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Linear Regression,how Is Data Modeling Different From Database Design P-value Signify Statistical ?

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By Author: Suvardhan Raju
Total Articles: 4
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> What is Linear Regression?
It is the most commonly used method for predictive analytics. The Linear Regression method is used to describe relationship between a dependent variable and one or independent variable. The main task in the Linear Regression is the method of fitting a single line within a scatter plot. The Linear Regression consists of the following three methods:

Determining and analyzing the correlation and direction of the data
Deploying the estimation of the model
Ensuring the usefulness and validity of the model
It is extensively used in scenarios where the cause effect model comes into play.
For example you want to know the effect of a certain action in order to determine the various outcomes and extent of effect the cause has in determining the final outcome.

> How is Data modeling different from Database design?
Data Modeling: It can be considered as the first step towards the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. The process involves moving from the conceptual stage to the logical model to the physical schema. It involves the systematic method of applying the data modeling techniques.

Database Design: This is the process of designing the database. The database design creates an output which is a detailed data model of the database. Strictly speaking database design includes the detailed logical model of a database but it can also include physical design choices and storage parameters.

> What does P-value signify about the statistical data?
P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1.

P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.
P-value

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