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[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 1
Bruce Mayer, PE Chabot College Mathematics
Bruce Mayer, PELicensed Electrical & Mechanical Engineer
Chabot Mathematics§11.2
ProbabilityDistribution
Fcns
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 2
Bruce Mayer, PE Chabot College Mathematics
Review §
Any QUESTIONS About• §11.1 Discrete Probability
Any QUESTIONS About HomeWork• §11.1
→ HW-20
11.1
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 3
Bruce Mayer, PE Chabot College Mathematics
§11.2 Learning Goals
Define and examine continuous probability density/distribution functions
Use uniform and exponential probability distributions
Study joint probability distributions
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 4
Bruce Mayer, PE Chabot College Mathematics
Probability Distribution
Consider Data on the Height of a sample group of 20 year old Men
Ht (in) No.
64 164.5 065 065.5 066 266.5 467 567.5 468 868.5 1169 1269.5 1070 970.5 871 771.5 572 472.5 473 373.5 174 174.5 075 1
We can Plot this Frequency Data using bar
y_abs=[1,0,0,0,2,4,5,4,8,11,12,10,9,8,7,5,4,4,3,1,1,0,1]xbins = [64:0.5:75];axes; set(gca,'FontSize',12);whitebg([0.8 1 1]); % Chg Plot BackGround to Blue-Greenbar(xbins, y_abs, 'LineWidth', 2),grid, ... xlabel('\fontsize{14}Height (Inches)'), ylabel('\fontsize{14}Height (Inches)'),... title(['\fontsize{16}Height of 20 Yr-Old Men',])
62 64 66 68 70 72 74 760
2
4
6
8
10
12
Height (Inches)
He
igh
t (In
che
s)
Height of 20 Yr-Old Men
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 5
Bruce Mayer, PE Chabot College Mathematics
Probability Distribution Fcn (PDF)
Because the Area Under the Scaled Plot is 1.00, exactly, The FRACTIONAL Area under any bar, or set-of-bars gives the probability that any randomly Selected 20 yr-old man will be that height
e.g., from the Plot we Find • 67.5 in → 4%• 68 in → 8%• 68.5 in → 11%
Summing → 23 % Thus by this data-
set 23% of 20 yr-old men are 67.25-68.75 inches tall
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 6
Bruce Mayer, PE Chabot College Mathematics
Random variables can be Discrete or Continuous
Discrete random variables have a countable number of outcomes• Examples: Dead/Alive, Red/Black,
Heads/Tales, dice, deck of cards, etc.
Continuous random variables have an infinite continuum of possible values. • Examples: Battery Current, human weight,
Air Temperature, the speed of a car, the real numbers from 7 to 11.
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 7
Bruce Mayer, PE Chabot College Mathematics
Continuous Case
The probability function that accompanies a continuous random variable is a continuous mathematical function that integrates to 1.
The Probabilities associated with continuous functions are just areas under a Region of the curve (→ Definite Integrals)
Probabilities are given for a range of values, rather than a particular value • e.g., the probability of Jan RainFall in Hayward,
CA being between 6-7 inches (avg = 5.20”)
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 8
Bruce Mayer, PE Chabot College Mathematics
Continuous Probability Dist Fcn
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 9
Bruce Mayer, PE Chabot College Mathematics
Continuous Case PDF Example
Recall the negative exponential function (in probability, this is called an “exponential distribution”):
0 if0
0 if)(
x
xexf
x
This Function Integrates to 1 for limits of zero to infinity as required for all PDF’s
1100
0
xx ee
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 10
Bruce Mayer, PE Chabot College Mathematics
Continuous Case PDF Example
x
p(x)=e-x
1
For example, the probability of x falling within 1 to 2:
The probability that x is any exact value (e.g.: 1.9476) is 0 • we can ONLY assign
Probabilities to possible RANGES of x
x
1
1 2
p(x)=e-x
NO Area Under a
LINE
23% 23.368.135.
2)(1
12
2
1
2
1
ee
eexp xx
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 11
Bruce Mayer, PE Chabot College Mathematics
Example DownLoad Wait
When downloading OpenProject SoftWare, the website may put users in a queue as they attempt the download.
The time spent in line before the particular download begins is a random variable with approx. density function
minutes 01for0
min 01min 0for05.004.00045.0
minutes 0for02
x
xxx
x
xP
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 12
Bruce Mayer, PE Chabot College Mathematics
Example DownLoad Wait
For this PDF then, What is the probability that a user waits at least five (5) minutes before the download?
SOLUTION: We need P(x) ≥ 5 which can be found by
integration and noting that if x is larger than 10, the probability is zero. Thus by the Probability:
5
5 dxxPxP
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 13
Bruce Mayer, PE Chabot College Mathematics
Example DownLoad Wait
ContinuePDFReduction
Thus There is a 43.75% chance of a 5 minute PreDownLoad Wait Time
10
5
2 05.004.00045.0 dxxx
5
5 dxxPxP
1674375.0
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 14
Bruce Mayer, PE Chabot College Mathematics
Example Build a PDF
Find a value of k so that the following represents a Valid, Continuous Probability Distribution Function
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 15
Bruce Mayer, PE Chabot College Mathematics
Example Build a PDF
SOLUTION: The function is always NON-negative
for non-negative inputs, so simply need to verify that the definite integral equals 1 (that all probabilities together Add-Up, or Integrate, to 100%).
Thus, the correct value of k produces this functional behavior →
1
dxxf
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 16
Bruce Mayer, PE Chabot College Mathematics
Example Build a PDF
Because the function is identically zero everywhere outside of the interval [0, k], restrict the evaluation to that interval →
Solve by SubStitution; Let:
Then 1 10
2
kx
x
dxx
x
xdx
du2 dxdu
x
2
1
12
0
kx
x x
du
u
x
1 10
2
k
dxx
x
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 17
Bruce Mayer, PE Chabot College Mathematics
Example Build a PDF
Then 1 1
2
1
0
kx
x
duu
1ln2
1
1 00
2
kx
x
k
udxx
x
21ln0
2 k
x
210ln1ln 22 k
21ln 2 k
1 10
2
kx
x
dxx
x
21ln1ln 2 k
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 18
Bruce Mayer, PE Chabot College Mathematics
Example Build a PDF
Finally However, the 0 ≤ x ≤ k interval ends in a
non-negative value so need k-positive:
Thus the Desired PDF
577.212 ek
.otherwise , 0
10 if , 1)(
22
ex
x
xxf
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 19
Bruce Mayer, PE Chabot College Mathematics
Uniform Density Function
Definition
Graph
OtherWise0
if1
bxaabxf
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 20
Bruce Mayer, PE Chabot College Mathematics
Example Random No. Generator
A Random Number Generator (RNG) selects any number between 0 and 100 (including any number of decimal places).
Because each number is equally likely, a uniform distribution models the probability distribution.
What is the probability that the RNG selects a number between 50 and 60?
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 21
Bruce Mayer, PE Chabot College Mathematics
Example Random No. Generator
SOLUTION: The Probability Distribution Function:
Then the Probability of Generating a RN between 50 & 60
60
50
100
16050 dxxP
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 22
Bruce Mayer, PE Chabot College Mathematics
Example Random No. Generator
Evaluating the Integral
As Expected find the Probability of a 50-60 RN as 10%
60
50
100
16050 dxxP
60
50100
x
1.0100
50
100
60
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 23
Bruce Mayer, PE Chabot College Mathematics
Exponential Density Function
Definition
Graph
00
0if
x
xexf
x
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 24
Bruce Mayer, PE Chabot College Mathematics
Example SmartPhone LifeSpan
The battery of a popular SmartPhone loses about 20% of its charged capacity after 400 full charges.
Assuming one charge per day, the estimated probability density function for the length of tolerable lifespan for a phone that is t years old →
otherwise , 0
0 if , 12.1 12.1 tetf
t
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 25
Bruce Mayer, PE Chabot College Mathematics
Example SmartPhone LifeSpan
Find the probability that the tolerable lifespan of the SmartPhone is at least 500 days (500 charges).
SOLUTION: The probability of a tolerable lifespan being greater than or equal to 500 days (500/365 years):
365/500
12.1 12.1 dte t
Mt
Me
365/500
12.1
12.1
12.1lim
)365/500(12.112.1lim
ee M
M
%56.212156.00
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 26
Bruce Mayer, PE Chabot College Mathematics
Joint Probability Distribution Fcn
A joint probability density function f(x, y) has the following properties:
1. f(x, y) ≥ 0 for all points (x, y) in the Cartesian Plane
2. Double Integrates to 1:
3. The Probability that an Ordered Pair, (X, Y) Lies in Region R found by:
x
x
y
ydxdyyxf ,
R
dAyxfRYXP ,in ,
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 27
Bruce Mayer, PE Chabot College Mathematics
Joint Probability Distribution Fcn
Example joint probability density function Graph
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 28
Bruce Mayer, PE Chabot College Mathematics
Example Joint PDF
Consider the Joint PDF:
Find: 5 yxP
xyP 5
dxdyex
yx 65
0
5
0
32
5
0
5
032 2 dxe
xy
yyx
5
0
215 22 dxee xx
502152 xx ee
99986.0
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 29
Bruce Mayer, PE Chabot College Mathematics
WhiteBoard PPT Work
Problems From §11.2• P48 → Traffic Lite Roullette
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 30
Bruce Mayer, PE Chabot College Mathematics
All Done for Today
FittingPDFs to
Hists
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 31
Bruce Mayer, PE Chabot College Mathematics
Bruce Mayer, PELicensed Electrical & Mechanical Engineer
Chabot Mathematics
Appendix
–
srsrsr 22
a2 b2
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 32
Bruce Mayer, PE Chabot College Mathematics
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 33
Bruce Mayer, PE Chabot College Mathematics
[email protected] • MTH16_Lec-21_sec_11-2_Continuous_PDFs.pptx 34
Bruce Mayer, PE Chabot College Mathematics