ADM2303X- Summer 2014
Assignment 2- Part 2-Solution
Assignment 2 Part 2 Solution
For Part1:
Must be done on MyStatLab
Due Date & Time: Tuesday, June 10, 2014 by 23:59 hrs.
For Part 2:
Due Date & Time: Tuesday, June 10, 2014, in class
Can be done in a
ADM2303X- Summer 2014
Assignment 3- Part 2
Assignment 3 Part 2
For Part1:
Must be done on MyStatLab
Due Date & Time: Tuesday, July 8, 2014 by 23:59 hrs.
For Part 2:
Due Date & Time: Tuesday, July 8, 2014, in class
Can be done in a group of at most 2 s
Dr. Suren Phansalker
ADM2303
Samples, Probability and
Its Rules
Populations and Samples:
1. Population: Is the Entire Group of Individuals or Instances about
whom we expect to learn.
2. Samples: A sample is a Representative Sub-Set of the
Population.
3. R
INTRODUCTION TO RANDOM
VARIABLES
Introduction to Random Variables
Introduction to random variables
Discrete random variables
Introduction to probability models
Expected value and variance
Operations on random variables
Random Variables
A random variable
Probability theory - Bayes' theorem
In this lecture we discuss :
Tree digram
Bayes theorem
Davood Astaraky
Telfer School of Management
Outline
Randomness and probability denitions
Special words in probability
Assessing probability
Disjoint (mutually exclu
Probability theory - Conditional probability
In this lecture we discuss :
Dependence
Conditional probability
Tree digram
Multiplication rule for dependent events
Checking for independence
ADM2303- Davood Astaraky
Telfer School of Management
Outline
Random
Probability theory - Independence
In this lecture we discuss :
Independence
Multiplication rule for independent events
ADM2303- Davood Astaraky
Telfer School of Management
Outline
Randomness and probability denitions
Special words in probability
Assessing
Probability Theory- General Addition Rule
In this lecture we discuss :
Marginal probability
Joint probability
Union probability
General addition rule
ADM2303- Davood Astaraky
Telfer School of Management
Outline
Randomness and probability denitions
Special
Probability theory
In this lecture we discuss :
Randomness and probability
Special words in probability
Assessing probability
Disjoint (mutually exclusive) events
Complement of an event
Probability distributions
ADM2303 - Davood Astaraky
Telfer School of
Populations, Samples and Sampling
Techniques
Data Types
Primary Data
Raw data that are collected by you or another
person with whom you are closely associated.
Secondary Data
Data that are collected by an outside source or
by someone in your organizatio
CONTINUOUS PROBABILITY
DISTRIBUTIONS
Continuous probability distributions
Review of discrete and continuous random
variables
Continuous probability distributions (models)
Continuous uniform probability distribution
Normal probability distribution
Nor
COMBINING RANDOM VARIABLES
&
CORRELATION
Combining random variables &
Correlation
Scatterplots
Correlation & Covariance
Causality
Combining random variables
IMPORTANCE
in portfolio analysis (finance)
anywhere we need to combine random variables
Scatterp
DISCRETE PROBABILITY
DISTRIBUTIONS:
The hyperGeometric, Binomial and
Poisson models
Discrete Probability
Distributions
A discrete random variable is a variable that can
assume only a countable number of values
Many possible outcomes:
number of complaint
Introduction to Quality and
Statistical Process Control
Themes of Quality
Management
Primary focus is on process improvement
Most variations in process are due to systems
Teamwork is integral to quality management
Customer satisfaction is a primary goal
O
STATISTICS for MANAGEMENT
ADM 2303D
Dr. Sarah Ben Amor
Intorduction
1
Prof Contact Information
[e-mail] benamor@telfer.uottawa.ca
[tel] 562-5800 x 4909
[Office] 7123, Desmarais Hall
[Office Hours]
Tue 14:30 to 15:30
or by appointment
2
Learning Statis
Samples, Probability and its Rules
Dr. Suren Phansalker
A Sample is a Representative Sub-Set of the Population.
A Sample must be Random.
In a Random Sample each occurrence or outcome is
Equally Likely.
Random Samples are:
1. Simple Random Samples (SR
ADM2303E
September 2013
Statistics for Management I
Location: DMS-1150
Course Times: Wed 19:00-22:00
Text Book: Business Statistics, Latest 2ndCanadian ed.
Author: Sharpe et al
Prof.: Dr. Suren Phansalker
Office: DMS-5142
Office Hours: Tuesdays-15:00-16:0
Dr. Suren Phansalker
ADM2303
Two-Variable Table Probabilities
When there are numerous cases and the data becomes very large, it is a common practice
to Group the data in specific categories. Calculations with these groups become very
efficient. Consider 1
Random Variables & Expected Values
Dr. Suren Phansalker
Random Variable (RV):
An RV is a variable whose value is the result of an
experiment with an unpredictable outcome.
Nomenclature:
X: RV
x or xi: the value RV assumes.
Example:
X: the RV indicating
Dr. Suren Phansalker
ADM2303
Random Variables and
Expected Values and
Solved Problems
I. Random Variable (RV):
An RV is a variable whose value is the result of an experiment with
unpredictable outcomes.
Nomenclature:
X, Y, Z: RV X, RV Y or RV Z (Uppercase
ADM 2303: Week 3
Measures of relative position only for numerical data.
o Percentile: p percentile divides data in 2
E.g (ascending order) 20 people, 4th tallest, you are in
the 80th percentile
Index of percentile (method 1): i = (k / 100) (n + 1)
n is
ADM 2303 Week 4
Randomness : Know outcomes, but not the EXACT outcome
Probability (P): value between 0 and 1. (Impossible to certain) *rule
1*
Experiment: observe something to collect data
Sample space (S): all possible outcomes
Sample point: one of the p
ADM 2303: Week 2
Data
o
Numerical
Scatter plot
Histogram
Box plot
Dot plot
Dependency/association means there is a pattern. *use this language.
Positive association = upward trend/increasing.
Negative association = downward trend/decreasing.
Independent m
ADM 2303 Week 8
Discrete: P(x) = probability.
Continuous: F(x) = density (shape of distribution, not
probability)
o
o
Desired outcome / total # outcomes
o
Specific area / total area (always = 1)
o
Probability: find Area under curve
Probability of individu
ADM 2303 Week 5
Note for dependant experiments:
o
o
P(A AND B) = p(A) x p(B|A)
P(A AND B) = p(B) x p(A|B)
Note for independent experiments:
o
P(A AND B) = p(A) x p(B)
Without replacement = dependant
With replacement = independent
Given that = conditional
o
Skewness: tail on left (negative skewed), tail right (positive), symmetric
The Sample/Population Mean (avg.): The sum of the observed values / Sample Size (or Population
Size)
Median and Index Point: i=(k/100)(n+1) OR i=(p/100)(n) *when index is not a
ADM 2303 Week 6
Random Variable: value of each outcome in experiment
o Discrete random variable: typically take on whole #
E.g. histogram
o Continuous random variable: any numerical value
E.g bell curve
Probability of random variable denoted as P(x =