Probability and gauss integral

In summary, we need to find the area under the curve for various values of Z using the standard normal distribution. This can be done by using the Z value table or software. When calculating the area, there is no difference between using < or ≤. For the integral problems, we can use a combination of the power series expansion and the formula for the standard normal distribution. Specifically, for the first integral, we can use the formula and for the second integral, we can use the power series expansion.
  • #1
squaremeplz
124
0

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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  • #2
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*}
P(Z \le 2.1) & = \int_{-\infty}^{2.1} \frac 1 {\sqrt{2 \pi}} e^{-{x^2}/2} \, dx\\
& = \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 \\
& = 0.5 + \frac 1 {\sqrt{2 \pi}} \int_0^{2.1} e^{-{x^2}/2} \, dx
\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.
 
  • #3
Great explanations.

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

Is this right? Thanks.
 

Related to Probability and gauss integral

1. What is the concept of probability?

The concept of probability is a measure of the likelihood that an event will occur. It is often expressed as a number between 0 and 1, where 0 indicates impossibility and 1 indicates certainty.

2. What is the role of probability in science?

Probability plays a crucial role in science as it helps us understand the likelihood of certain outcomes and make predictions based on data and evidence. It is used in various fields such as physics, biology, and economics to model and explain real-world phenomena.

3. What is the Gaussian or normal distribution?

The Gaussian or normal distribution is a probability distribution that is commonly used to describe natural phenomena, such as the distribution of heights or weights of a population. It is characterized by a bell-shaped curve and is symmetric around its mean, with most values falling close to the mean and fewer values further away.

4. How is the Gaussian distribution related to the concept of probability?

The Gaussian distribution is closely related to the concept of probability as it allows us to calculate the probability of a particular outcome or range of outcomes within a given dataset. It is often used in statistical analysis to make predictions and draw conclusions about a population based on a sample.

5. What is the Gauss integral and how is it used in probability?

The Gauss integral, also known as the Gaussian integral, is a mathematical formula used to calculate the area under a Gaussian curve. In probability, it is used to find the probability of a random variable falling within a certain range of values. It is also used to calculate other important quantities, such as mean and standard deviation, in the Gaussian distribution.

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