Probability and gauss integral

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SUMMARY

The discussion focuses on calculating probabilities and evaluating integrals related to the standard normal distribution using the Z value table and power series expansion. Key calculations include finding areas under the curve for specific Z values, such as P(0 <= Z <= 2.07) resulting in 0.4808, and evaluating integrals like ∫ from 0 to 1.24 of e^(-x^2/2) yielding approximately 0.6807. The distinction between Z < x and Z <= x is clarified, confirming that both yield the same probability in continuous distributions. The integral evaluation methods discussed include using polar coordinates and rearranging terms to simplify calculations.

PREREQUISITES
  • Understanding of standard normal distribution and Z values
  • Familiarity with probability concepts and area under the curve
  • Knowledge of integral calculus, specifically Gaussian integrals
  • Experience with power series expansion of functions
NEXT STEPS
  • Learn how to use Z value tables for various probabilities in the standard normal distribution
  • Study Gaussian integrals and their applications in probability theory
  • Explore the use of polar coordinates in evaluating double integrals
  • Practice power series expansions for different functions beyond e^x
USEFUL FOR

Students studying statistics, mathematicians focusing on probability theory, and anyone interested in mastering integral calculus related to normal distributions.

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Homework Statement



1) Find the area under the curve for:
a) P( 0 <= Z <= 2.07)
b) P(-.64 <= Z < -.11)
c) P( Z > -1.06)
d) P(Z < -2.33)
e) P(Z >= 4.61)

2) a) Evaluate integral from 0 to 1.24 of e^(-x^2/2)
b) Evaluate the integral from -inf to inf of 6*e^(-x^2/2)

Homework Equations



Z value table
Power series expansion of e^x

The Attempt at a Solution



1)
a) Fz(2.07) - Fz(0) = .9808 - .5 = .4808
b) Fz(-.64) - Fz(a) = .4247 - .2611 = .163
c) 1 - Fz(-1.06) = .85
d) .0104
e) .00002

Can someone explain to me the difference in calculating an area where Z < x vs. Z <= x. Do I just use the value closest to x and less than it on the table for Z < x?
2) a)I used the power series expansion and dervied each term upto n = 4 and got
.6807
b) No clue how to do this. Polar coordinates?
 
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With the standard normal distribution (indeed, any continuous probability distribution)
there is no difference working with [tex]<[/tex] and [tex]\le[/tex], so that

[tex] P(Z < z) = P(Z \le z)[/tex]

As an example

[tex] P(Z < 1.24) = P(Z \le 1.24)[/tex]

Both can be found from a table or software.

For the integral problem, consider this. If I use [tex]Z = 2.1[/tex]

[tex]\begin{align*}<br /> P(Z \le 2.1) & = \int_{-\infty}^{2.1} \frac 1 {\sqrt{2 \pi}} e^{-{x^2}/2} \, dx\\<br /> & = \int_{-\infty}^0 \frac 1 {\sqrt{2 \pi}} e^{-{x^2}/2} \, dx + \int_0^{2.1} \frac 1 {\sqrt{2 \pi}} e^{-{x^2}/2} \, dx \\<br /> & = 0.5 + \frac 1 {\sqrt{2 \pi}} \int_0^{2.1} e^{-{x^2}/2} \, dx<br /> \end{align*}[/tex]

If you rearrange terms you find that

[tex] \int_0^{2.1} e^{-{x^2}/2} \, dx = \sqrt{2 \pi} \left(P(Z \le 2.1) - .5 \right)[/tex]

None of the terms on the right require a series expansion.
This idea should help you with your integral questions.
 
Great explanations.

a) = sqrt(2pi) * (P(Z <= 1.24) - .5)
b) = sqrt(2pi)/6

Is this right? Thanks.
 

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