Sample Class Test Questions for Week 8
Question 1
The Glendale Steel Company manufactures steel bars. If the production
process is working properly, it turns out steel bars with mean length of at least
855mm with a standard deviation of 65mm (as determine
Business Statistics
Hypothesis Testing Mean (Critical Value Approach)
Tutorial 08:
Part A
A8.1
In hypothesis testing, the level of significance () is a very important concept.
Explain what this means.
is the probability of rejecting the null hypothesis w
Business Statistics
Tutorial 8
S2_2015
Hypothesis Testing Mean (Critical Value Approach)
Part A
A8.1
In hypothesis testing, the level of significance () is a very important concept.
Explain what this means.
_
_
_
_
_
_
A8.2
State the Helpful Hints for hyp
Business Statistics
Tutorial 6
Part A:
For all your answers, please remember to do the following:
1. Draw curves
2. State the distribution
3. Define the variable
A6.1
An automatic machine in a manufacturing process is operating properly if the
lengths of
620-371: Linear Models
Practice Class 8
28th April, 2009
1. It is known that R( 1 | 2 ) has a noncentral 2 distribution with r degrees
of freedom and noncentrality parameter
=
1 T T
T
T
X [X(X T X)1 X T X2 (X2 X2 )1 X2 ]X.
2 2
Show that if H0 : 1 = 0 is
One-factor tests
Two-factor models
ANCOVA
Linear Models: R Examples The less than full
rank model: inference
Linear Models: R Examples The less than full rank model: inference
Yao-ban Chan
One-factor tests
Two-factor models
ANCOVA
One-factor tests
q
80
85
Adequacy
Subvectors
Partial/sequential
Nonzero tests
GL Hypothesis
Linear Models: R Examples The full rank
model: inference
Linear Models: R Examples The full rank model: inference
Yao-ban Chan
Adequacy
Subvectors
Partial/sequential
Nonzero tests
GL Hypot
Business Statistics
U
Tutorial 09:
S2_2015
U
Part A
A9.1
Eighteen per cent of multinational companies provide an allowance for personal
long-distance calls for executives living overseas. Suppose a researcher thinks that
multinational companies are having
< Full rank
Reparametrisation
Conditional inverses
Normal equations
Estimability
R
2
Intervals
Linear Models: R Examples The less than full
rank model: estimation and estimability
Linear Models: R Examples The less than full rank model: estimation and est
Random vectors
Quadratic forms
Distributions
Distribution of quadratic forms
Independence
Linear Models: R Examples Random vectors
Linear Models: R Examples Random vectors
Yao-ban Chan
Random vectors
Quadratic forms
Distributions
Distribution of quadratic
Matrix manipulations
LA Results
Quadratic forms
Linear Models: R Examples Linear algebra
Linear Models: R Examples Linear algebra
Yao-ban Chan
Matrix manipulations
LA Results
Quadratic forms
Dening a matrix
> A <- c(1, 2, 0, 2, 3, -1, 0, -1, 8)
> dim(A) <
620-371: Linear Models
Practice Class 11
19th May, 2009
1. Let H0 : C = 0 be testable. Show that C(X T X)c C T is unique, i.e.
does not depend on the choice of conditional inverse for X T X. (Hint: Let
(X T X)c be another conditional inverse for X T X and
Basics
Identity and inverse
Orthogonality
Eigenthings
Rank
Idempotence
Trace
Results
Quadratic forms
Linear Models: Linear Algebra
Linear Models: Linear Algebra
Yao-ban Chan
Basics
Identity and inverse
Orthogonality
Eigenthings
Rank
Idempotence
Trace
Resu
620-371: Linear Models
Practice Class 11
19th May, 2009
1. Let H0 : C = 0 be testable. Show that C(X T X)c C T is unique, i.e.
does not depend on the choice of conditional inverse for X T X. (Hint: Let
(X T X)c be another conditional inverse for X T X and
620-371: Linear Models
Practice Class 10
12th May, 2009
In a manufacturing plant, lters are used to remove pollutants. We are
interested in comparing the lifespan of 5 dierent types of lters. Six lters of
each type are tested, and the time to failure in h
620-371: Linear Models
Practice Class 9
5th April, 2009
1. Show that in a mutually orthogonal full rank model, X T X is diagonal.
Calculate (X T X)1 (expressed in terms of the elements of the X matrix).
Solution: The (i, j)th element of X T X is
T
Xki Xkj
Parameter estimation
What if?
Other estimation
Intervals
Linear Models: R Examples The full rank
model: estimation
Linear Models: R Examples The full rank model: estimation
Yao-ban Chan
Parameter estimation
What if?
Other estimation
Intervals
Parameter es
< Full rank
Reparametrization
Conditional inverses
Normal equations
Estimability
Estimability thms
2
Intervals
Linear Models: The less than full rank model
estimation and estimability
Linear Models: The less than full rank model estimation and estimabili
Model adequacy
Subvector of
Partial & sequential
Nonzero tests
General linear hypothesis
Orthogonality
Linear Models: The full rank model inference
Linear Models: The full rank model inference
Yao-ban Chan
Model adequacy
Subvector of
Partial & sequentia
LA ORGANIZACIN EMPRESARIAL Y EL PAPEL DE LAS ASISTENTES EJECUTIVAS
Una organizacin, a cualquier nivel, es un sistema y funciona como un todo
integrado, en el cual las partes son interdependientes. Una empresa, como
sistema, est formada por un conjunto coh
1
ETF5900
Business
Statistics
2
INFERENCE I:
Lecture Six
Please remember to bring Statistical Tables to this Lecture
Confidence Interval Estimation
Mean
Proportion
Textbook references:
Berenson et al Basic Business Statistics 3e,
Chapter 8 Sections 8.1, 8
1
ETF5900
Business
Statistics
2
Lecture Five
Please remember to bring Statistical Tables to this Lecture
Sampling and Sampling Distributions
Introduction to Confidence Interval Estimation
Textbook references:
Berenson et al Basic Business Statistics 3e,
C
1
ETF5900
Business
Statistics
INFERENCE II:
2
Lecture Seven
Please remember to bring Statistical Tables to this Lecture
Hypothesis Testing:
Mean(for s known and s unknown)
Using:
Critical Value Approach
Textbook references:
Berenson et al Basic Business
1
ETF5900
Business
Statistics
2
Lecture Nine Part I
Introduction to Correlation
and Regression Analysis
Textbook references:
Berenson et al Basic Business Statistics 3e,
Chapter 12 Sections 12.1-12.3, excluding most hand
calculations (See lecture for deta
Tutorial 07: Confidence Interval Estimation
Part A: Use Excel for Part A
For all your answers, please remember to do the following:
1. Draw curves
2. State the distribution
3. Define the variable
A7.1
Probabilities and critical values associated with the
Business Statistics
2015 Semester 2
Week 09: Correlation & Regression Analyses
Pre-Class Exercises
Week 09 is the second experience of our flipped classroom approach.
The activities associated with the Week 09 Correlation & Regression Analysis topic
consi
U
Class Test 1: Test Information
1. The class test will be held during tutorials in week 8, i.e. week
commencing 14 September, 2015.
2. Please refer to the Class Test Folder under the section for week 7 (and
8) on Moodle. This contains all information per
Sample Class Test Questions for Week 8
Question 1
The Glendale Steel Company manufactures steel bars. If the production
process is working properly, it turns out steel bars with mean length of at least
855mm with a standard deviation of 65mm (as determine
Week 8:Hypothesis Test for Mean (P value approach) and
Proportion
Textbook questions for further practice
Chapter 9:
Page 291: 9.24 -9.32
Page 296-297: 9.37-9.45
Page 302: 9.55-9.57
Page 306: 9.62-9.69
(Ignore any reference to the Six-step method. Use lec
Full rank model
Parameters
Variance
ML
Suciency
Intervals
Prediction
Joint intervals
Generalisations
Linear Models: The full rank model estimation
Linear Models: The full rank model estimation
Yao-ban Chan
Full rank model
Parameters
Variance
ML
Suciency
I
ETF5900 Business Statistics
T3_S1, 2017
ETF5900 TUTORIAL 3 SOLUTIONS
PART A (To be completed for homework):
A3.1
The data required for this question is provided in Bulbs.xls. It is available in the Tutorial
Material Folder under the Week 3 section on Mood