Probability and Distribution | Bài giảng số 7 chương 4 học phần Applied statistics | Trường Đại học Quốc tế, Đại học Quốc gia Thành phố Hồ Chí Minh

Normal approximation of binomial probabilities: The binomial random variable is the number of successes in the n trials, and probability questions pertain to the probability of x successes in the n trials. Binomial probability refers to the probability of exactly x successes on n repeated trials in an experiment which has two possible outcomes (commonly called a binomial experiment).  Tài liệu giúp bạn tham khảo, ôn tập và đạt kết quả cao. Mời bạn đón xem.

Thông tin:
30 trang 1 tháng trước

Bình luận

Vui lòng đăng nhập hoặc đăng ký để gửi bình luận.

Probability and Distribution | Bài giảng số 7 chương 4 học phần Applied statistics | Trường Đại học Quốc tế, Đại học Quốc gia Thành phố Hồ Chí Minh

Normal approximation of binomial probabilities: The binomial random variable is the number of successes in the n trials, and probability questions pertain to the probability of x successes in the n trials. Binomial probability refers to the probability of exactly x successes on n repeated trials in an experiment which has two possible outcomes (commonly called a binomial experiment).  Tài liệu giúp bạn tham khảo, ôn tập và đạt kết quả cao. Mời bạn đón xem.

34 17 lượt tải Tải xuống
APPLIED STATISTICS
COURSE CODE: ENEE1006IU
Lecture 7:
Chapter 4: Probability and Distribuon
(3 credits: 2 is for lecture, 1 is for lab-work)
Instructor: TRAN THANH TU Email:
tu@hcmiu.edu.vn
tu@hcmiu.edu.vn 1
tu@hcmiu.edu.vn 2
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•A fundamental dierence separates discrete and connuous random variables in
terms of how probabilies are computed.
-For a discrete random variable, the probability funcon f (x) provides the
probability that the random variable assumes a parcular value.
-With connuous random variables, the counterpart of the probability funcon is
the probability density funcon, also denoted by f (x).
The dierence is that the probability density funcon does not directly provide
probabilies.
tu@hcmiu.edu.vn 3
The area under the graph of f (x) corresponding to a given interval does provide
the probability that the connuous random variable x assumes a value in that
interval.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Connuous probability distribuon: A probability distribuon in which
the random variable x can take on any value (is connuous).
•Because there are innite values that x could assume, the
probability of x taking on any one specic value is zero.
tu@hcmiu.edu.vn 4
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
Uniform probability distribuon
Normal probability distribuon
Normal approximaon of binomial probabilies
tu@hcmiu.edu.vn 5
Exponenal probability distribuon
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
tu@hcmiu.edu.vn 6
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS •Uniform
probability distribuon:
Uniform distribuons are probability distribuons with equally likely
outcomes.
There are two types of uniform distribuons: discrete and connuous.
In a discrete distribuon, each outcome is discrete.
In a connuous distribuon, outcomes are connuous and innite.
tu@hcmiu.edu.vn 7
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS •Uniform
probability distribuon:
tu@hcmiu.edu.vn 8
tu@hcmiu.edu.vn 9
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS •Uniform
probability distribuon:
tu@hcmiu.edu.vn 10
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribuon: The most important probability
distribuon for describing a connuous random variable.
•Normal Curve: The form, or shape, of the normal distribuon is illustrated by the
bell-shaped normal curve
tu@hcmiu.edu.vn 11
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribuon: characteriscs
•The enre family of normal distribuons is dierenated by two parameters: the
mean µ and the standard deviaon σ.
tu@hcmiu.edu.vn 12
•The highest point on the normal curve is at the mean, which is also the median
and mode of the distribuon.
•The mean of the distribuon can be any numerical value: negave, zero, or
posive. Three normal distribuons with the same standard deviaon but three
dierent means (−10, 0, and 20) are shown here.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
tu@hcmiu.edu.vn 13
•Normal probability distribuon: characteriscs
The normal distribuon is symmetric, with the shape of the normal curve to the le of
the mean a mirror image of the shape of the normal curve to the right of the mean. The
tails of the normal curve extend to innity in both direcons and theorecally never
touch the horizontal axis. Because it is symmetric, the normal distribuon is not
skewed; its skewness measure is zero.
The standard deviaon determines how at and wide the normal curve is. Larger values
of the standard deviaon result in wider, aer curves, showing more variability in the
data. Two normal distribuons with the same mean but with dierent standard
deviaons are shown here.
tu@hcmiu.edu.vn 14
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribuon: characteriscs
•Probabilies for the normal random variable are given by areas under the normal
curve. The total area under the curve for the normal distribuon is 1. Because the
distribuon is symmetric, the area under the curve to the le of the mean is 0.5
and the area under the curve to the right of the mean is 0.5.
tu@hcmiu.edu.vn 15
•The percentage of values in some commonly used intervals are:
a. 68.3% of the values of a normal random variable are within
plus or minus one standard deviaon of its mean. b. 95.4% of
the values of a normal random variable are within plus or minus
two standard deviaons of its mean. c. 99.7% of the values of a
normal random variable are within plus or minus three standard
deviaons of its mean.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribuon:
Standard Normal Probability Distribuon: A
random variable that has a normal distribuon
with a mean of zero and a standard deviaon
tu@hcmiu.edu.vn 16
of one is said to have a standard normal probability distribuon.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribuon:
Compung Probabilies for Any Normal Probability Distribuon:
tu@hcmiu.edu.vn 17
we can interpret z as the number of standard deviaons that the normal random
variable x is from its mean µ.
Use the table to check the probability
tu@hcmiu.edu.vn 18
•Normal
tu@hcmiu.edu.vn 19
probability
distribuon (z <
0):
tu@hcmiu.edu.vn 20
•Normal
| 1/30

Preview text:

APPLIED STATISTICS COURSE CODE: ENEE1006IU Lecture 7:
Chapter 4: Probability and Distribution
(3 credits: 2 is for lecture, 1 is for lab-work)
Instructor: TRAN THANH TU Email: tttu@hcmiu.edu.vn tttu@hcmiu.edu.vn 1
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•A fundamental difference separates discrete and continuous random variables in
terms of how probabilities are computed.
-For a discrete random variable, the probability function f (x) provides the
probability that the random variable assumes a particular value.
-With continuous random variables, the counterpart of the probability function is
the probability density function, also denoted by f (x).
The difference is that the probability density function does not directly provide probabilities. tttu@hcmiu.edu.vn 2
The area under the graph of f (x) corresponding to a given interval does provide
the probability that the continuous random variable x assumes a value in that interval.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Continuous probability distribution: A probability distribution in which
the random variable x can take on any value (is continuous).
•Because there are infinite values that x could assume, the
probability of x taking on any one specific value is zero. tttu@hcmiu.edu.vn 3
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
Uniform probability distribution
Normal probability distribution
Normal approximation of binomial probabilities tttu@hcmiu.edu.vn 4
Exponential probability distribution
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS tttu@hcmiu.edu.vn 5
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS •Uniform probability distribution:
Uniform distributions are probability distributions with equally likely outcomes.
There are two types of uniform distributions: discrete and continuous.
In a discrete distribution, each outcome is discrete.
In a continuous distribution, outcomes are continuous and infinite. tttu@hcmiu.edu.vn 6
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS •Uniform probability distribution: tttu@hcmiu.edu.vn 7 tttu@hcmiu.edu.vn 8
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS •Uniform probability distribution: tttu@hcmiu.edu.vn 9
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribution: The most important probability
distribution for describing a continuous random variable.
•Normal Curve: The form, or shape, of the normal distribution is illustrated by the bell-shaped normal curve tttu@hcmiu.edu.vn 10
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribution: characteristics
•The entire family of normal distributions is differentiated by two parameters: the
mean µ and the standard deviation σ. tttu@hcmiu.edu.vn 11
•The highest point on the normal curve is at the mean, which is also the median and mode of the distribution.
•The mean of the distribution can be any numerical value: negative, zero, or
positive. Three normal distributions with the same standard deviation but three
different means (−10, 0, and 20) are shown here.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS tttu@hcmiu.edu.vn 12
•Normal probability distribution: characteristics
• The normal distribution is symmetric, with the shape of the normal curve to the left of
the mean a mirror image of the shape of the normal curve to the right of the mean. The
tails of the normal curve extend to infinity in both directions and theoretically never
touch the horizontal axis. Because it is symmetric, the normal distribution is not
skewed; its skewness measure is zero.
• The standard deviation determines how flat and wide the normal curve is. Larger values
of the standard deviation result in wider, flatter curves, showing more variability in the
data. Two normal distributions with the same mean but with different standard deviations are shown here. tttu@hcmiu.edu.vn 13
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribution: characteristics
•Probabilities for the normal random variable are given by areas under the normal
curve. The total area under the curve for the normal distribution is 1. Because the
distribution is symmetric, the area under the curve to the left of the mean is 0.5
and the area under the curve to the right of the mean is 0.5. tttu@hcmiu.edu.vn 14
•The percentage of values in some commonly used intervals are:
a. 68.3% of the values of a normal random variable are within
plus or minus one standard deviation of its mean. b. 95.4% of
the values of a normal random variable are within plus or minus
two standard deviations of its mean. c. 99.7% of the values of a
normal random variable are within plus or minus three standard deviations of its mean.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribution:
Standard Normal Probability Distribution: A
random variable that has a normal distribution
with a mean of zero and a standard deviation tttu@hcmiu.edu.vn 15
of one is said to have a standard normal probability distribution.
4.3. CONTINUOUS PROBABILITY DISTRIBUTIONS
•Normal probability distribution:
Computing Probabilities for Any Normal Probability Distribution: tttu@hcmiu.edu.vn 16
we can interpret z as the number of standard deviations that the normal random
variable x is from its mean µ.
Use the table to check the probability tttu@hcmiu.edu.vn 17 •Normal tttu@hcmiu.edu.vn 18 probability distribution (z < 0): tttu@hcmiu.edu.vn 19 •Normal tttu@hcmiu.edu.vn 20