6/1/2015
CHAPTER 2 (Week 3)
DESCRIPTIVE STATISTICS
Dr.S.Rajalingam
L1  Numerical Summary of Data
L2  Data Display and summary
REMARK REFER TO CHAPTER 4
1
Learning Objectives:
At the end of the lesson, students should be able to:
Explain the concepts of
Chapter 10 (Week 12)
DOE , 1 & 2Way ANOVA
Dr.S.Rajalingam
L1: Factorial Experiments
L2: 22 Factorial Design & Regression
Model
1
Learning Outcomes
At the end of the lesson student should be able
to:
Design and conduct factorial experiments
involving two
Chapter 1 (Week 1 & 2)
Probability
Dr.S.Rajalingam
Lecture 4:
Conditional Probability
Independent Events
1
May 15
Learning Objectives
Understand and apply probability properties to
calculate probabilities of specific events.
Calculate the conditional pr
6/11/2015
Chapter 3 (Week 4)
Continuous Random
Variable
Dr.Rajalingam
L2 Continuous random variable
Remark Refer to chapter 3
CONTINUOUS RANDOM
VARIABLES
Learning Objectives:
At the end of the lecture, you will be able to :
 describe continuous random v
CHAPTER 4 (Week 6)
CONTINUOUS PROBABILITY
DISTRIBUTION
Dr.S.Rajalingam
L2 Nonstandard Normal Distributions
And other continuous distributions
Remark Refer to Chapter 3 (Reading Material) +
FYS
Learning Objectives:
At the end of the lecture, you will be a
5/26/2015
Chapter 1 (week 1 & 2)
Probability
Dr.S.Rajalingam
Lecture 3: Permutations and
Combinations
1
May 15
Learning Objectives
After the lesson student should be able to
 Use the counting rule: permutation and
combination, to find the number of ways
Chapter 1 Week 1 & 2
Probability
Dr.S.Rajalingam
Lecture 5:
Multiplicative Law
Total Probability Theorem
Bayes Theorem
1
May 15
Learning Objectives
Apply multiplicative law to fine probability of certain
events.
Apply a total probability rule to find the
CHAPTER 2 (Week 3)
DESCRIPTIVE STATISTICS
Dr.S.Rajalingam
L3  Graphical display of Data
Remark Refer to Chapter 4 (Printed Notes)
Learning Objectives:
At the end of the lesson, students should be able to:
Construct and interpret pictorial and tabular dis
Chapter 1 (Week 1  2)
Probability
(Dr.S.Rajalingam)
Lecture 1
Introduction to Basic Probability
Sample Spaces and Events
Learning Objectives:
At the end of the lecture student should be able to:
Define and construct sample space of an experiment.
Define
7/14/2015
Chapter 9 (Week 11)
Multiple Linear
Regression Model
Dr.S.Rajalingam
L1 Multiple Linear Regression
(MLR) Model
1
Learning Outcomes
At the end of the lesson, the student should be able to
Use the LSM to estimate a multiple linear model
Determine
6/11/2015
CHAPTER 4 (Week 6)
CONTINUOUS PROBABILITY
DISTRIBUTION
Dr.S.Rajalingam
L1 Exponential and Normal distribution
Remark : Refer to Chapter 3
Exponential and Normal
Distribution
Learning Objectives:
At the end of the lecture, you will be able to :
6/3/2015
Chapter 3 (week 4)
Discrete Random Variable
Dr.S.Rajalingam
L1 Discrete random variable
Remark Refer to Chapter 2
DISCRETE RANDOM VARIABLES
Learning Objectives:
At the end of the lecture, you will be able to :
Describe types of random variables
Chapter 1 (Week 12)
Probability
Dr.S.Rajalingam
Lecture 2:
Probability of Events
Counting Rule
May 15
1
Learning Objectives
At the end of the session student should be able to:

Determine the probability of events
Interpret and understand the properties
6/11/2015
CHAPTER 3 (Week 5)
DISCRETE PROBABILITY
DISTRIBUTION
Dr.S.Rajalingam
L 1 Binomial and Poisson Distribution
Remark: Chapter 2 (Printed Notes)
DISCRETE PROBABILITY
DISTRIBUTION
Learning Objectives:
:
At the end of the lecture, you will be able to
6/22/2015
Chapter 5 (Week 7)
Random Sample & CLT, Normal
Approximation & X bar R Charts
Dr. S.Rajalingam
L2 & L3  Xbar and R Charts
1
Learning Outcomes
At the end of the lesson student should be able to
Understand the concept of SPC especially for
Xbar
6/22/2015
Chapter 5 (Week 7)
Random Sample & CLT, Normal
Approximation & X bar R Charts
Dr. S.Rajalingam
L1 Random Sample
1
Learning Outcomes
At the end of the lesson student should be able to
Describe the terms random sample, statistics
and sampling di
7/14/2015
Chapter 7 (Week 10)
Hypothesis Testing for Two
Populations
Dr.S.Rajalingam
L3: (7) Hypothesis Testing for Difference in Mean for two
populations (variance unknown and Unequal)
(8) Hypothesis Testing for Difference in means for
difference in two
7/8/2015
Chapter 6 (Week 9)
Hypothesis Testing for Single
Population
Dr.S.Rajalingam
L3
(1). Hypothesis testing claim about normal mean
() when variance (2 ) is known (Z test)
Learning Outcomes:
At the end of the lesson student should be
able to

Perform
Tutorial 4 Solution
1.
Solution:
i.
The probability that all the student pilots successfully land the plane using the simulator
is,
9
P( X 9) (0.7) 9 (0.3) 0 0.0404
9
ii.
The probability that none of the student pilots successfully lands the plane
us
Tutorial 1  Answers
1.
i. 0.4
vi. 0.53
2.
ii. 0.77
vii. 0.39
iii. 0.23
viii. 0.75
iv. 0.87
v. 0.3
(Use tree diagram)
i.
Let E1 be the event the second ball drawn is black, that is E1 = cfw_BBUWB.
So, P(E1) = P(BB) + P(WB) = (3/7)(2/6)+(4/7)(3/6)=3/7
ii.
7/8/2015
Chapter 7 (Week 10)
Hypothesis Testing for
Two Populations
Dr.S.Rajalingam
L 1:
(5) Hypothesis Testing for Difference in Mean for two
populations (variance known)
L2: (6) Hypothesis Testing for Difference in Mean for two
populations (variance unk
7/8/2015
Chapter 6 (Week 9)
Hypothesis Testing for Single Population
Dr.S.Rajalingam
L6  Determine the Confidence Interval for hypothesis
test with Variance Unknown (ttest)
L7  (3) Hypothesis Test about a Proportion &
(4) Hypothesis Test for variance
1
Tutorial 1  Answers
1.
i. 0.4
vi. 0.53
2.
ii. 0.77
vii. 0.39
iii. 0.23
viii. 0.75
iv. 0.87
v. 0.3
(Use tree diagram)
i.
Let E1 be the event the second ball drawn is black, that is E1 = cfw_BBUWB.
So, P(E1) = P(BB) + P(WB) = (3/7)(2/6)+(4/7)(3/6)=3/7
ii.
Tutorial 5  Solution
1.
Solution:
E(x) = 0.66, Var(x) = 0.5244, so X ~ N(0.66, 0.5244)
Given n=100, so the X ~ N (0.66,
0.5244
0.5 0.66
) P( X 0.5) P( Z
) 0.014
100
0.005244
2.
Solution:
X ~ N (22.3,
4
23 22.3
) P( X 23) P( Z
) 0.081
64
0.25
3.
Solutio
7/1/2015
Chapter 6 (Week 8)
Point Estimate & Hypothesis
Testing
Dr.S.Rajalingam
L1  Point Estimate
Learning Outcomes
At the end of the lesson student should be
able to
Estimate population mean.
Differentiate between biased and unbiased
estimator.
Iden
Chapter 8 (Week11)
Simple Linear Regression
Analysis
Dr.S.Rajalingam
L2 ANOVA table & Confidence Interval
Learning Outcomes
At the end of the lesson, the student should be able to
Perform the ANOVA tests  to determine the significance
of the model.
Const
7/8/2015
Chapter 6 (Week 9)
Hypothesis Testing for Single
Population
Dr.S.Rajalingam
L4  Determine the confidence interval for
hypothesis test with variance known.
L5 (2).Hypothesis Test about the Normal
Mean, Variance Unknown (ttest)
1
July 15
Learning
Tutorial 3  Solution
1. Solution:
i.
ii.
iii.
iv.
k = 0.25;
Discrete since the profit is in term of integer value;
E(X) = 82.5
v.
P( X 150) 1 P( X 150) 1 P( X 200) 1 0.05 0.95
Sd(X) = V (X ) 3068.75 55.3963
2. Solution:
i.
Discrete distribution
ii.
k =
Chapter 8 (Week 11)
Simple Linear Regression
Model
Dr.S.Rajalingam
L1 Simple Linear Regression by Least Squares
Method
 Methods to Assess the Model
1
Learning Outcomes
At the end of the lesson, the student should be able to
Use the least squares method
Chapter 6 (Week 8)
Point Estimate & Hypothesis
Testing
Dr.S.Rajalingam
L2 Hypothesis testing  Introduction
Learning Outcomes:
At the end of the lesson student should be able
to
Describe the hypothesis testing;
Define what is types of error in hypothesi