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
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
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
ADM2304X
May 2017
Applied Statistical Methods in Business
Class: on Thursdays, 19:00 22:00
In DMS-1140
DGD: On Tuesdays, 17:30 19:00
In DMS-1150
Text Book: Business Statistics, A-W Pearson, 2014
Authors: Sharpe et al (DVW) Canadian Ed# 2.
Prof.: Dr. Suren
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
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 - 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 - 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 - 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
ADM2304X
Assignment#2
Spring-Summer 2017
ADM2304X
Prof.: Dr. Suren Phansalker
Assignment#2 (100 Marks)
1. Due Date & Time: Must be Uploaded by Sunday, June 11, 2017 by 23:30 hrs.
2. Integrity Statement: Must be Printed/ Signed and Attached.
General Instru
ADM2304X:
May 2017
Applied Statistical Methods in Business
Class: on Thursdays, 19:00 22:00
In DMS-1140
DGD: On Fridays, 17:30 19:00
In DMS-1150
Text Book: Business Statistics, A-W Pearson, 2014
Authors: Sharpe et al(DVW) Canadian Ed# 2.
Prof.: Dr. Suren
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
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
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
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
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
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
STATISTICS for MANAGEMENT
ADM 2303D
Dr. Sarah Ben Amor
Intorduction
1
Prof Contact Information
[e-mail] [email protected]
[tel] 562-5800 x 4909
[Office] 7123, Desmarais Hall
[Office Hours]
Tue 14:30 to 15:30
or by appointment
2
Learning Statis
USING PROBABILITYAND
BAYES THEOREM
Using probability and Bayes
Theorem
Basics of probability
Picturing probability: Venn diagrams, Tree
diagrams
Probability rules
Complement rule
Addition rules
Multiplication rules
Conditional probability
Independence
ADM2303X- Summer 2014
Assignment 4- Part 2
Assignment 4 Part 2 - Solution
For Part1:
Must be done on MyStatLab
Due Date & Time: Tuesday, July 22, 2014 by 23:59 hrs.
For Part 2:
Due Date & Time: Tuesday, July 22, 2014, in class
Can be done in a group o
ADM 2303 Week 7
Binomial Experiments: discrete (can count), fixed # of trials,
2 outcomes only (yes/no, success/fail), probability success
p, probability fail q, each trial INDEPENDENT.
Total # outcomes: multiply choices of each event with each
other.
If
a) Linear with outliers best describes the general shape.
b)
Regression Analysis: medinc versus p_hsgrad, p_trades, .
The regression equation is
medinc = - 8329 + 19578 p_hsgrad + 67370 p_trades + 101042 p_collcert
- 80053 p_univdipl + 59767 p_univdeg
Adm 2304S
Assignment 2
Jordan DSouza
Saturday March 5, 2016
Question 1
A)
Two-sample T for BP_Toronto vs BP_Ottawa
BP_Toronto
BP_Ottawa
N
35
20
Mean
121.1
112.9
StDev
11.4
10.6
SE Mean
1.9
2.4
Difference = mu (BP_Toronto) - mu (BP_Ottawa)
Estimate for dif
ADM 2304S
Assignment 3
March 19, 2016
Jordan DSouza
7813814
Personal Ethics Statement
Individual Assignment:
By signing this Statement, I am attesting to the fact that I have reviewed the entirety of my
attached work and that I have applied all the approp
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
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
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