Simple question regarding linear regression model poisson

In summary, $E(Y|X=x) = Var(Y|X=x) = x'\beta$, but this does not justify a linear regression model for $y = x'\beta + e$.
  • #1
Usagi
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The question:

Suppose $Y$ is discrete and only takes on non-negative integers and that the conditional distribution of $Y$ given $X=x$ is Poisson, that is, $$P(Y=y|X=x) = \frac{\exp(-x'\beta) (x'\beta)^y}{y!}$$ where $y = 0, 1, 2, \cdots$. First compute $E(Y|X=x)$ and $Var(Y|X=x)$, does this justify a linear regression model of the form $y = x'\beta + e$?

My attempt:

I have calculated $E(Y|X=x) = Var(Y|X=x) = x'\beta$ by the properties of a Poisson distribution. I am unsure how to answer the last part of the question related to the linear regression model. Any help would be appreciated.
 
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  • #2
A:No, the linear regression model isn't justified. The linear regression model assumes that $Y$ follows a normal distribution. Since $Y$ is discrete, the linear regression model is not an appropriate model.
 

1. What is a linear regression model?

A linear regression model is a statistical method used to analyze the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables and uses a straight line to represent the relationship in a mathematical equation.

2. How is a linear regression model different from a Poisson regression model?

A linear regression model is used for continuous dependent variables, while a Poisson regression model is used for count data or discrete dependent variables. Additionally, a linear regression model assumes a normally distributed error term, while a Poisson regression model assumes a Poisson distribution for the error term.

3. What is the purpose of using a Poisson regression model in linear regression?

A Poisson regression model is used when the dependent variable is a count or discrete variable, such as the number of occurrences of an event. It takes into account the non-normal distribution of the data and provides more accurate predictions for count data.

4. How do you interpret the coefficients in a linear regression model with a Poisson distribution?

The coefficients in a linear regression model with a Poisson distribution represent the expected change in the log of the dependent variable for a one-unit increase in the corresponding independent variable. This is because the dependent variable is transformed using the natural logarithm in order to follow a Poisson distribution.

5. Can a linear regression model with a Poisson distribution be used for prediction?

Yes, a linear regression model with a Poisson distribution can be used for prediction. However, the model may not perform well if the data is not normally distributed or if there are outliers in the data. It is important to evaluate the assumptions of the model before using it for prediction.

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