Anomaly detection is the process of identifying data points that deviate from the norm. This can be useful for identifying fraud, detecting intrusions, and preventing equipment failures. There are many different statistical approaches to anomaly detection, each with its own strengths and weaknesses.

In this talk, we will discuss the following statistical approaches to anomaly detection:

Z-score: The z-score is a simple statistical technique that compares a data point to the mean and standard deviation of the dataset. A data point is considered an anomaly if its z-score is greater than a certain threshold.
Interquartile range (IQR): The IQR is a more robust statistical technique than the z-score. It is not as sensitive to outliers as the z-score, and it can be used to detect anomalies in non-normally distributed data.
Boxplot: A boxplot is a graphical representation of the distribution of data. It can be used to identify outliers and to visualize the spread of data.
Histogram: A histogram is a graphical representation of the frequency of data points in a dataset. It can be used to identify outliers and to visualize the distribution of data.
We will discuss the advantages and disadvantages of each statistical approach, and we will provide some guidelines for choosing the right approach for a particular task.

We will also discuss the challenges of anomaly detection, such as the problem of false positives and the problem of detecting anomalies in streaming data.

This talk will be of interest to anyone who is involved in data mining or machine learning. It will provide you with the knowledge you need to choose the right statistical approach to anomaly detection for your data mining projects.