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

Click For Summary
SUMMARY

The discussion centers on understanding the cumulative distribution function (CDF) for a continuous random variable defined by a piecewise probability density function (PDF). The participant seeks clarification on the role of the variable 't' in the CDF definition, particularly when calculating values for different intervals of 't'. The correct CDF values are established as F(t) = 0 for t < 0, F(t) = 1 for t > 2, F(t) = t²/2 for 0 < t < 1, and F(t) = 2t - t²/2 for 1 ≤ t < 2. The integration of the PDF over specified intervals is crucial for accurate CDF computation.

PREREQUISITES
  • Understanding of continuous random variables and probability density functions (PDFs).
  • Knowledge of cumulative distribution functions (CDFs) and their definitions.
  • Proficiency in integration techniques, particularly for piecewise functions.
  • Familiarity with mathematical notation and terminology related to probability theory.
NEXT STEPS
  • Study the properties of cumulative distribution functions (CDFs) in probability theory.
  • Learn how to compute integrals for piecewise functions, focusing on continuous random variables.
  • Explore examples of cumulative distribution functions for various probability density functions.
  • Investigate the implications of dummy variables in mathematical definitions and their applications in statistics.
USEFUL FOR

Students studying statistics, mathematicians working with probability theory, and anyone involved in data analysis requiring a solid understanding of cumulative distribution functions and their calculations.

Mattofix
Messages
137
Reaction score
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 \leq 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 \leq t \leq 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^{2}/2 and for 1<x<2 F(x)= 2x - x^{2}/2 , but its not...
 
Physics news on Phys.org
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:
\sum_{n=1}^5 3n- 1= (3(1)-1)+ (3(2)-1)+ (3(3)-1)+ (3(4)-1)+ (3(5)-1)
= 2+ 5+ 8+ 11+ 14= 40
the "n" is a "dummy index" which exists only in the sum.
 
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.
 
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^{2}/2 and for 1<x<2 F(x)= 2x - x^{2}/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^{2}/2 and for 1\leqt<2 F(x)= 2t - t^{2}/2 , but I am not being asked for that, I am being asked for 0\leqt\leq2, what is the answer to this?
 
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: 1 \le t &lt; 2. Here is where you miss something. The correct value for F(t) is found by integrating the density from 0 to t. Since the density is in two pieces, and t 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.
 

Similar threads

  • · Replies 11 ·
Replies
11
Views
3K
  • · Replies 20 ·
Replies
20
Views
3K
Replies
7
Views
1K
Replies
8
Views
2K
  • · Replies 11 ·
Replies
11
Views
2K
  • · Replies 5 ·
Replies
5
Views
1K
  • · Replies 105 ·
4
Replies
105
Views
7K
  • · Replies 13 ·
Replies
13
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
Replies
6
Views
2K