Question

Discuss in detail the roles of the following various procedures used in regression:

1. Variable selection
2. Variable transformations
3. Test model assumptions
4. Diagnose model problems
5. Test the Hypothesis

Answer 1

1) variable selection:-

• This task of identifying the best subset of predictors to include in the model among all possible subsets of predictors.

•most models don't deal well with a large number of irrelevant variables. These variables will only introduce noise into your model, worse, cause you to over-fit.

•It's a good idea to exclude these variables from analysis.

•Regression analysis refers to a method of mathematically sorting out which variables may have an impact. The importance of regression analysis for a small business is that it helps determine which factors matter most, which it can ignore, and how those factors interact with each other.

• primarily focused on removing non-informative or redundant predictors from the model.

2) variable transformation

• regression model to the transformed rather than the original variables.

•Transformation of a variable can change its distribution from a skewed distribution to a normal distribution and gives better result of dataset.

•Data is transformed to make it better-organized.Transformed data may be easier for both humans and computers to use. Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible .

3)Test model assumptions:-

• There are four assumptions associated with regression model:

Linearity: The relationship between X and the variable Y is linear.

Homoscedasticity: The variance of residual is the same for any value of X.

Independence: Observations are independent of each other.

Additivity

Main role of model assumption is checking diagnostics.

Model Assumptions denotes the large collection of explicitly stated or implicit premises, conventions, choices and other specifications on which any Risk Model is based.

The suitability of those assumptionsis a major factor behind the Model Risk associated with a given model.

4)Diagnose model problem :-

Regression diagnostics are used to evaluate the model assumptions and investigate whether or not there are observations with a large, undue influence on the analysis.

5)Test the hypothesis :-

Hypothesis testing is used to assess the plausibility of sample data.

The test provides evidence concerning the plausibility of the hypothesis, given the data.

Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed.