Final Exam DPEE
Note:
· For demonstrating conceptual understanding, you are required to work on the model that is easier to handle or compute, not necessarily the more suitable (or more complicated) model for the dataset. Follow the question description.
· You dont need to check the assumption of a model unless the question asks for it. For example, if the question asks you to make prediction based on a model, you dont need to check the assumption for the model before making prediction.
· For any of the testing (hypothesis test) problem, define Ho/Ha, compute the test statistic, report the exact p value, and state the conclusion. The default alpha value is 5%, unless specify.
· Elaborate your reasoning clearly and show relevant plots, R results, and tables to support your opinion in each step and conclusion.
· Submit the Rmd file and the corresponding pdf file knitted from it, along with your answer, this format is similar to your homework.
· The data is real, just like the project you are working on. Hence it is possible that even after the remedial method has been done, the model is still not perfect. When this happen
s, evaluation will be based on the level you execute the methods covered in Stat512 to improve the model. Dont worry if your model is not perfect, try your best to demonstrate the skill set you learn in this class.
Study the data with a linear analysis and complete the problems. The data set, dataDPEE.csv has 3 continues predictors and two categorical predictors.
Problem 1. Consider only the first order model with X1, X2 and X3, perform the following hypothesis.
a. (10) whether X1 can be dropped from the full model.
> dpeemod <- lm(y ~ x1 + x2 + x3) > plot(dpee)
> summary(dpeemod)
Call:
lm(formula = y ~ x1 + x2 + x3)
Residuals:
Min 1Q Median 3Q Max
-15.948 -11.640 -1.480 6.402 31.650
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 90.79017 22.07408 4.113 0.00106 **
x1 -0.68731 0.47959 -1.433 0.17377
x2 -0.47047 0.24227 -1.942 0.07254 .
x3 -0.06845 0.46523 -0.147 0.88513
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 14.75 on 14 degrees of freedom
Multiple R-squared: 0.6257, Adjusted R-squared: 0.5455
F-statistic: 7.8 on 3 and 14 DF, p-value: 0.002652