Extensive Summary of Lecture 1 and 2 for Statistical Learning in Marketing (EBM214A05), including complete R introduction, guide and function cheat sheets, in-depth summary of lecture 2 and R-code interpretation. Check out the complete summary bundle for the best value.
Table of Contents
Week 1 (Lecture 1)........................................................................................... 10
Lecture 1 – Intro to R + Intermediate R ......................................................... 10
Introduction to R: Variables, Vectors, Matrices, Factors, Data Frames and
Lists............................................................................................................ 10
Variables .............................................................................................. 10
Vectors ................................................................................................. 10
Matrices ............................................................................................... 10
Factors ................................................................................................. 11
Data Frames (DF).................................................................................. 11
Lists ...................................................................................................... 12
Intermediate R: Conditionals, Control flow, Loops, Functions, Apply family,
Utilities. ..................................................................................................... 12
Conditionals and Control Flow ............................................................. 12
- Relational operators ................................................................... 12
- Logical operators ........................................................................ 13
- Conditional Statements .............................................................. 13
Loops.................................................................................................... 14
- While loops ................................................................................ 14
- For loops .................................................................................... 14
Functions.............................................................................................. 15
Apply family ......................................................................................... 16
- Lapply ......................................................................................... 16
- Sapply ......................................................................................... 16
- Vapply ........................................................................................ 17
Utilities ................................................................................................. 17
- Data utilities ............................................................................... 18
- Importing Data in R .................................................................... 19
Week 2 (Lecture 2)........................................................................................... 20
Lecture 2 – Reducing data complexity .......................................................... 20
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Concepts in this lecture: ............................................................................ 20
Principal Component Analysis .................................................................... 20
Sequence of action in PCA .................................................................... 20
Terminology ......................................................................................... 23
In Practice – How to in R: ..................................................................... 23
Exploratory Factor Analysis (EFA) .............................................................. 25
Sequence of action in EFA .................................................................... 25
Estimation process ............................................................................... 26
In Practice – How to in R: ..................................................................... 28
Multidimensional scaling (MDS) ................................................................ 32
In Practice – How to in R: ..................................................................... 33
Final Remarks: ........................................................................................... 34
Week 3 (Lecture 3)........................................................................................... 35
Lecture 3 – General linear model, part A: Introduction/ANOVA/ANCOVA .... 35
Assumptions to be satisfied for OLS: .................................................... 37
Goodness of fit (R2) .............................................................................. 37
Interpreting Adjusted-R2 ................................................................ 37
Other Goodness of Fit Indices .............................................................. 38
Statistical Inference.............................................................................. 38
Possible reasons for insignificance ....................................................... 39
In Practice (the case) – How to in R: ..................................................... 40
ANOVA....................................................................................................... 45
Full vs. Reduced model .............................................................................. 47
In Practice – How to in R (continued): .................................................. 47
ANCOVA..................................................................................................... 48
Homogeneity of regression slopes ....................................................... 49
In practice – How to in R (continued): .................................................. 51
Week 4 (Lecture 4)........................................................................................... 52
Lecture 4 – General linear model, part B: Multiple Regression ..................... 52
Multiple Linear Regression ........................................................................ 52
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Interpretation: variable transformations.............................................. 52
Judging the impact of variables ............................................................ 53
Standardized coefficients ..................................................................... 54
Multicollinearity ........................................................................................ 54
Indications of multicollinearity: How to test for it? .............................. 55
Variance Inflation Factors & Tolerance ................................................ 55
Solutions for Multicollinearity: ............................................................. 56
Dummy Variable Trap ................................................................................ 56
Moderation Effects .................................................................................... 58
Types of Moderation Effects ................................................................ 58
Types of Moderator Variables .............................................................. 58
Interpreting Moderation Effects........................................................... 59
Rescaling of Variables ................................................................................ 59
Is the presence of multicollinearity always a problem? ............................. 60
Good Practices ........................................................................................... 60
In Practice – How to in R: ..................................................................... 61
Week 5 (Lecture 5 and 6) ................................................................................. 66
Lecture 5 – Modern Time Series Analysis, part A .......................................... 66
2 Types of Time Series ............................................................................... 66
6 Methodological Steps ............................................................................. 67
In Practice – How to in R (pre- 6 Methodological Steps above): ........... 67
1. Granger causality ................................................................................... 69
How many lags to include?................................................................... 69
Back to Practice – How to in R (1. Granger Causality):.......................... 70
2. Unit Root and Cointegration Tests ......................................................... 71
Autoregressive process ........................................................................ 71
Role of (phy) .................................................................................. 72
Stationarity .......................................................................................... 73
Condition for Stationarity: ................................................................ 73
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Cointegration ....................................................................................... 74
Testing for Cointegration.................................................................. 75
Back to Practice – How to in R:............................................................. 76
Cointegration in Marketing .................................................................. 80
4 long-term scenarios ....................................................................... 80
VAR (Vector Autoregressive) Model .......................................................... 81
Lecture 6 – Modern Time Series Analysis, part B .......................................... 82
VAR ............................................................................................................ 82
3 Forms of VAR models ........................................................................ 82
Structural VAR (SVAR) ...................................................................... 82
Reduced-form VAR ........................................................................... 83
In Practice – How to in R ...................................................................... 83
Endogenous vs. Exogenous (1) ................................................................... 84
Types of Exogeneity ............................................................................. 84
Lag Selection (2)......................................................................................... 85
Lag Selection (continued) .......................................................................... 87
Symmetry vs. Asymmetries .................................................................. 87
Dealing with (non-)stationarity ............................................................ 87
Vector Error Correction (VEC) .................................................................... 88
Seasonality in VAR/VEC ........................................................................ 88
IRF.............................................................................................................. 90
Parameter numerosity ......................................................................... 90
Back to Practice – How to in R:............................................................. 91
Back to Practice – How to in R (IRF):..................................................... 93
FEVD: Forecast Error Variance Decomposition .......................................... 97
Week 6 (Lecture 7)........................................................................................... 99
Lecture 7 – Cluster Analysis .......................................................................... 99
Cluster Analysis: Introduction .................................................................... 99
Steps to Cluster Analysis .......................................................................... 100
Step 1: Define the Research Purpose ................................................. 100
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