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A beta error is another term for a type II error, an instance of accepting the null hypothesis when the null hypothesis is false.

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Q: What is a beta error?
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What is the major difference between a mathematical model and an econometric model?

Mathematical model is exact in nature.it has Beta zero and Beta one and no stochastic or disturbance variables. Econometric model represents omitted variable, error in measurement and stochastic variables.


Relationship between type 1 error and type 2 error?

In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.


True or False For a given level of significance if the sample size is increased the probability of comitting a Type II error will increase?

The statement is false. For a fixed alpha, an increase in the sample size will cause a decrease in beta (but an increase in the power).


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How do you calculate beta in mutual funds?

Check out these websites: http://faculty.babson.edu/academic/Beta/CalculateBeta.htm http://www.money-zine.com/Investing/Stocks/Stock-Beta-and-Volatility/

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What is the major difference between a mathematical model and an econometric model?

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Relationship between type 1 error and type 2 error?

In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.


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