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Statistical Modeling and Computing Final Exam Study Guide
May 1, 2023
The Final Exam is scheduled for May 9 in PH111 BUS starting at 6:00 pm. The Exam is closed book, closed notes, and will consist of
multiple choice, short answers, and True/False questions. Simple calculations will be required, but a calculator is not necessary. The
only material you need on your desk is a pen/pencil/eraser. In addition, you may bring a single 8.5 by 11-inch sheet of paper where
you can choose to write helpful information to you on both sides.
I will design the Exam so that you should complete it within 2 hours.
Please sit one seat apart as you enter the room. There is enough room to accommodate all students.
General Guidelines:
 There will be ~15 questions (true/false, short answer, multiple choice). The types of questions to be asked on the Final
Exam will be similar to the questions asked Midterm Exam.
 R coding questions will not be asked on this Exam. You should prepare to interpret and answer questions using R output.
 Please be sure to visit the Restroom prior to the start of the Final Exam.
I will be using ChatGPT as a source for Final Questions. ChatGPT can also serve as your source for Practice Questions. Final Exam
questions will be generated from the material covered during my lectures (both live and asynchronous).
The material reviewed during lectures is listed by topic in the Required Folders in Canvas.
I will have Support Hours in Hill Center Office 465 on May 2 and May 9 between 12 noon - 2 pm.
Section 01 TA Support Hour on May 5 9:30-10:30 am (Zebang Li) See Canvas Announcements for Zoom link.
Section 02 TA Support Hour on May 5 2:30-3:30 pm (Ye Tian) See Canvas Announcements for Zoom link.
Since the TAs are busy with their own end-of-semester activities, please email them to confirm their office hours.
I will be using ChatGPT as a source for Final Questions. ChatGPT can also serve as your source for Practice Questions. Questions will
focus on the material covered during the Lectures (both live and asynchronous).
Order of Required Documents to Review for Final Exam
Topic
PCA PCR PLS
Missing Data
Document Sequence
1
2
3
4
5
6
7
8
9
10
11
12
13
1
2
3
4
5
6
7
8
Document_Name
TheCurseofDimensionality
ISLR_Chapter_10_PCA_slides.pdf
PrincipalComponentAnalysis_PCA101_UsingR
ExplainedvarianceinPCA.pdf
PCAfatinmeat_txt.pdf, _out.pdf, .R
IntrotoPLS_sas.pdf
Lab_11_PCRandPLS_Regression.pdf
Lab11_PCR_PLS_txt.pdf, _out.pdf, .R
plsr_example1_txt.pdf, _out.pdf, .R
Logistic_PCA_txt.pdf, _out.pdf, .R
ComparingPLS_glmnet_RandomForest_Xgboost.pdf
SingularValueDecomposition.pdf
SVD_example_txt.pdf, .R
MissingData_Allison.pdf
RPubsMultipleImputationAnalysisforMissingData.pdf
ChatGPT_mice_example_txt.pdf, .R
ChatGPTmice_steps.docx
MultipleImputationbyChainedEquations.pdf
GalleryofMissingDataVisualisations.pdf
missing_visualization_txt.pdf, _out.pdf, .R
PredictiveMeanMatchingImputation.pdf
EM Algorithm
Resampling
Causal Inference
Multiple Testing
9
10
11
12
13
14
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
3
4
5
6
7
predictive_mean_matching.pdf
SimpleLinearRegression_PosteriorPredictiveDistribution.pdf
Little_MCARtest_description.pdf
LittleTestexample_txt.pdf, .R
Medidata_Poster.pdf
Article_ComparingMissingValueImputationMethods.pdf
Mixture_model_examples_Wikipedia.pdf
TheEMAlgorithm_AdvancedStatisticalComputing.pdf
ApreludetotheEMalgorithm.pdf
EMbyHand_Article.pdf
EMbyHand_txt.pdf, .R
IntroductiontoEM_ GaussianMixtureModels.pdf
EM_AlgorithmandBootstrap.pdf
EM_AlgorithmandBootstrap_txt.pdf, .R
ExpectationMaximizationAlgorithm_Wikipedia.pdf
Ch05_bootstrap_slides.pdf
bootstrap_example_txt.pdf, _out.pdf, .R
Ch13_ResamplingApproaches_slides.pdf
Simple_permutation_tests_inR.pdf
Permutation1_txt.pdf, _out.pdf, .R
Permutation2_txt.pdf, _out.pdf, .R
Permutation3_txt.pdf, _out.pdf, .R
CausalInference_Lecture.pdf
CausalInference_AnIntroduction.pdf
PropensityScoresLecture.pdf
rhc_txt.pdf, rhc.R
Balance_diagnostics_baseline_Austin.pdf
PropensityMatching_inR.pdf
PropensityScorePublication.pdf
Using_std_mean_differences_article.pdf
PropensityScores_AB_Testing.pdf
DID_Lecture_Material.pdf
DID_PrincetonProgram_txt.pdf, _out.pdf, .R
RDD_Lecture_Material.pdf
RTutorial_RDD_txt.pdf, _out.pdf, .R
Ch12_RDD_txt.pdf, _out.pdf, .R
Ch13_Multiple_Testing_slides.pdf
Bonferroni_CI_derivation.pdf
Ch13_ISLRv2_MultipleTestingLab_13_6.pdf
Ch13_multiple_lab_txt.pdf, _out.pdf, .R
Exercise13_7_txt.pdf, .R
Exercise13_8_txt.pdf, _out.pdf, .R
padjust_txt.pdf, padjust_out.pdf, padjust.R