There are quite a few answers you can give to this question, so be prepared to answer without much hesitation. Some of the examples you should give to your interviewer include the simplex algorithm, Markov process, and bayesian method.

When analyzing data, there are various statistical methods that can be applied depending on the nature of the data and the objectives of the analysis. Some common statistical methods used in data analytics include:

Descriptive Statistics: Descriptive statistics involve summarizing and describing the main features of a dataset. This includes measures such as mean, median, mode, standard deviation, variance, range, and percentiles.
Inferential Statistics: Inferential statistics are used to make inferences or predictions about a population based on a sample of data. This includes techniques such as hypothesis testing, confidence intervals, and regression analysis.
Correlation Analysis: Correlation analysis is used to examine the strength and direction of the relationship between two or more variables. Common measures of correlation include Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall tau coefficient.
Regression Analysis: Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. It helps to understand how changes in the independent variables affect the dependent variable.
Time Series Analysis: Time series analysis is used to analyze data collected over time to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, and autoregressive integrated moving average (ARIMA) models are commonly used in time series analysis.
Cluster Analysis: Cluster analysis is used to group similar observations or data points into clusters based on their characteristics. It helps to identify patterns and relationships in the data.
Classification and Prediction: Classification and prediction techniques are used to classify data into predefined categories or predict future outcomes based on historical data. Methods such as decision trees, logistic regression, and machine learning algorithms like random forests and support vector machines are commonly used for classification and prediction.
Anomaly Detection: Anomaly detection techniques are used to identify outliers or anomalies in the data that deviate from normal behavior. This includes methods such as statistical tests, machine learning algorithms, and time series analysis.
Factor Analysis: Factor analysis is used to identify underlying factors or latent variables that explain the correlations among observed variables. It helps to reduce the dimensionality of the data and identify the underlying structure.
Survival Analysis: Survival analysis is used to analyze time-to-event data, such as time until failure or time until an event occurs. It is commonly used in medical research, engineering, and social sciences.

These are just some of the statistical methods commonly used in data analytics. The choice of method depends on the specific objectives of the analysis, the type of data being analyzed, and the assumptions underlying the analysis.