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- Create a variable with name “model1” that stores the estimateof the linear model shown belowUNRATE_PCH = b_0 + b_1*DFII10_PCH + b_2 * CPILFESL_PCH + b_3 *XTEITT01CNM156S_PCH+ b_4* DCOILWTICO_PCH + b_5 * PCOPPUSDM_PCH + b_6 * PCE_PCH+ b_7 * WPU101_PCH + b_8 * GPDIC1_PCH + b_9 * RRVRUSQ156N_PCHPaste you model’s summary below:Hint: use lm and summary- List all the estimate parameters from step 3 that arestatistically significant for all α ≤ 0.05
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THIS IS HOW THE SAMPLE DATA LOOKSLIKE
- This Is How Model1 Looks Like By The Following Code 1 (80.89 KiB) Viewed 21 times
- Create model2 which is a refinement of model1 by removing allregressors that are statistically insignificant witha p < 0.55. Paste you model’s summary below:- Calculate prediction accuracy and error rates of model2.- Create model3 which is a refinement of model2. A requirement formodel3 it must only have three regressors.How you pick the three regressor is up to you, but explain why youpick these three. Paste the summary of model3below.- Create model4 that uses a manual sampling technique with atraining set of 60% of the data and a testing setof 40%. Paste the summary of the model below.- Use model4 to predict the values on the 40% testing set.Store the results in the distPred variable and pastebeginning of variable data below. Hint use head() for this.- Using model4 calculate prediction accuracy and error rates thenuse ggplot that shows actual vs Predictedvalues. Paste your plot below.- Run a k-fold cross validation with k=10. Paste the print ofthe model below.
DATE 2003-04-01 2003-07-01 2003-10-01 2004-01-01 2004-04-01 2004-07-01 2004-10-01 2005-01-01 2005-04-01 2005-07-01 2005-10-01 2006-01-01 2006-04-01 2006-07-01 2006-10-01 2007-01-01 2007-04-01 2007-07-01 2007-10-01 2008-01-01 2008-04-01 2008-07-01 2008-10-01 2009-01-01 2009-04-01 2009-07-01 2009-10-01 2010-01-01 2010-04-01 2010-07-01 2010-10-01 2011-01-01 2011-04-01 2011-07-01 0044 10.04 UNRATE_PCH 4.5 0.0 -4.9 -2.3 -1.8 -3.0 0.0 -2.5 -3.8 -2.6 0.0 -4.7 -2.1 0.0 -4.3 1.5 0.0 3.7 2.9 4.2 6.7 12.5 14.4 20.4 12.5 3.6 3.1 -1.0 -2.0 -1.7 0.4 -4.9 0.4 -0.7 d DFII10_PCH -6.8 13.7 -8.8 -15.8 20.9 -7.4 -10.7 1.6 -2.3 8.6 12.2 2.2 17.9 -3.9 -2.1 0.1 5.1 0.4 -21.3 -31.4 11.9 14.5 52.4 -30.8 -4.0 1.7 -21.2 4.5 -5.7 -22.1 -28.6 43.0 -26.1 -66.3 04.0 CPILFESL_PCH 0.2 0.4 0.3 0.5 0.6 0.4 0.6 0.6 0.5 0.3 0.7 0.6 0.8 0.7 0.5 0.6 0.5 0.5 0.7 0.7 0.4 0.6 0.2 0.4 0.5 0.3 0.5 0.0 0.1 0.2 0.3 0.5 0.5 0.7 or EITT01CNM156S_PDCOILWTICO_PCHPCOPPUSDM_PCH -14.9 4.3 3.1 13.4 8.4 14.5 10.0 3.3 6.4 19.2 -5.2 6.9 -1.3 2.4 -10.2 6.3 5.2 2.9 3.3 1.1 -0.6 -4.2 5.8 4.9 -0.4 -0.1 6.8 0.5 -4.4 -5.0 1.7 -0.6 2.8 11.3 3.4 -18.7 -4.3 1.4 -0.4 0.6 0.9 -44 -2.4 4.5 -1.3 AC 5.4 11.4 0.0 -15.0 -3.1 11.9 15.8 20.4 8.0 26.5 -4.4 -50.4 -26.5 38.2 14.3 11.5 3.7 -1.3 -2.3 12.0 10.5 8.5 -12.3 -1.3 6.8 17.5 32.2 2.1 2.6 8.4 5.5 3.8 10.7 14.8 15.0 46.1 6.2 -8.0 -15.7 28.4 0.9 -6.7 8.5 8.1 -9.3 -49.0 -11.7 35.6 25.3 13.4 8.7 -2.9 3.4 18.9 11.6 -5.0 -1.9 46 4 PCE PCH 1.1 2.1 11 1.7 1.3 1.6 2.0 1.2 1.7 2.1 1.1 1.6 1.3 1.3 0.8 1.5 1.0 1.1 1.3 0.6 1.2 0.3 -2.4 -0.8 0.0 1.4 0.6 0.8 0.9 0.9 1.3 1.3 1.2 0.9 05 WPU101_PCH GPDIC1_PCH RRVRUSQ156N_PCH 0.1 0.7 2.1 0.7 3.6 4.0 3.4 0.2 13.6 8.6 10.6 5.4 -0.8 -5.2 -3.6 5.0 2.8 4.4 5.1 -1.4 3.1 4.9 -2.5 -1.4 8.9 24.0 7.0 -25.6 -13.1 -8.5 9.8 4.3 9.2 10.0 -4.3 0.4 10.1 3.9 0.0 4.0 1.6 2.1 2.8 -1.3 1.3 3.3 1.5 -0.6 -0.4 -1.9 -0.7 1.1 -1.0 -1.2 -2.6 -1.8 -2.0 -9.6 -11.6 -5.8 -0.2 9.2 2.3 5.2 4.5 -0.3 -1.9 4.1 CO 0.3 77 3.1 3.0 2.0 -1.9 -1.0 -1.0 1.0 -3.0 1.0 -3.0 -1.0 1.1 3.1 -1.0 3.1 -5.9 3.2 -2.0 5.2 -1.0 -1.0 2.0 0.0 5.0 4.7 -3.6 -0.9 0.0 -2.8 -8.7 3.2 -5.2 6.5 11