Question relate to multi variable.

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In summary, the conversation discusses a function P defined at each point in spherical coordinates, with Pmax being the maximum value of P in a closed sphere S. The normalized value of P is represented by F, with Fmax equaling 1. The question is whether Pmax is a constant and can be moved inside the integral. The conclusion is that Pmax is indeed a constant and can be moved inside the integral.
  • #1
yungman
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Let [itex]\;P(R,\theta,\phi)\; [/itex] be function at each point defined by [itex] R,\theta,\phi[/itex] in spherical coordinates.

Let [itex]\;P_{max} \;[/itex] be the maximum value of [itex]\;P(R,\theta,\phi)\; [/itex] in the closed sphere S.

[tex]\hbox {Let }\;F(R,\theta,\phi)=\frac {P(R,\theta,\phi)}{P_{max}}[/tex]

Which is the normalized value of [itex]\;P(R,\theta,\phi)\; \hbox {where } \; F_{max} = 1[/itex].

My question is whether:

[tex] \frac {P(R,\theta,\phi)}{\oint_S P(R,\theta,\phi) d\;S}\; =\; \frac {F(R,\theta,\phi)}{\oint_S F(R,\theta,\phi) d\;S}[/tex]

I thought

[tex] \frac {\left [\frac {P(R,\theta,\phi)}{P_{max}}\right ]} {\left [\frac {\oint_S P(R,\theta,\phi) d\;S}{P_{max}}\right ]} \;\hbox { not equal to } \; \frac {F(R,\theta,\phi)}{\oint_S F(R,\theta,\phi) d\;S}[/tex]

Unless we can consider [itex] \;P_{max}\;[/itex] is a constant and can be moved inside the integration. So the question is whether [itex] \;P_{max}\;[/itex] is a constant? I am not sure.

Please help.

Thanks

Alan
 
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  • #2
Yes, once P has been defined, Pmax is a specific number, a constant.
 
  • #3
HallsofIvy said:
Yes, once P has been defined, Pmax is a specific number, a constant.

Thanks so much for answering.

So I can move [itex]\;P_{max}\;[/itex] inside the integral and:


[tex] \frac {P(R,\theta,\phi)}{\oint_S P(R,\theta,\phi) d\;S}\; =\; \frac {F(R,\theta,\phi)}{\oint_S F(R,\theta,\phi) d\;S}[/tex]

Thanks

Alan
 

FAQ: Question relate to multi variable.

What is multi-variable analysis and why is it important in scientific research?

Multi-variable analysis is a statistical method that examines the relationship between multiple variables in a dataset. This is important in scientific research because it allows researchers to identify potential causal relationships and make more accurate predictions about the outcome of a study.

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The selection of a multi-variable analysis technique depends on the research question, the type of data collected, and the assumptions of the analysis. Some common techniques include regression analysis, ANOVA, and factor analysis. It is important to consult with a statistician to determine the most appropriate technique for a specific study.

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5. What are some potential applications of multi-variable analysis in scientific research?

Multi-variable analysis can be used in various fields of scientific research, including psychology, biology, economics, and social sciences. Some potential applications include identifying risk factors for disease, understanding the impact of different variables on human behavior, and predicting outcomes based on multiple factors.

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