepwise approaches for selecting an excellent (if not optimal) fitted multiple regression model, which one approach would you select if you had to explain to a client what your chosen model is and what approach you used to obtain it?

USING MINITAB 13.2 - Multiple Linear Regression Exercise – Stock Price Data File: as above The...

USING MINITAB 13.2 - Multiple Linear Regression Exercise – Stock Price Data File: as above The stock broker now wants to include additional predictor variables to determine the driving factors for the stock price increase of Company A. Perform a multiple linear regression analysis between Company A and the 10 Yr. T, GDP and Unemployment (columns C1, C3, C4, and C5). Use the Minitab data file Regression.MTW. Company A Company B 10-Yr T GDP ($millions) Unemployment 36.25 30 2.33 7308755 ...

Use the HousePrice data and via multiple regression select the two variables that predict the house...

Use the HousePrice data and via multiple regression select the two variables that predict the house selling price the best. Make another table with these two variables and answer the questions. Numerical answers are rounded so choose the answer that matches the best: 9. Identify the negative coefficient. What is its value and what is the interpretation of this number? (Choose the most appropriate answer. Note: numbers are truncated.) a. -0.037; This is a negative number and serves no statistical...

Use the CO2 data and via Multiple regression select the two variables that predict the CO2...

Use the CO2 data and via Multiple regression select the two variables that predict the CO2 level with the best P-value. Make another table with these two variables and answer the questions. Numerical answers are rounded so choose the answer that matches the best: Hour CO Traffic Wind 1 2.4 50 -0.2 2 1.7 26 0.0 3 1.4 16 0.0 4 1.2 10 0.0 5 1.2 12 0.1 6 2.0 41 -0.1 7 3.4 157 -0.1 8 5.8 276 -0.2...

1.Develop a multiple linear regression model to predict the price of a house using the square...

1.Develop a multiple linear regression model to predict the price of a house using the square feet of living area, number of bedrooms, and number of bathrooms as the predictor variables Write the reqression equation. Discuss the statistical significance of the model as a whole using the appropriate regression statistic at a 95% level of confidence. Discuss the statistical significance of the coefficient for each independent variable using the appropriate regression statistics at a 95% level of confidence....

The table below summarizes nested multiple regression models used to predict a person’s quality of life...

The table below summarizes nested multiple regression models used to predict a person’s quality of life score. Model 1 Model 2 Model 3 est. sig. est. sig. est. sig. intercept 16.68 <.001 9.16 <.001 7.57 <.001 size of social network 0.59 0.027 0.44 0.076 0.43 0.059 college degree — 3.73 0.014 3.87 0.030 time (yrs) at current job — — 0.91 0.046 # of siblings — — –0.68 0.146 R2R2 3.65% 8.01% 9.60% ΔR2ΔR2 3.65% 4.36% 1.59% FF (for ΔR2ΔR2)...

Question