Statistical Models: A Detailed Analysis of Statistical Methods

This report offers an overview of several statistical models. It begins by explaining the general concept of statistical models and their use in predicting outcomes based on historical data, differentiating between dependent and independent variables. The report then delves into specific models such as scorecard models, which use a scoring system to predict future outcomes based on data characteristics and statistical measures like K-means and R. Next, it explores decision trees, which graphically represent the results of predictive models, and random forests, which involve multiple regression trees and a voting process to determine the most representative model. Additionally, the report touches on the Standard Model (Basel 2 framework), NN networks, and the Rete Algorithm, illustrating different approaches to data analysis and model creation. This report is designed to provide a clear understanding of the different methods used in statistical analysis.

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Statistical models
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Statistical Models
Statistical models
This is the process of making predictions using historical data. There are two variables that has
to be present and these are independent and dependent variables. For instance, if one wants to
predict the revenue for Company A, he/she should obtain historical data for the variables that
affected revenue in the past. In that case, revenue becomes the dependent variable and other
variables such variable costs such as fixed costs and production efficiency becomes the
independent variables.
Score card models
This model makes prediction about the future by measuring characteristics in the model
quantitatively. Each characteristic is divided into different ranges and different scores are
assigned to each of the ranges. In order to assign accurate scores, statistical measures such as K-
means, clustering, data analysis using statistical software such as R and SAS are used. In the end,
the final score card model will contain all the information that regarding different ranges and
their corresponding partial scores.
Decision trees
This is a graphical representation of the results obtained after analyzing predictive models. Each
of the characteristic in the data acts like a node that attaches to the branch of a tree. The results of
the analysis are attached at as a leaf in the branch of the tree.
Random Forest
This model represents trees of egression models that have not been processed to determine the
correct values that should be assigned to them. A boot strap approach is used to sample the data

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randomly that is being used in the model. Once a the number of trees selected are large enough,
the most popular tree is voted for to be the best representative of the model.
Standard Model (Basel 2 frame work)
This model uses a standard format where variables are assigned values to be used in the actual
model
NN network
This model simply uses data architecture to come up with the most appropriate model for a given
set of data.
Rete Algorithim
This method requires one to match different algorithms. These are the used to anlayze the
performance of the systems