Summary Exam prep sheet: Statistics and Methodology
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Course
Statistics and Methodology (880259M6)
Institution
Tilburg University (UVT)
The file contains materials to prepare for the course exam of Statistics and Methodology (880259-M-6), the core course for the M.Sc. Data Science & Society. It includes theoretical concepts from all 10 lectures. Knowing and being able to reproduce the materials of this summary should allow for a su...
Sta$s$cs and Methodology concepts and theory
Exam prepara)on M.Sc. Data Science & Society (year 2023/24)
1. Basics
Wald test
Sampling distribution = the probability distribution of a statistic.
p-value = the probability of observing the given test statistic, or one more extreme, if the null
hypothesis were true.
Statistical modeling - method of knowledge generation through the application of the statistical
analysis to real-world data. Allows to control for confounding factors. As opposed to statistical
testing which uses experimental data with random assignment.
2. Design
Data science cycle: Define -> Collect -> Process -> Clean
EDA can be used:
• to generate hypotheses for confirmatory data analysis;
• to sanity-check hypotheses.
3a. Data imputation
P items (columns) -> 2P possible response patterns (with missing data and without it)
Covariance coverage = the proportion of cases/observations/rows available to estimate a given
pairwise relationship (e.g., a covariance between two variables)
Missing data mechanisms
• MCAR -> non-response is not dependent on the data observed
o P(R|Ymis, Yobs) = P(R)
• MAR -> non-response is dependent on the data observed (e.g., by a certain cohort)
o P(R|Ymis, Yobs) = P(R|Yobs)
• MNAR -> non-response is directly defined by the data observed
o P(R|Ymis, Yobs) ≠ P(R|Yobs)
• Indirect MNAR -> certain variable correlated with non-response is not in the dataset
Missing data treatments
• Listwise deletion
o Biased parameter estimates for MAR and MNAR
o Biased (downwards) SEs
• Pairwise deletion
o Biased parameter estimates for MAR and MNAR
o Biased (downwards) SEs
• Unconditional mean substitution
o Biased parameter estimates in all scenarios
o Weakens measures of linear association
o Biased (downwards) SEs
, • Deterministic regression imputation (conditional mean subs.)
o Biased parameter estimates in all scenarios
o Inflates measures of linear association
o Biased (downwards) SEs
• Averaging available items (person-mean imputation)
o Biased parameter estimates for MAR and MNAR
o Biased parameter estimates if items do not contribute equally to the aggregate
score
• Last Observation Carried Forward
o Weakens estimates of growth
• Stochastic regression imputation
o Adds a random residual error to the imputated values to eliminate parameter bias
• Yimp = Y^mis + ε
o Biased (downwards) SEs
• Multiple imputation
o Models random residual error AND uncertainty in the regr. coefficients used to
create imputations
• A different set of coeff.s is randomly sampled to create each of the M
imputation
• Yimp = Y^mis + ε -> where Y^mis = 𝛽0 + 𝛽1Xmis is new for each M
o Eliminates parameter bias and SE bias (= accurate type I error rate)
o Biased parameter estimates for MNAR
3b. Outliers
• Int. student. residuals: an observation Xn is an outlier if Tn > c
where
• Ext. student. residuals: an observation Xn is an outlier if T(n) > c
o where:
o deletion mean and deletion SD are used,
o and (n) includes all observations bar the observation n itself.
• MAD: same logic but use TMAD:
• Tukey's boxplot method:
o A value outside of the inner fence (c = 1.5) is a possible outlier.
o A value outside of the outer fence (c = 3) is a probable outlier.
• By breakdown points (lowest first):
o Mean (int. stud. res.)
o Deletion mean (ext. stud. res)
o Tukey's boxplot
o MAD
, • Robust Mahalanobis (MCD estimation):
o "Multivariate generalization of the ext. stud. res."
o Fraction of the sample used to define center of the data determines robustness
• Fraction ↑ -> identified outliers ↓
o Cut-off determined as the sq.root of some quartile of chisq distribution
• Cutoff ↑ -> identified outliers ↓
4. Simple linear regression
• The full population model
o 𝑌 = 𝛽0 + 𝛽1𝑋 + 𝜀
• The estimated, sample model
o
• The estimated best-fit line(s)
o
• The true best-fit line
o
Sum of the squared residuals (RSS)
where
Statistical inference is needed to compute the precision with which we’ve estimated the OLS
estimators
Confidence intervals:
where tcrit = z1–α/2 for CI1–α
• => i.e. CI95 for the slope (𝛽^1) suggests that we can be 95% confident that the true value of 𝛽1
is between [_ ; _]
• => if we repeat the analysis an infinite number of times, 95% of the CIs that we calculate will
surround the true value of 𝛽1
• => CIs give us a plausible range for the population value of 𝛽
5. Multiple linear regression
Model fit:
where:
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