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What is the least squares method?

12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
The least squares method is a procedure for fitting data to a mathematical function. It is commonly used in statistical analysis to estimate the parameters of a given function from a set of observed data points. The procedure consists of minimizing the deviation between the observed values and the predicted values by adjusting the parameters of the function.
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What is analysis of variance?

12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Analysis of variance is a statistical data analysis technique used to study the differences between two or more groups. It is often used to find out how much influence each variable has on a particular measured variable. Analysis of variance can also be used to evaluate the strength of the relationship between two or more variables.
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What is the standard error?

12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
The standard error is a mathematical quantity that describes the variance of a measured value of a sample. The standard error is often used to determine the accuracy of a sample. It indicates how much the measured values in a given sample deviate from the average value.
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What are Markov chains?

12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Markov chains are a mathematical approach used to model random behavior. They are often used to simulate random processes based not only on the current situation, but also on previous events. They use probability distributions to predict how likely certain events are. This technique is commonly used in fields such as natural sciences, finance, artificial intelligence and data analysis.
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What is autocorrelation analysis?

12/06/2022 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Autocorrelation analysis is a statistical technique used to examine dependencies in a data set. It measures the similarity of a variable to itself at different points in time. It can be used to examine the stationary and trend properties of a series and determine if it is self-correlated. Autocorrelation analysis is often used to predict future values of a behavior or event.
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