Model assessment exercises

About the course
What are we doing?
Basic Python programming review
Polars and split-apply-combine
Data display
Probability: The foundation for generative modeling
- 20 Probability: definitions and interpretations
- 21 Probability distributions
Sampling out of probability distributions
Plug-in estimates and confidence intervals
Nonparametric inference with hacker stats
- 28 Performing bootstrap calculations
- 29 Pairs bootstrap and correlation
Null hypothesis significance testing
Generative modeling
- 33 Statistical modeling
- 34 Building a generative model
Parameter estimation
Variate-covariate models
Model assessment
Statistical watchouts
Basic Python exercises
Polars and split-apply-combine exercises
Data visualization exercises
Probability exercises
Nonparametric hacker stats exercises
- 71 Confidence intervals for microtubule catastrophe
- 72 Wrangling and hacker stats with Darwin’s finches
NHST exercises
- 73 NHST with Darwin’s finches
- 74 Dance of the p-values
Parameter estimation exercises I
Parameter estimation exercises II
- 78 Determining dissociation constants
- 79 MLE for a ligand-gated ion channel
Model assessment exercises
- 80 Analysis of microtubule catastrophe
- 81 Data transformations and parameter estimation

Model assessment exercises

Now, we will perform come parameter estimates, and then perform model assessment to ensure that or models are commensurate with the observed data.

79 MLE for a ligand-gated ion channel

80 Analysis of microtubule catastrophe

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