In the lecture videos, the professor demonstrated how to model some oil related
ID: 3217193 • Letter: I
Question
In the lecture videos, the professor demonstrated how to model some oil related data using linear regression. An excerpt of that data is shown below. Copy and paste this table into Excel. Then, using this data, predict WTI $ using all of the remaining variables in the data set. Then answer the following questions. For full credit, number your answers in your response and state specific statistics to justify your answers such as a p-value = 0.043, or X1 coefficient = 1.45698, or R-squared value = 44.5%
1. Is the model significant at a 5% level? Why or why not?
2. Assess the quality of the model. Is it a strong model? Why or why not?
3. Are all of the independent variables significant at a 5% level? Why or why not?
The data is as follows:
WTI $ Year US Proven Reserves CPI CO2 Emissions NA Rotary Rig Count 22.93 1986 26889 333.1307 180.495 1115 18.65 1987 27256 337.9939 186.715 1115 17.13 1988 26825 351.6717 195.518 1131 18.02 1989 26501 368.0851 192.452 999 22.86 1990 26254 387.234 190.438 1147 25.23 1991 24682 409.1185 183.577 984 18.79 1992 23745 419.7568 187.144 839 19.03 1993 22957 433.4347 175.569 1003 15.03 1994 22457 444.3769 194.689 1088 18.04 1995 22351 456.8389 184.345 1080 18.86 1996 22017 469.3009 196.917 1052 25.13 1997 22546 483.5866 198.612 1223 16.72 1998 21034 491.1854 195.707 1474 12.52 1999 21765 499.3921 203.984 928 27.26 2000 22045 513.0699 200.467 1277 29.59 2001 22446 532.2188 217.885 1657 19.72 2002 22677 538.2979 208.637 1271 32.95 2003 21891 552.2796 212.136 1331 34.31 2004 21371 562.9179 219.276 1654 46.84 2005 21757 579.6353 219.183 1805 65.49 2006 20972 602.7356 219.854 2133 54.51 2007 21317 615.2462 217.096 2282 92.97 2008 19121 641.5805 211.774 2243 41.71 2009 20682 641.772 201.261 1931 78.33 2010 23267 658.6231 188.351 1725 89.17 2011 26544 669.3708 191.581 2276 100.27 2012 30529 688.9514 184.704 2580 94.76 2013 33371 699.9392 188.001 2259 94.62 2014 37002 710.9909 189.089 2033Explanation / Answer
Excel output for regression analysis of the data is given below.
Based on these I will answer the questions.
1. Is the model significant at a 5% level? Why or why not?
Answer :- Since the P-value for regression is 2.4E-12 which is very low, this model is significant.
2. Assess the quality of the model. Is it a strong model? Why or why not?
Answer :- The R-square value for this ,model is 0.9274. When the R-square value for a regression model is close to 1 it is a strong model. Since it is the same case here, this model should be strong.
3. Are all of the independent variables significant at a 5% level? Why or why not?
Answer :- Except the variable CO2 emissions which has a P-value of 0.7921 all of the variables have P-values less than 0.05. Therefore all of the independent variables except the CO2 emissions are significant at 5% significance level.
You can download the EXCEL file from the link given below.
https://www.dropbox.com/s/bw6q839wppg78yf/WTI.xlsx?dl=0
SUMMARY OUTPUT Regression Statistics Multiple R 0.9630 R Square 0.9274 Adjusted R Square 0.9117 Standard Error 8.7382 Observations 29 ANOVA df SS MS F Significance F Regression 5 22446.28 4489.26 58.79 2.4E-12 Residual 23 1756.18 76.36 Total 28 24202.45 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 22035.59 9375.43 2.35 0.0277 2641.03 41430.15 Year -11.28 4.80 -2.35 0.0277 -21.20 -1.35 US Proven Reserves 0.00 0.00 2.75 0.0115 0.00 0.00 CPI 0.92 0.36 2.59 0.0163 0.19 1.66 CO2 Emissions -0.06 0.24 -0.27 0.7921 -0.55 0.43 NA Rotary Rig Count 0.03 0.01 4.09 0.0004 0.01 0.05Related Questions
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