Learning objectives
Model of power in organizations
Chapter 10
Power, counterpower, and dependence
Sources of power
Contingencies of power
Power and Influence in
and Influence in
the Workplace
the Workplace
Influence tactics
Organizational politics
politi
Feedback on project reports
Make sure different parts of your report fit together
Analyses at 2 levels e.g. motivation and leadership; personality
and conflict; values and teams
In-depth analyses
Covering the various reasons for why a theory / concept pre
Learning objectives
Teams in organizations
Groups versus teams
Self-directed work teams, virtual teams
Team effectiveness model
Team decision making
Team building
Strengths, weaknesses, techniques
Limitations of teams
CHAPTER 8 Communicating in Teams and
Learning objectives
Compare and contrast teams (vs. group) and
between different types of teams in organizations
between different types of teams in organizations
Ch
Chapter 8
Team Dynamics
Dynamics
Team effectiveness model
Team design: Task characteristi
Business Statistics
St
PAN Baoqian, Kris
Baoqian Kris
ISOM 2500
Topic 8: Simple Linear Regression
Part b
What we have done
What we have done
Descriptive
St
Statistics
Probability
Random
variables
variables
Sampling
distribution
distribution
Linear
regress
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 8: Simple Linear Regression
Part a
What we have done
What we have done
Descriptive
St
Statistics
Probability
Random
variables
variables
Sampling
distribution
distribution
Linear
regres
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 7: Hypothesis Testing
Part c
What we have done
What we have done
Descriptive
St
Statistics
Probability
Random
variables
variables
Sampling
distribution
distribution
Linear
regression
r
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 7: Hypothesis Testing
Part b
What we have done
What we have done
Descriptive
St
Statistics
Probability
Random
variables
variables
Sampling
distribution
distribution
Linear
regression
r
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 7: Hypothesis Testing
Part a
What we have done
What we have done
Descriptive
St
Statistics
Probability
Random
variables
variables
Sampling
distribution
distribution
Linear
regression
r
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 6: Confidence Interval
Part b
What we have done
What we have done
Descriptive
St
Statistics
Probability
Random
variables
variables
Sampling
distribution
distribution
Linear
regression
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 6: Confidence Interval
Part a
What we have done
What we have done
Discrete
Random
Variable
Descriptive
Statistics
Probability
Random
variables
Continuous
Random
Variable
Sampling
distr
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 5: Sampling Distribution
Part b
What we have done
What we have done
Discrete
Random
Variable
Descriptive
Statistics
Probability
Random
variables
Continuous
Random
Variable
Sampling
dis
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 5: Sampling Distribution
Part a
What we have done
What we have done
Discrete
Random
Variable
Descriptive
Statistics
Probability
Random
variables
Continuous
Random
Variable
Sampling
dis
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 4: Continuous random variables
What we have done
What we have done
Discrete
Random
Variable
Descriptive
Statistics
Probability
Random
Random
variables
Variables
Continuous
Random
Varia
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 3: Discrete random variables
What we have done
What we have done
Discrete
Random
Variable
Descriptive
Statistics
Probability
Random
variables
Continuous
Random
Variable
Sampling
distri
Business Statistics
St
PAN Baoqian, Kris
Baoqian Kris
ISOM 2500
Topic 2: Probability Part b
What we have done
What we have done
Descriptive
St
Statistics
Proba bility
Random
variables
Sampling
distribution
Simple
Linear
regression
Hypothesis
testing
Confi
Business Statistics
St
PAN Baoqian, Kris
Baoqian Kris
ISOM 2500
Topic 2: Probability Part a
What we have done
What we have done
Descriptive
St
Statistics
Probability
Probability
Random
variables
Sampling
distribution
Simple
Linear
regression
Hypothesis
te
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 1: Descriptive Statistics
- Part b
What we have done
What we have done
Descriptive
Statistics
Probability
Probability
Random
variables
Sampling
distribution
Simple
Linear
regression
Hy
Business Statistics
St
PAN Baoqian, Kris
Baoqian, Kris
ISOM 2500
Topic 1: Descriptive Statistics
- Part a
Course Overview
Course Overview
Descriptive
Statistics
Probability
Random
variables
Sampling
distribution
Linear
regression
Hypothesis
testing
Confid
Business Statistics
St
PAN Baoqian, Kris
Baoqian Kris
ISOM 2500
Topic 0: Introduction
Primary Goals for the Course
Primary Goals for the Course
For you to become an intelligent user of
Business Statistics information
As a manager, a consultant, or an inve
Random Variables
( number)
(
)
events (
)random() random
random variables. random variable sample space S
real function X : S , S element s
real number x ( discrete i ) X
random variable.
Note. random variable
real random variable
Practice Quiz I
1. The following bar chart describes the results of a survey concerning the relevance of study to
present job by school.
Focus on the School of Business and Management. What are the mode and the median
respectively?
(a) Relevant, Neutral
(
STAT 452/652 - MINITAB LAB 5
MULTIPLE REGRESSION
The data for todays lab work is in the MINITAB PROJECT le: regression lab data.MPJ on
the Gauss classdata drive in math452 652 folder. The data for the regression homework is in the
same folder, le: regress
Game 3: The M&M
Game Goal: to understand sampling variation and CLT for sample
proportion
Experiment: The M&M candies have six colors: blue, orange, red,
green, brown, and yellow. Choose any three colors among them, say,
blue, orange, and yellow. We wil
Answer 8
1.
A random sample of size 40 is drawn from a Bernoulli distribution where the population proportion
p of successes is unknown. p is an unbiased estimator of p. If p = 0.1, find a 90% confidence
interval for p.
(Give the answer correct to 3 signi
FAQ
By Ken
> Dear Ken,
>
I wanna make sure is da the below topics won't be
tested in test 2 ?
> -Difference of 2 sample means
> - Difference of 2 sample proportions
> -CI for difference ./. 2 population means
> -HT for 2 sample means
> 1)yes, quiz 2 does
ISOM 2500 Exercise 3
1.
Find the number of elements in each of the following sets.
(a) M = cfw_x : x is a prime number such that 30 < x < 50
(b) N = cfw_x : x is a possible outcome of tossing a coin twice
(c) O = cfw_x : x is a possible outcome of the eve
ISOM 2500 Exercise 2
1.
In each of the following cases, determine whether face-to-face interview is appropriate. Explain
briefly.
(a) Citizens opinions about small class teaching in Hong Kong.
(b) A study of favourite fashion brands of teenagers.
(c) Empl