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I update my study notes from time to time.
🪴 Aster's study notes
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- Alternative hypotheses
- Analysis of Variance (ANOVA)
- Bayes classifier
- Bayes' theorem
- Bayesian statistics
- Bias variance trade off
- Binomial distribution
- Bootstrapping
- Boxplots
- Calculate p-value
- Central limit theorem
- Classification
- Classification error metrics
- Conditional probabilities
- Confidence intervals
- Covariance
- Data science
- Data visualization
- Decision tree and random forest
- Design matrix
- Expected value
- Exponential distribution
- False discovery rates FDR
- Fitting a curve to data
- Fitting a line to data in linear regression
- Histogram
- Hypothesis testing and the null hypothesis
- K nearest neighbours (KNN) theory
- K nearest neighbours (KNN) with python
- Least squares
- Linear discriminant analysis (LDA)
- Linear regression in R
- Linear regresssion
- Linear regresssion in python
- Log (logarithm)
- Logistic regression
- Logistic regression coefficients
- Logistic regression in python
- Machine learning
- Mathematical model
- Mathematics
- Maximum likelihood
- Mean, variance and standard deviation
- Multiple regression
- Multiple regression in R
- Normal distribution
- P-hacking and false positives
- P-value
- Pearson Correlation
- Population and estimated parameters
- Power analysis
- Prior probability and posterior probability
- Probability
- Probability distribution
- Probability vs likelihood
- Python
- Quantile normalization
- Quantile-quantile plot (QQ plot)
- Quantiles and percentiles
- R
- R-squared
- Random sampling
- Regression error metrics
- Resampling
- Sample size and effective sample size
- Sampling a distribution
- Standard error
- Statistical power
- Statistical test
- Stats
- Supervised learning
- t-test
- Technical replicates vs Biological replicates
- Underestimate the variance
- Weighted least squares
- Z-value
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