Minor fixes

README.md

@@ -45,7 +45,7 @@
 NannyML is an open-source python library that allows you to **estimate post-deployment model performance** (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and **regression**.
 
 The core contributors of NannyML have researched and developed multiple novel algorithms for estimating model performance: [confidence-based performance estimation (CBPE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#confidence-based-performance-estimation-cbpe) and [direct loss estimation (DLE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#direct-loss-estimation-dle).
-The nansters also invented a new approach to detect [multivariate data drift](https://nannyml.readthedocs.io/en/stable/how_it_works/data_reconstruction.html) using PCA-based data reconstruction.
+The nansters also invented a new approach to detect [multivariate data drift](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html#data-reconstruction-with-pca) using PCA-based data reconstruction.
 
 If you like what we are working on, be sure to become a Nanster yourself, join our [community slack](https://join.slack.com/t/nannymlbeta/shared_invite/zt-16fvpeddz-HAvTsjNEyC9CE6JXbiM7BQ) <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/slack.png" height='15'> and support us with a GitHub <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/github.png" height='15'> star ⭐.
 
@@ -98,9 +98,9 @@ NannyML can also **track the realised performance** of your machine learning mod
 
 ### 2. Data drift detection
 
-To detect **multivariate feature drift** NannyML uses [PCA-based data reconstruction](https://nannyml.readthedocs.io/en/main/how_it_works/data_reconstruction.html). Changes in the resulting reconstruction error are monitored over time and data drift alerts are logged when the reconstruction error in a certain period exceeds a threshold. This threshold is calculated based on the reconstruction error observed in the reference period.
+To detect **multivariate feature drift** NannyML uses [PCA-based data reconstruction](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html#data-reconstruction-with-pca). Changes in the resulting reconstruction error are monitored over time and data drift alerts are logged when the reconstruction error in a certain period exceeds a threshold. This threshold is calculated based on the reconstruction error observed in the reference period.
 
-<p><img src="https://raw.githubusercontent.com/NannyML/nannyml/main/docs/_static/butterfly-multivariate-drift.svg"></p>
+<p><img src="https://raw.githubusercontent.com/NannyML/nannyml/main/docs/_static/how-it-works/butterfly-multivariate-drift-pca.svg"></p>
 
 NannyML utilises statistical tests to detect **univariate feature drift**. We have just added a bunch of new univariate tests including Jensen-Shannon Distance and L-Infinity Distance, check out the [comprehensive list](https://nannyml.readthedocs.io/en/stable/how_it_works/univariate_drift_detection.html#methods-for-continuous-features). The results of these tests are tracked over time, properly corrected to counteract multiplicity and overlayed on the temporal feature distributions. (It is also possible to visualise the test-statistics over time, to get a notion of the drift magnitude.)
 

docs/how_it_works/estimation_of_standard_error.rst

@@ -205,7 +205,7 @@ Through a simple application of error propagation:
 which means that the standard error of the sum is the standard error of the mean multiplied by sample size.
 
 
-Stnadard Deviation
+Standard Deviation
 ------------------
 
 The standard error of the variance of a random variable is given by the following exact formula:

docs/how_it_works/multivariate_drift.rst

@@ -177,7 +177,7 @@ The classifier cross validation part uses the data created and consists of the f
 
 - Optionally, hyperparameter tuning is performed. The hyperparameters learnt during
   this step will be used in the model training steps below. If hyperparameter tuning
-  is not requested, user specified hyperpatameters can be used instead of the default LightGBM optioms.
+  is not requested, user specified hyperparameters can be used instead of the default LightGBM options.
 - Stratified split is used to split the data into validation folds
 - For each split NannyML trains an `LGBMClassifier` and saves its predicted
   scores in the validation fold.