A continuous random variable: has an unaccountably infinite number of possible values in an interval
between two points a and b. And, it can assume any value in the interval between two points a and b
Session 1 Activity 1.2
1. Read Chapter 1 (An overview of auditing) and answer the following.
1.17
How have corporate collapses influenced the role of auditing in recent years?
1.20
What are the curren
Chapter 13
Hypothesis testing:
Describing a single population
Learning Objectives
LO1
Understand the fundamental concepts of
hypothesis testing
LO2
Set up the null and alternative hypotheses,
and be f
CHAPTER 11
Estimation:
Describing a single population
Chapter outline
11.1 Concepts of estimation
11.2 Estimating the population mean when the
population variance 2 is known
11.3 Estimating the popula
Chapter 10
Sampling distributions
Chapter outline
10.1 Sampling distribution of the sample mean
10.2 Sampling distribution of the sample
proportion
10.3 From here to inference
Learning objectives
LO1
Chapter 9
Statistical inference:
Introduction
Chapter outline
9.1
9.2
9.3
Data type and problem objective
How, when and why of statistical inference
Systematic
approach
to
statistical
inference: a sum
I I2 BUSINESS STATISTICS: AUSTRALIA AND NEW ZEALAND
CD
in your own words, define and give an example
of each of the following statistical terms:
a population
1: sample
c parameter
d statistic
. . .
1
A QUICK REVIEW OF TOPIC 5
2
In Topic 5, we introduced the concepts & techniques
of probability and discussed the concepts of random
variable & discrete random variable.
We then discussed two descr
TOPIC 2: DATA COLLECTION
& PRESENTATION
Chapter 2
TYPES OF DATA, DATA
COLLECTION AND
SAMPLING
Learning objectives
LO1 Describe different types of data
LO2 Understand the primary and secondary
sources
TOPIC 3: DATA COLLECTION &
RESENTATION: GRAPHICAL AND
TABULAER TECHNIQUES FOR
DECSION MAKING
Chapter 3: Graphical descriptive
techniques Nominal data
Learning Objectives
LO1 Understand how to present
TOPIC 3: (CONTINUE)
Chapter 4: Graphical
descriptive techniques
Numerical data
Learning objectives
LO1
Understand how to tabulate and construct
graphs to summarise numerical data
LO2 Understand how t
Session 7 Slides
Confidence Interval Estimation For The Mean
Estimation of
We will begin by assuming is known
We will deal with an unknown later in this
session
Estimation of
Point Estimate for a Pa
Session 10 Slides
Bivariate Data Relationships and
Correlation
1
Bivariate Data
Bivariate Data: Consists of the values of two different response variables that are
obtained from the same population of
Session 3 Slides
Descriptive Analysis &
Presentation of Single-Variable Data
Continued
1
Measures of Central Tendency
Numerical values used to locate the middle of a set of data, or where the
data is
Lecture 2
Descriptive Analysis &
Presentation of Single-Variable Data
1
Lecture Goals
This topic will be presented over two weeks.
Learn how to present and describe sets of data.
Learn measures of cen
Session 9 Slides
Hypothesis Testing of Population Mean
(Sigma Known and Unknown)
Hypothesis test of mean
A Classical Approach:
The assumption for hypothesis tests about mean using a known
: The samp
Session 6 Slides
Sampling Variability
Sampling Distribution of
Sample Means
If the probability distribution is normal we can
make inferences about the population.
Sampling Distribution of
Sample Means
Summation Notation
This section offers an introduction to the use of summation notation. Because
summation notation is used extensively throughout statistics, you should
review this section even if yo
Greek Symbols
Some students may have difficulty with the use of Greek Symbols. To help overcome this problem a
list of Greek Letters with their names and usual statistical meaning is provided.
Large
C
Question 1
A national survey found that 17% Of Australians consume milk with their breakfast.
However, in Victoria, a large milk producer believes that more than 17% of Victorians
consume milk with th