Conditional Probability vs Normal

In summary, regression analysis allows for the identification of correlation between variables, but it does not determine causality. Therefore, the correct answer is b) identify correlation only.
  • #1
jacobson00
5
0
am a bit confused, if i want to find out for example the P(Having the disease among everyone) , using conditional, would it be total people Having the disease over total population?Prescreening Positive and Have the disease is 66
Prescreening Positive but does not the disease 150
prescreening Negative and Have the disease is 5
prescreening Negative and does not Have the disease is 555 so if using conditional probability, i want to find.P(Having the disease among everyone)

66 +5/ total population (776)

P(Prescreening Positive for everyone)

66+150/776

Am i understanding it correctly?
 
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  • #2
Hi jacobson00,

Welcome to MHB! :)

I would suggest using parentheses to be more clear, but yes I agree with your approach. There are two ways of having the disease with the way the data is arranged - either you have it and you are prescreened positive or you have it and are prescreened negative. Divide by total number of people. There isn't any overlap between the groups so it seems pretty straightforward and I agree with your answer.
 
  • #3
thank you. I think i was thrown off by the term "Conditional". If looks like plain probabilities.
 
  • #4
jacobson00 said:
thank you. I think i was thrown off by the term "Conditional". If looks like plain probabilities.

Well they are conditional probabilities but you are given a lot of information so you don't have to think of them that way.

You could write each of these numbers as a conditional probability and calculate the answer through the law of total probability, but that's not necessary here.

\(\displaystyle P[+|S^{+}], P[+|S^{-}], P[-|S^{+}], P[-|S^{-}]\)
 
  • #5
Phewww! i don't think i am there yet. baby steps. Thank you
 
  • #6
jacobson00 said:
Phewww! i don't think i am there yet. baby steps. Thank you

Ha, it looks scarier than it is. :) Glad you found us. Please keep posting your questions so we can help when you are stuck.
 
  • #7
Regression alone enables you to do what? a) infer causality only, b) identify correlation only c) both?
 

Related to Conditional Probability vs Normal

What is conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It is calculated by dividing the probability of the joint events by the probability of the initial event.

What is normal distribution?

Normal distribution, also known as Gaussian distribution, is a probability distribution that is symmetrical and bell-shaped. It is commonly used to model real-life phenomena, such as height or weight of a population, and is characterized by its mean and standard deviation.

What is the difference between conditional probability and normal distribution?

The main difference is that conditional probability focuses on the likelihood of an event given another event has occurred, while normal distribution describes the probability of a random variable taking on a certain value. Additionally, conditional probability can be calculated for any type of events, while normal distribution is only applicable for continuous variables.

How is conditional probability related to normal distribution?

Conditional probability can be calculated using normal distribution when the events involved are continuous and normally distributed. In this case, the mean and standard deviation of the normal distribution can be used to calculate the probabilities of the joint events and the initial event.

Why is understanding conditional probability and normal distribution important in science?

Conditional probability and normal distribution are important concepts in science because they allow us to make predictions and draw conclusions from data. They are used in various fields such as statistics, biology, and psychology to analyze and interpret experimental results and make informed decisions based on probabilities.

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