- Why logistic regression is better than linear regression?
- Which of the following is a limitation of using regression?
- What are the advantages and disadvantages of linear regression?
- What are the factors that affect a linear regression model?
- What is the benefit of linear regression?
- When linear regression is not appropriate?
- Why do linear regression fail?
- What is the disadvantages of linear model?
- What are the advantages and disadvantages of linear model of communication?
- What are the assumptions of linear regression?
- Why would a linear model not be appropriate?

## Why logistic regression is better than linear regression?

Linear regression is used to predict the continuous dependent variable using a given set of independent variables.

Logistic Regression is used to predict the categorical dependent variable using a given set of independent variables.

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Logistic regression is used for solving Classification problems..

## Which of the following is a limitation of using regression?

One limitation to regression is that, due to latent variables, it is hard to know what variable should predict what. One of the limitations of regression is that it can be used only for linear relationships.

## What are the advantages and disadvantages of linear regression?

Linear regression is a linear method to model the relationship between your independent variables and your dependent variables. Advantages include how simple it is and ease with implementation and disadvantages include how is’ lack of practicality and how most problems in our real world aren’t “linear”.

## What are the factors that affect a linear regression model?

These design factors are: the range of values of the independent variable (X), the arrangement of X values within the range, the number of replicate observations (Y), and the variation among the Y values at each value of X.

## What is the benefit of linear regression?

The biggest advantage of linear regression models is linearity: It makes the estimation procedure simple and, most importantly, these linear equations have an easy to understand interpretation on a modular level (i.e. the weights).

## When linear regression is not appropriate?

This article explains why logistic regression performs better than linear regression for classification problems, and 2 reasons why linear regression is not suitable: the predicted value is continuous, not probabilistic. sensitive to imbalance data when using linear regression for classification.

## Why do linear regression fail?

This article explains why logistic regression performs better than linear regression for classification problems, and 2 reasons why linear regression is not suitable: the predicted value is continuous, not probabilistic. sensitive to imbalance data when using linear regression for classification.

## What is the disadvantages of linear model?

A major disadvantage of the linear model is that often this model can isolate people who should be involved from the line of communication. As a result they may miss out on vital information and the opportunity to contribute ideas. … This is an example of a time where linear communication would not be as successful.

## What are the advantages and disadvantages of linear model of communication?

A linear model communication is one-way talking process An advantage of linear model communication is that the message of the sender is clear and there is no confusion . It reaches to the audience straightforward. But the disadvantage is that there is no feedback of the message by the receiver.

## What are the assumptions of linear regression?

There are four assumptions associated with a linear regression model:Linearity: The relationship between X and the mean of Y is linear.Homoscedasticity: The variance of residual is the same for any value of X.Independence: Observations are independent of each other.More items…

## Why would a linear model not be appropriate?

To determine whether a linear model is appropriate, we examine the residual plot. It is a good idea to look at both a histogram of the residuals and a scatterplot of the residuals versus the predicted values. … If we see a curved relationship in the residual plot, the linear model is not appropriate.