Probability density function

In summary: I did the calculations I got .204. Without the negative sign, since z is squared, i got .78when I did the calculations I got .204. Without the negative sign, since z is squared, i got .78
  • #1
g.lemaitre
267
2

Homework Statement



I want to calculate the probability of a random sample falling between 2 z scores using the way real mathematicians do it not the fake way by resorting to tables. Ok, so the book outlines the equation below but says that it requires calculus which is beyond the scope of this course. I know calculus so let's do it.

Homework Equations



Screenshot2012-10-06at83131PM.png


z = (x - μ)/σ

The Attempt at a Solution



Let's say x = 21, μ = 14 and σ = 6

thus

(21 - 14)/6 = 1.16, according to the tables the probability of a random sample falling between the z scores 0 and 1.16 is .3770

Now, let's use the calc equation:

[tex] \frac{\exp\frac{1}{2}(\frac{21-14}{6})^2}{6 \sqrt{2\pi}} [/tex]

= .0336, not .3770, so I'm doing something wrong
 
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  • #2
hold on, I'm forgetting what tags are used to enclose latex.
 
  • #3
well [tex] is the way to enclose latex, it's not working on my computer.
 
  • #4
g.lemaitre said:

Homework Statement



I want to calculate the probability of a random sample falling between 2 z scores using the way real mathematicians do it not the fake way by resorting to tables. Ok, so the book outlines the equation below but says that it requires calculus which is beyond the scope of this course. I know calculus so let's do it.

Homework Equations



Screenshot2012-10-06at83131PM.png


z = (x - μ)/σ

The Attempt at a Solution



Let's say x = 21, μ = 14 and σ = 6

thus

(21 - 14)/6 = 1.16, according to the tables the probability of a random sample falling between the z scores 0 and 1.16 is .3770

Now, let's use the calc equation:

[tex] \frac{\exp\frac{1}{2}(\frac{21-14}{6})^2}{6 \sqrt{2\pi} [/tex]

= .0336, not .3770, so I'm doing something wrong

You need a second bracket } after the 2\pi, because without it you have \frac{}{ (no closure).

So you have written
[tex] \frac{\exp\frac{1}{2}(\frac{21-14}{6})^2}{6 \sqrt{2\pi}} [/tex]
and called this the calc equation. Well, it is nonsense! The actual probability that an N(0,1) random variable falls between 0 and 1.16 is
[tex]\int_0^{1.16} \frac{1}{\sqrt{2\pi}} \exp(-z^2/2)\, dz,[/tex] which is a non-elementary integral; that is, there is no finite formula to express an integral of the form
[tex] G(a) = \int_0^a \frac{1}{\sqrt{2\pi}} \exp(-z^2/2)\, dz,[/tex] for general values of 'a' (although exact values are available for some special values of 'a'). This is a *theorem*: it is impossible to express G(a) in finitely many elementary terms. It not that nobody has been smart enough to figure out a formula; it is a rigorously-proven fact that it is impossible to write such a formula. Basically, even if you write down a complicated formula taking 1 million pages to write out, it still won't represent G(a)! Not even 1 billion pages are enough. So, when we want to compute G(a) we must resort to the use of tables or numerical integration methods or approximate formulas. Don't scorn tables---they (or their modern equivalents) are necessary.

Anyway, using tables (or Maple, which I prefer) the answer for that integral is 0.3769755969, approximately.

RGV
 
  • #5
when I did the calculations I got .204. Without the negative sign, since z is squared, i got .78

Screenshot2012-10-06at115836PM.png
 
  • #6
g.lemaitre said:
when I did the calculations I got .204. Without the negative sign, since z is squared, i got .78

Screenshot2012-10-06at115836PM.png

You are NOT computing the integral, which is what needs to be done. Of course we can calculate f(z) for any z, but that is not the point. We need to calculate the INTEGRAL of f(z) for z from 0 to 1.16, and that is a much different problem. It needs tables or equivalent.

RGV
 

Related to Probability density function

1. What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the distribution of a continuous random variable. It shows the relative likelihood of different outcomes occurring within a given range of values.

2. How is a PDF different from a cumulative distribution function (CDF)?

A PDF represents the probability of obtaining a specific value of a continuous variable, while a CDF represents the probability of obtaining a value less than or equal to a specific value.

3. What is the area under a PDF curve equal to?

The area under a PDF curve is equal to 1, as the total probability of all possible outcomes must equal 1.

4. How is a PDF used in statistics and data analysis?

A PDF is used to calculate the probability of obtaining a certain value or range of values for a continuous random variable. It is also used to compare and analyze the distribution of data.

5. Can a PDF be used for discrete random variables?

No, a PDF is only applicable for continuous random variables. Discrete random variables have a discrete probability distribution, which is described by a probability mass function (PMF).

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