Cumulative distribution function (what is this 't'?)

In summary, a cumulative distribution function is a statistical tool used to measure the probability distribution of a random variable. It calculates the probability that the variable will take on a value less than or equal to a given value, denoted by 't'. This can be visualized as a curve on a graph, where the area under the curve up to a certain point represents the probability of the variable being less than or equal to that point. The cumulative distribution function is an important tool in understanding and analyzing data in various fields, such as finance, economics, and engineering.
  • #1
Mattofix
138
0
my question is regarding 'continuous' cumulative distribution functions.

i kind of get it apart from that darn 't' in the definition (see http://upload.wikimedia.org/math/f/2/4/f24252ffb5e5e747b246189b7e1cfcce.png). My textbook, my lecture notes and even wikipedia don't refrer to the 't', apart from in the definition. I wouldn't mind apart from that i am working my way through some questions and have come across quite a few
asking me for the 'c.d.f of X for all t' (for example t<0 and t>2), not asking for the c.d.f of x values (like all of the worked examples i have come across) so what are the questions after?

Here is an example so you understand what my problem is.

Homework Statement


'X is a continuous random quantity with probability density function f(x) = x for 0<x<1, f(x)=2-x for 1 [tex]\leq[/tex] x < 2 with f(x)=0
for all other x. Find the value of Fx(t), the cumulative distribution function of X, for all t (that is t<0 and t>2 as well as 0 [tex]\leq[/tex] t [tex]\leq[/tex] 2 )'


Homework Equations



http://upload.wikimedia.org/math/f/2/4/f24252ffb5e5e747b246189b7e1cfcce.png
http://upload.wikimedia.org/math/f/d/e/fdec25ee8674e78b0bad557daa923a41.png (maybe for when i find the new boundaries they are asking for?)

The Attempt at a Solution


If it was like all of the examples iv seen id say for x<0 F(x)=0, for x>2 F(x)=1, for 0<x<1 F(x)= x[tex]^{2}[/tex]/2 and for 1<x<2 F(x)= 2x - x[tex]^{2}[/tex]/2 , but its not...
 
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  • #2
If that definition makes any sense to you at all, you must know how to integrate. And if that is true, then you should understand that the t is a "dummy variable"- it does not exist and has no meaning "outside" the integral.

Perhaps it would be easier to understand by looking at the analogous situation in a sum:
[tex]\sum_{n=1}^5 3n- 1= (3(1)-1)+ (3(2)-1)+ (3(3)-1)+ (3(4)-1)+ (3(5)-1)[/tex]
[tex]= 2+ 5+ 8+ 11+ 14= 40[/tex]
the "n" is a "dummy index" which exists only in the sum.
 
  • #3
ok, but what about the example? i have integrated.

if you can help me get the correct answer for this then hopefully i will be able to make the links and get to grips with it.
 
  • #4
Mattofix said:

The Attempt at a Solution


for x<0 F(x)=0, for x>2 F(x)=1, for 0<x<1 F(x)= x[tex]^{2}[/tex]/2 and for 1<x<2 F(x)= 2x - x[tex]^{2}[/tex]/2 , but its not...


ok so for t<0 F(t)=0, for t>2 F(t)=1,

and for 0<t<1 F(t)= t[tex]^{2}[/tex]/2 and for 1[tex]\leq[/tex]t<2 F(x)= 2t - t[tex]^{2}[/tex]/2 , but I am not being asked for that, I am being asked for 0[tex]\leq[/tex]t[tex]\leq[/tex]2, what is the answer to this?
 
  • #5
You are close, but still miss one point:

Since the density is in two "pieces", you need to calculate the distribution function F(t) for two cases.

case 1: 0 < t < 1. Integrate the density from 0 to t to obtain F(t) for this case.

case 2: [tex] 1 \le t < 2 [/tex]. Here is where you miss something. The correct value for [tex] F(t) [/tex] is found by integrating the density from 0 to t. Since the density is in two pieces, and [tex] t [/tex] is in the second interval, the integral here is broken into two pieces.
Write it out - it is MUCH easier to see in symbols than in my cryptic explanation.
 

What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a mathematical function that describes the probability that a random variable takes on a value less than or equal to a given value. It is a way of summarizing the distribution of a random variable in a single function.

What is the purpose of a cumulative distribution function (CDF)?

The purpose of a cumulative distribution function (CDF) is to provide a way to calculate the probability that a random variable will take on a value less than or equal to a given value. It allows us to understand the distribution of a random variable and make predictions about the likelihood of certain outcomes.

What is the difference between a cumulative distribution function (CDF) and a probability density function (PDF)?

A cumulative distribution function (CDF) and a probability density function (PDF) are two different ways of describing the same information about a random variable. A CDF gives the probability of a random variable taking on a value less than or equal to a given value, while a PDF gives the probability of a random variable taking on a specific value. In other words, a CDF is the integral of a PDF.

What is the 't' in a cumulative distribution function (CDF)?

The 't' in a cumulative distribution function (CDF) typically represents the threshold value for which we are calculating the probability. It can also represent the time variable in a time-dependent CDF, where the probability is calculated for a specific point in time.

How is a cumulative distribution function (CDF) used in statistical analysis?

A cumulative distribution function (CDF) is used in statistical analysis to understand the distribution of a random variable and make predictions about the likelihood of certain outcomes. It is also used to calculate percentiles, which can be used to compare different groups or populations. Additionally, CDFs can be used to test hypotheses and make inferences about the data.

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