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Silas Bergen

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DSCI 415 1.2 - linear transformations
STAT 460: 8.2 - interval estimators
STAT 460 - 8.1: bias variance and MSE
STAT 460: 7.7 - asymptotic sampling distributions and the CLT
STAT 460: 7.6 - sampling from normal: the F distribution
STAT 460: 7.5 - sampling from normal: the t distribution
STAT 460: 8.3 - Consistency
STAT 460: 7.4 - sampling from normal: mean and variance
STAT 460: 7.3 - joint distribution of order statistics
7.2- intro to order statistics
7.1 - intro to sampling distributions
STAT 450 - 6.4: bivariate transformations (Jacobian method)
STAT 450 - 6.3: The MGF method
STAT 450 - 6.2: The pdf method for monotone transformations
DSCI 415 - 4.3: DBSCAN
STAT 450 - 6.1: The method of distribution functions (CDF method)
DSCI 415 - 4.2: partitioning clustering methods
STAT 450 - 5.7: simulating jointly continuous random variables
STAT 450 - 5.8: Multinomial and bivariate normal
DSCI 415 - 4.1: Hierarchical clustering
STAT 450 - 5.6: Variance of linear combinations and conditional expectation
DSCI 415 - 3.3: PCA implementation
5.4 - independence vs dependence
STAT 450 - 5.5: Expectation of functions
PS 5.2 KEY
PS 5.1 KEY
PS 5.3
STAT 450 - 5.3: continuous marginal and conditional distributions
DSCI 415 - 3.2: PCA concepts
DSCI415 - oral exam 1 sample
DSCI 415 - 3.1: SVD
PS 5.2
STAT 450 - 5.2: bivariate continuous
PS 5.1
STAT 450 - 5.1: bivariate discrete
MyDoc
DSCI 415 - Activity 2.2 KEY
STAT 450 - Midterm coding portion Fall 2025
STAT 450 - coding exam instructions
STAT 450 - coding midterm sample
STAT 450 - 4.4: gamma distribution
STAT 450 - PS 3.8 key
DSCI 415 2_2
PS 4.7 - parametric grid simulations
STAT 450 - 4.7: simulating across a parameter grid
STAT 450 - 4.6: beta distribution
STAT 450 - 4.5: normal distribution
STAT 450: 4.7 KEY
DSCI 415 1.1 - vector definitions, norms, and distances