Understanding the Power Function for Poisson Distribution

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In summary, statistics plays a crucial role in scientific research by allowing for data analysis, prediction, and drawing conclusions. There are various types of statistical analyses, each serving a specific purpose. To ensure accuracy, it is important to carefully select samples, use appropriate tests, and avoid bias. Common misconceptions about statistics include correlation implying causation and assuming a sample is representative of the entire population. Statistics can also be used to manipulate data, but it is important to use them ethically and transparently to ensure the validity of research findings.
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Artusartos
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In this link:

http://www.math.harvard.edu/~phorn/362/362assn3-solns.pdf

I do not understand how they got the power function for number 5...can anybody explain it to me please?

Thanks in advance
 
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You understand that Y ~ Poisson(12θ)? Given that, what are the probabilities for Y being 0, 1, 2?
 
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haruspex said:
You understand that Y ~ Poisson(12θ)? Given that, what are the probabilities for Y being 0, 1, 2?

Oh, ok thanks.
 

FAQ: Understanding the Power Function for Poisson Distribution

What is the importance of statistics in scientific research?

Statistics is crucial in scientific research as it allows researchers to analyze and interpret data, make accurate predictions, and draw meaningful conclusions. It also helps in identifying patterns and trends, which can lead to the development of new theories or the refinement of existing ones.

What are the different types of statistical analyses?

There are various types of statistical analyses, including descriptive statistics, inferential statistics, correlation analysis, regression analysis, and hypothesis testing. Each type serves a specific purpose and is used to answer different research questions.

How can I ensure the accuracy of my statistical analysis?

To ensure the accuracy of your statistical analysis, it is essential to carefully select a representative sample, use appropriate statistical tests, and properly interpret the results. It is also crucial to avoid bias and account for any potential confounding factors.

What are some common misconceptions about statistics?

Some common misconceptions about statistics include the belief that correlation implies causation, assuming that a sample is representative of the entire population, and mistaking statistical significance for practical significance. It is important to understand the limitations and assumptions of statistical analyses to avoid these misconceptions.

How can statistics be used to manipulate data?

Statistics can be used to manipulate data by selectively choosing which data to include or exclude, using inappropriate statistical methods, or misinterpreting results. It is crucial to use statistics ethically and transparently to avoid manipulation of data and ensure the validity of research findings.

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