Week 1 - Reading

Question 1

Decisions are…

Answer 1

• Often based on limited information.
• An investor does not know with certainty whether financial markets will be buoyant, steady or depressed

Question 2

What is population?

Answer 2

• A population is a complete set of all items that interest an investigator.
• Population size, N, can be very large or even infinite

Question 3

What is a sample?

Answer 3

• A sample is an observed subset of a population with sample size given by ‘n’.

Question 4

Example of a population?

Answer 4

• All potential buyers of a new product
• All stocks traded on the LSE
• All registered voters in a country or region

Question 5

What is random sampling?

Answer 5

• Simple random sampling is a procedure use to select a sample of ‘n’ objects from a population in such a way that each member of the population is chosen strictly by chance
• This method is so common that the adjective ‘simple’ is usually dropped

Question 6

What is systematic sampling?

Answer 6

• Involves the selection of every ‘j’th item in the population, where ‘j’ is the ratio of the population size ‘N’ to the desired sample size, ‘n’; that is j=N/n

Question 7

Give me an example of systematic sampling?

Answer 7

• Suppose that a sample size of 100 is desired and the population consists of 5,000 names in alphabetical order, then j=50. Randomly select a number from 1-50, your number is 20, giving the systematic sample of elements numbered 20, 70, 120, 170, 220, and so forth, until all 100 items are sampled
• Systematic sampling reduces bias and they provide a good representation of the population if there is no cynical variation in the population

Question 8

What is a parameter?

Answer 8

A numerical measure that describes a specific characteristic of a population

Question 9

What is a statistic?

Answer 9

A statistic is a numerical measure that describes a specific characteristic of a sample

Question 10

What is the key thing you always have to remember when it comes to parameters and statistics?

Answer 10

We must realise that some element of uncertainty will always remain, as we do not know the exact value of the parameter.
That is, when a sample is taken from a population, the value of the population parameter will not be able to be known precisely

Question 11

What is sampling error?

Answer 11

Results from the fact that information is available on only a small subset of all the population members

Question 12

What are nonsampling errors?

Answer 12

In practical analyses, there is the possibility of an error unconnected with the kind of sampling procedure used
e.g:
- The population actually sampled is not the relevant one
- Survey subjects may give inaccurate answers
- There may be no response to survey questions

Question 13

What are descriptive statistics?

Answer 13

Focus on graphical and numerical procedures that are used to summarise and process data

Question 14

What are inferential statistics?

Answer 14

Focus on using the data to make predictions, forecasts, and estimates to make better decisions

Question 15

What is a variable?

Answer 15

A specific characteristic of an individual or object

Question 16

What are categorical variables?

Answer 16

• Produce responses that belong to groups or categories
• For example, responses to yes/no questions are categorical - ‘are you a business major - yes’

Question 17

What are numerical variables?

Answer 17

variables that have values that are numbers and represent something that can be measured or counted

Question 18

What are discrete numerical variables?

Answer 18

• May, but do not necessarily have a finite number of samples
• The most common type of discrete numerical variable produces a response that comes from a counting process. e.g the number of students enrolled in a class

Question 19

What is a continuous numerical variable?

Answer 19

• These may take on any value within a given range of real numbers and usually arises from a measurement (not a counting) process
• Examples of continuous numerical variables include the weight of a cereal box or the time to run a race. In each case, the value could deviate within a certain amount

Question 20

What is qualitative data?

Answer 20

• There is no measurable meaning to the ‘difference’ in numbers
• For example, one football player is assigned the number 7 and another player is number 10 - we cannot conclude that number 10 will play better than number 7

Question 21

What is quantitative data?

Answer 21

• There is a measurable meaning to the difference in numbers
• When one student scores 90 on an exam and the other scores 45, the difference is measurable and meaningful

Question 22

What can we use to describe categorical data?

Answer 22

• We can use frequency distribution tables and graphs such as bar charts, pie charts and pareto diagrams

Question 23

What is a frequency distribution?

Answer 23

• A frequency distribution is a table used to organise data
• The left column includes all possible responses on a variable being studied
• The right column is a list of the frequencies, or number of observations for each class

Question 24

When is a bar chart most likely used?

Answer 24

If our intent is to draw attention to the frequency of each category, then a bar chart is likely to be used

Question 25

What happens when we need to describe relationships between categorical and ordinal variables?

Answer 25

These situations are usually described by cross tables and pictured by component or cluster bar charts

Question 26

When is a pie chart most likely used?

Answer 26

• If we want to draw attention to the proportion of frequencies in each category, then we can use a pie chart to predict the division of a whole into its constituent parts

Question 27

What is a pareto diagram?

Answer 27

• A pareto diagram is a bar chart that displays the frequency of defect causes
• The bar at the left indicates the most frequent cause and the bars to the right indicate causes with decreasing frequencies

Question 28

What is the 80-20 rule?

Answer 28

• A general observation that 80% of outcomes or results come from 20% of causes or inputs.
• For example, a student might think that 80% of the work in a group project was completed by 20% of the group.
• The 80% mark on a pareto diagram is often marked out. This is because, the 80% line indicates the ‘vital few’ categories, which are the causes that contribute to 80% of the problems