Dr Nick

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About Dr Nick

Levels Tought:
Elementary,Middle School,High School,College,University,PHD

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Teaching Since: May 2017
Last Sign in: 246 Weeks Ago, 2 Days Ago
Questions Answered: 19234
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Education

  • MBA (IT), PHD
    Kaplan University
    Apr-2009 - Mar-2014

Experience

  • Professor
    University of Santo Tomas
    Aug-2006 - Present

Category > Statistics Posted 10 Nov 2017 My Price 15.00

the null hypothesis of obtaining the observed value or any other value wich brings more evidence to reject ##H_0##.

##\hatp## : the estimated value of the parameter ##p## based on your sample.

##\p_0## : the real value of ##p## under the null hypothesis.

p-value : formally, the probability, under the null hypothesis of obtaining the observed value or any other value wich brings more evidence to reject ##H_0##.

As an example let's say you want to test if a coin toss is actually fair or heads has a larger probability. In this case you would have:

##H_0##: ##p = p_0 = 0.5## vs ##H_1##: ##p > 0.5##, where p is the probability of the "heads" results.

Then you calculate your ##\hatp## wich in this case will be the proportion of heads in your sample. Let's say you got a 0.65 proportion in your sample.

Then your p-value will be the probability under ##H_0## (this means you calculate this probability assuming it's a fair coin) of having a proportion of 0.65 or more in your sample, because the larger the proportion, we get more evidence that "heads" has more probability.

Small p-values leads us to reject ##H_0##.

Answers

Dr Nick
(3)
Status NEW Posted 10 Nov 2017 11:11 AM My Price 15.00

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