QUAT6221 LU1
QUAT6221 LU1 – Intro to stats
Chapter 1 – Statistics in Management
1.1 Introduction
Management decision making
Decision making requires data to be analysed.
Data analysis is initiated when there is a clear problem or question that needs to be
addressed in a verifiable way.
Each problem or question has critical attributes / key performance indicators that need to be
identified and defined – this is the first step in data analysis.
Information
Info must be timely, accurate, relevant, adequate and easily accessible. Info needs to be
generated from data.
Data – LO1 (Explain the meaning)
Data consists of individual values each convey little useful and usable info to management.
Statistics – LO1 (Explain the meaning)
Statistics can be defined as the science of data. It is a set of mathematically based tools
and techniques to transform raw (unprocessed) data into a few summary measures that
represent useful and usable information to support effective decision-making. These
summary measures are used to describe profiles (patterns) of data, produce estimates, test
relationships between sets of data and identify trends in data over time.
Stats can be seen as an evidence-based information generator. It provides an objective
basis on which a management problem/question can be addressed with confidence once the
statistical information has been correctly interpreted by the decision-maker.
Table – Statistical analysis in management decision-making
Input Process Output Benefit
Management
Data Statistical analysis Information
decision-making
[raw values] [transformational [statistical summary
process] measures]
[relationship,
patterns, trends]
Management decision support system
1
, QUAT6221 LU1
Stats help managers:
- Recognize when statistics can enhance decision-making
- Use tools like Excel for basic statistical analysis
- Interpret numerical management reports effectively
- Critically assess statistical findings to avoid misleading data
- Understand statistical methods to initiate research studies
- Communicate clearly with statistical analysts
We want a manager to be an active participant rather than a passive observer when it
comes to stats, reports and analysis.
1.2 The Language of Statistics – LO2
A Random Variable = any attribute of interest on which data are collected and analysed.
Data = actual values (numbers) or outcomes recorded on a random variable
Random variables and their data examples:
- the travel distances of delivery vehicles (data: 34 km, 13 km, 21 km)
- the daily occupancy rates of hotels in Cape Town (data: 45%, 72%, 54%)
- the duration of machine downtime (data: 14 min, 25 min, 6 min)
- brand of coffee preferred (data: Nescafe, Jacobs, Frisco)
A Sampling unit = object being measured, counted, or observed with respect to random
variable under study
This could be a consumer, an employee, a household, a company or a product. More
than one random variable can be defined for a given sampling unit. For example, an
employee could be measured in terms of age, qualification and gender.
A Population = collection of all possible data values that exist for the random variable under
study
A study on a hotel occupancy levels (the random variable) in cape town only, all
hotels in cape town would represent the target population
A Population Parameter = measure that described a characteristic of a population. A
population average is a parameter, so is a population proportion. It is called a parameter if it
uses all the population data values to compute its value.
A Sample = subset of data values drawn from a population. Samples are used bc it is often
not possible to record every data value of the population (due to cost, time and item
destruction)
A sample of 25 hotels in cape town is selected to study hotel occupancy levels
A sample of 50 savings account holders from each of four national banks is selected
to study the profile of their age, gender and savings account balances.
2
QUAT6221 LU1 – Intro to stats
Chapter 1 – Statistics in Management
1.1 Introduction
Management decision making
Decision making requires data to be analysed.
Data analysis is initiated when there is a clear problem or question that needs to be
addressed in a verifiable way.
Each problem or question has critical attributes / key performance indicators that need to be
identified and defined – this is the first step in data analysis.
Information
Info must be timely, accurate, relevant, adequate and easily accessible. Info needs to be
generated from data.
Data – LO1 (Explain the meaning)
Data consists of individual values each convey little useful and usable info to management.
Statistics – LO1 (Explain the meaning)
Statistics can be defined as the science of data. It is a set of mathematically based tools
and techniques to transform raw (unprocessed) data into a few summary measures that
represent useful and usable information to support effective decision-making. These
summary measures are used to describe profiles (patterns) of data, produce estimates, test
relationships between sets of data and identify trends in data over time.
Stats can be seen as an evidence-based information generator. It provides an objective
basis on which a management problem/question can be addressed with confidence once the
statistical information has been correctly interpreted by the decision-maker.
Table – Statistical analysis in management decision-making
Input Process Output Benefit
Management
Data Statistical analysis Information
decision-making
[raw values] [transformational [statistical summary
process] measures]
[relationship,
patterns, trends]
Management decision support system
1
, QUAT6221 LU1
Stats help managers:
- Recognize when statistics can enhance decision-making
- Use tools like Excel for basic statistical analysis
- Interpret numerical management reports effectively
- Critically assess statistical findings to avoid misleading data
- Understand statistical methods to initiate research studies
- Communicate clearly with statistical analysts
We want a manager to be an active participant rather than a passive observer when it
comes to stats, reports and analysis.
1.2 The Language of Statistics – LO2
A Random Variable = any attribute of interest on which data are collected and analysed.
Data = actual values (numbers) or outcomes recorded on a random variable
Random variables and their data examples:
- the travel distances of delivery vehicles (data: 34 km, 13 km, 21 km)
- the daily occupancy rates of hotels in Cape Town (data: 45%, 72%, 54%)
- the duration of machine downtime (data: 14 min, 25 min, 6 min)
- brand of coffee preferred (data: Nescafe, Jacobs, Frisco)
A Sampling unit = object being measured, counted, or observed with respect to random
variable under study
This could be a consumer, an employee, a household, a company or a product. More
than one random variable can be defined for a given sampling unit. For example, an
employee could be measured in terms of age, qualification and gender.
A Population = collection of all possible data values that exist for the random variable under
study
A study on a hotel occupancy levels (the random variable) in cape town only, all
hotels in cape town would represent the target population
A Population Parameter = measure that described a characteristic of a population. A
population average is a parameter, so is a population proportion. It is called a parameter if it
uses all the population data values to compute its value.
A Sample = subset of data values drawn from a population. Samples are used bc it is often
not possible to record every data value of the population (due to cost, time and item
destruction)
A sample of 25 hotels in cape town is selected to study hotel occupancy levels
A sample of 50 savings account holders from each of four national banks is selected
to study the profile of their age, gender and savings account balances.
2
