Linear regression is used to predict the value of one variable from another variable. Since it...

Linear regression is used to predict the value of one variable from another variable. Since it is based on correlation, it cannot provide causation. In addition, the strength of the relationship between the two variables affects the ability to predict one variable from the other variable; that is, the stronger the relationship between the two variables, the better the ability to do prediction.

What is one instance where you think linear regression would be useful to you in your workplace or chosen major? Please describe including why and how it would be used.

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Answer :

Connection measures the quality of the direct connection between a couple of factors, though relapse communicates the relationship as a condition.
The objective of a connection investigation is to see whether two estimation factors co shift, and to evaluate the quality of the connection between the factors, while relapse communicates the relationship as a condition.
For instance, in understudies taking a Math's and English test, we could utilize connection to decide if understudies who are great at Math's will in general be great at English also, and relapse to decide if the imprints in English can be anticipated for given stamps in Math's.
2 In the model, in the event that we are attempting to decide if the imprints in English can be anticipated for given checks in Math's, at that point stamps in Math's is the autonomous variable and checks in English is a needy variable.' '
On the off chance that there are more than one autonomous variable, at that point a basic direct relapse demonstrate is unacceptable for the expectation model.In such cases, a Multiple Regression show is utilized.
A various direct relapse display demonstrates the connection between the needy variable and numerous (at least two) autonomous factors.

orchestra answered 1 year ago

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