BinomialDistribution
Author(s)
David M. Lane
Prerequisites
Distributions, Basic Probability, Variability
Learning Objectives
1. Define binomial outcomes
2. Compute the probability of getting X successes in N trials
3. Compute cumulative binomialprobabilities
4. Find the mean and standard...
Special ProbabilityDistributions
Chapter 8
Ibrahim Bohari
bibrahim@preuni.unimas.my
LOGO
BinomialDistributionBinomialDistribution
In an experiment of n independent trials, where
p is a the probability of a successful outcome
q=1-p is the probability that the outcome is a failure
...
Descriptive Statistics (graded)
If you were given a large data set such as the sales over the last year of our top 1,000 customers, what might you be able to do with this data? What might be the benefits of describing the data?
Week 2
Regression (graded)
Suppose you are given data from a survey showing...
Term
Random Variable
Definition
A variable that takes on different numerical values based on chance
Term
Discrete Random Variable
Definition
A random variable that can only assume a finite number of values or an infinite sequence of values such as 0,1,2,3....
Term
Continuous Random...
Chapter 5 BinomialDistribution
5 BINOMIALDISTRIBUTION
Objectives
After studying this chapter you should
•
be able to recognise when to use the binomialdistribution;
•
understand how to find the mean and variance of the
distribution;
•
be able to apply the binomialdistribution to a...
sunny or cloudy. If a day is sunny, the following day will be
sunny with probability 0.60. If a day is cloudy, the following day will be cloudy with probability 0.70.
Suppose it is cloudy on Monday.
a) What is the probability that it will be sunny on Wednesday?
There are two mutually exclusive ways...
series, such series is termed as normal frequency distribution or just frequency distribution. On the contrary, when there is estimation as to how the outcomes for any event will vary, they form a theoretical frequency distribution.
FREQUENCY DISTRIBUTION
• Observed frequency
• Event occurred
...
B. Weaver (31-Oct-2005)
Probability & Hypothesis Testing 1
Probability and Hypothesis Testing
1.1 PROBABILITY AND INFERENCE The area of descriptive statistics is concerned with meaningful and efficient ways of presenting data. When it comes to inferential statistics, though, our goal is to make...
Know the difference and relationship between sample statistics and population parameters.
1.3 Graphical Summaries Not included in the exam Ch. 2 Probability 2.1 Basic Ideas • • Understand the following important concepts: Random experiment, sample space, event. Understand the mathematical and physical...
6
The Normal
Distribution
Objectives
Outline
After completing this chapter, you should be able to
1
2
3
Identify distributions as symmetric or skewed.
4
Find probabilities for a normally distributed
variable by transforming it into a standard
normal variable.
Introduction
...
P(A’)P(B’)
Conditional probability:
The law of total probability and Bayes’ theorem:
Properties (2):
X and Y are independent X and Y are independentExpectation:
Bernoulli distribution: X ~ Bernoulli(p)
for x = 0 or 1 ; otherwise
E(X) = p Var(X) = p(1-p)
Binomialdistribution: X ~ Bin(n,p)
...
error will result in which of the following? A. Probability sampling B. Non-response bias C. Statistics about the actual population rather than the target population D. Inability to perform inferential statistics
2) A poll is planned to determine what proportion of all students favor an increase in...
sier!™ ing Everything Ea Mak
ta t i s t i c s S e nt ia l s Ess
Learn:
• Exactly what you need to know about statistical ideas and techniques • The “must-know” formulas and calculations • Core topics in quick, focused lessons
Deborah Rumsey, PhD
Auxiliary Professor and Statistics Education Specialist...
Statistics and Probability for Engineering Applications
With Microsoft® Excel
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Statistics and Probability for Engineering Applications
With Microsoft® Excel
by
W.J. DeCoursey
College of Engineering, University of Saskatchewan Saskatoon
Amsterdam San Diego
Boston
...
Introduction
Many experimental situations occur in which we observe the counts of events
within a set unit of time, area, volume, length etc. For example,
• The number of cases of a disease in different towns
• The number of mutations in set sized regions of a chromosome
• The number of dolphin pod...
module:
* Module 3 online lectures
* From the textbook, Business Statistics in Practice, read the following chapters:
* Sampling Distributions
* Confidence Intervals
* Additional Resources:
* Z Scores in Business
* Decision Tree Analysis: www.mindtools.com/dectree...
Appl. Statist. (2005)
54, Part 1, pp. 127–142
A useful distribution for ﬁtting discrete data: revival
of the Conway–Maxwell–Poisson distribution
Galit Shmueli,
University of Maryland, College Park, USA
Thomas P. Minka and Joseph B. Kadane,
Carnegie Mellon University, Pittsburgh, USA
Sharad...