Ph1690 - Foundation of
Biostatistics
Section 3: Sampling
Distributions and the Central
Limit Theorem
Statistical Inference:
Populations
and Samples to reach
Statistical inference is the attempt
a
conclusion concerning all members of a class
from observati
Ph1690 - Foundation of
Biostatistics
Section 1:Descriptive Statistics
Part 2: Measures of Spread
Measures of Spread:
Range
Inter-quartile Range
Variance
Standard deviation
Coefficient of variation
Range
It is the difference between the largest
obser
Random variable
and its distribution
(continued)
Cumulative Distribution
The cumulative distribution function (cdf)
of a random variable X is
F(x) = Pr( X <= x)
cdf is simply the probability of the event cfw_X <= x,
cdf is widely used because many statist
Ph1690 - Foundation of
Biostatistics
Section 1:Descriptive Statistics
Part 2: Measures of Spread
Measures of Spread:
Range
Inter-quartile Range
Variance
Standard deviation
Coefficient of variation
Range
It is the difference between the largest
obser
Ph1690 - Foundation of
Biostatistics
Section 1:Descriptive Statistics
Introduction
The study of statistics explores the collection,
organization, analysis and interpretation of
numerical data.
We will need several basic methods used to
describe, present
Ph1690 - Foundation of
Biostatistics
Section 1:Descriptive Statistics
Part 4: Graphic Methods
Often we study large groups of persons and have
multiple measurements on each person. It is often
helpful to convey information about the entire study
populatio
Probability I
Examples of probability in daily
life
The chance of raining tomorrow is high
The likelihood of winning a Texas lottery is
about 1 in 10 million
The rate of automobile accident for a
drunk driver is 5 times higher than a
normal one
Proportion
Probability II
Independence and
multiplication law
The occurrence of one event may or may
not have effect on the chance of
occurrence of another event. Two events
A and B are called independent if
P(AB ) = P(A) x P(B)
otherwise, they are said to be depend