Business
Statistics
Important note:
These study notes have been compiled
personally based on the module outline
for the 2023 academic year and are
merely a summary of the work. They
may include additional content or omit
certain information relevant to other
academic years. Additionally, please be
aware that the document may contain
some errors and spelling mistakes. It is
important to note that these notes are
intended to serve as a supplementary
aid for certain topics and should not
replace the use of textbooks or other
primary study materials. Students are
strongly encouraged to consult their
prescribed academic sources for
accurate and comprehensive
information regarding the module.
, LEARNING UNIT 1 - CHAPTER 1
Terms:
Random variable Any attribute of interest on data which is
collected and analysed.
Sampling unit Object being measured, counted or
observed.
Population parameter Describes characteristics of population if all
population data values are used.
Data Values or outcomes recorded on a random
variable.
Population Collection of all possible data values in
random variable understudy.
Sample statistic. Measure that describes a characteristic of a
sample.
Sample. Subset of data values drawn from a
population.
Symbolic notation:
Statistical measure Sample statistic Population parameter
Mean 𝑥̅
Standard deviation s
Variance 𝑠2 2
Size n
Proportion p
Correlation r
,Components of statistics:
• Descriptive statistics.
Large quantities of data that needs to be organised, summarised and
extracted from essential information.
𝑥𝑖
Sample mean: 𝑛
• Inferential statistics.
Describes the behaviour of a random variable.
Main concern is about the behaviour and characteristics of random variable.
Sees it as a “bigger population picture”.
• Statistic modelling.
Constructs equations between variables related to each other.
Used to estimate and predicts values.
Useful in forecasting decisions.
Data types and measurement scales:
• Qualitative.
Data measurements that fall into represented categories.
• Quantitative.
Data measured on numerical scales.
• Discrete.
A whole number of data. Amount of students.
• Continuous data.
Numbers occurred in interval. Mass of luggage.
Measurement scales:
• Nominal data.
Associated with categorical data.
Only names are meaningful.
Weakest form as there is no numerical properties.
• Ordinal data.
Categorical data.
Implied ranking between different categories or random variables.
Stronger as it has numerical properties.
• Interval data.
Associated with numeric data and quantitative random variables.
• Ratio data.
Real numbers associated with quantitative random variables.
, Data sources:
Primary data:
• Data recorded first time at source with a specific purpose at mind.
• Can be internal or external.
• Advantage:
High quality.
• Disadvantage:
Time consuming.
Expensive.
Secondary data:
• Data already exists in a processed format.
• Previously collected and processed for a purpose other than the problem.
• Internally or externally sourced.
• Advantages:
Access time is relatively short.
Less expensive.
• Disadvantages:
Not problem specific.
Out of date.
Difficult to access.
Data distortion.
Surveys:
• Used to gather primary data through direct questioning of respondents using
questionaries.
• Capture attitudinal types of dat.
• Conducted via:
Personal interviews.
Telephone surveys.
E-surveys.
Statistics
Important note:
These study notes have been compiled
personally based on the module outline
for the 2023 academic year and are
merely a summary of the work. They
may include additional content or omit
certain information relevant to other
academic years. Additionally, please be
aware that the document may contain
some errors and spelling mistakes. It is
important to note that these notes are
intended to serve as a supplementary
aid for certain topics and should not
replace the use of textbooks or other
primary study materials. Students are
strongly encouraged to consult their
prescribed academic sources for
accurate and comprehensive
information regarding the module.
, LEARNING UNIT 1 - CHAPTER 1
Terms:
Random variable Any attribute of interest on data which is
collected and analysed.
Sampling unit Object being measured, counted or
observed.
Population parameter Describes characteristics of population if all
population data values are used.
Data Values or outcomes recorded on a random
variable.
Population Collection of all possible data values in
random variable understudy.
Sample statistic. Measure that describes a characteristic of a
sample.
Sample. Subset of data values drawn from a
population.
Symbolic notation:
Statistical measure Sample statistic Population parameter
Mean 𝑥̅
Standard deviation s
Variance 𝑠2 2
Size n
Proportion p
Correlation r
,Components of statistics:
• Descriptive statistics.
Large quantities of data that needs to be organised, summarised and
extracted from essential information.
𝑥𝑖
Sample mean: 𝑛
• Inferential statistics.
Describes the behaviour of a random variable.
Main concern is about the behaviour and characteristics of random variable.
Sees it as a “bigger population picture”.
• Statistic modelling.
Constructs equations between variables related to each other.
Used to estimate and predicts values.
Useful in forecasting decisions.
Data types and measurement scales:
• Qualitative.
Data measurements that fall into represented categories.
• Quantitative.
Data measured on numerical scales.
• Discrete.
A whole number of data. Amount of students.
• Continuous data.
Numbers occurred in interval. Mass of luggage.
Measurement scales:
• Nominal data.
Associated with categorical data.
Only names are meaningful.
Weakest form as there is no numerical properties.
• Ordinal data.
Categorical data.
Implied ranking between different categories or random variables.
Stronger as it has numerical properties.
• Interval data.
Associated with numeric data and quantitative random variables.
• Ratio data.
Real numbers associated with quantitative random variables.
, Data sources:
Primary data:
• Data recorded first time at source with a specific purpose at mind.
• Can be internal or external.
• Advantage:
High quality.
• Disadvantage:
Time consuming.
Expensive.
Secondary data:
• Data already exists in a processed format.
• Previously collected and processed for a purpose other than the problem.
• Internally or externally sourced.
• Advantages:
Access time is relatively short.
Less expensive.
• Disadvantages:
Not problem specific.
Out of date.
Difficult to access.
Data distortion.
Surveys:
• Used to gather primary data through direct questioning of respondents using
questionaries.
• Capture attitudinal types of dat.
• Conducted via:
Personal interviews.
Telephone surveys.
E-surveys.
