Chapter 1

Question 1

What is Statistics?

Answer 1

Statistics is the sciences of conducting studies to collect, organise, summarise, analyse, present, interpret and draw conclusions from data

Question 2

What is Data?

Answer 2

Data is any value either observation or measurement that has been collected

Question 3

What is Variable?

Answer 3

Variable is a characteristic or attribute that can assume different values. These values are data.

Question 4

What is a Population?

Answer 4

A population is a complete collection of measurements, outcomes, objects or individuals under study.

Question 5

What is a Sample?

Answer 5

A sample is a subset of the population that is being observed.

Question 6

What is a Parameter?

Answer 6

Parameter is a numerical value that represents a certain population characteristic.

Question 7

What is a Statistic?

Answer 7

Statistic is a numerical value that represents a certain sample characteristic.

Question 8

Symbol of mean ( average ) for parameter?

Answer 8

μ

Question 9

Symbol of variance for parameter?

Answer 9

σ²

Question 10

Symbol of standard deviation for parameter?

Answer 10

σ

Question 11

Symbol of proportion ( sample ) for parameter?

Answer 11

π

Question 12

Symbol of mean ( average ) for statistic?

Answer 12

x̄

Question 13

Symbol of variance for statistic?

Answer 13

s²

Question 14

Symbol of standard deviation for statistic?

Answer 14

s

Question 15

Symbol of proportion for statistic?

Answer 15

p

Question 16

What is Descriptive Statistics?

Answer 16

To describe the characteristics of the sample and determine whether the sample represents the target population by comparing the sample statistic and population parameter.

Question 17

What is Inferential Statistics?

Answer 17

To describe infer, estimate, approximate the characteristics of the target population and draw a conclusion for the data obtained from the sample.

Question 18

What is the Statistical Problem-Solving Methodology?

Answer 18

1. Identify the problem or opportunity
2. Decide on the method of data collection
3. Collect the data ( sampling techniques )
4. Classify and summarise the data
5. Present and analyse the data
6. Make the decision and conclusion

Question 19

What are the types of sampling techniques?

Answer 19

Non-probability Sampling and Probability Sampling

Question 20

What are the types of Non-probability Sampling?

Answer 20

Judgement, Voluntary and Convenience

Question 21

What are the types of Probability Sampling?

Answer 21

Random, Systematic, Cluster and Stratified

Question 22

What is Judgement Sampling?

Answer 22

Data is selected based on the opinion of one or more experts.

Question 23

what is Voluntary Sampling?

Answer 23

Questions are posed to the public. The resulting sample tends to over represent individuals who have strong opinions.

Question 24

What is Convenience Sampling?

Answer 24

The data selected is an “easy sample”, haphazard or accidental sampling. The researcher obtains units or people who are most conveniently available.

Question 25

What is Random Sampling?

Answer 25

Each data is numbered, then the data is selected using a chance or a random method such as a random number. Each has an equal chance to be selected as a sample. Random samples can be selected with or without replacement.

Question 26

What is Systematic Sampling?

Answer 26

A set of data is numbered from 1 to N. The first data is selected randomly between number 1 and k where k=N/n and n sample size. The next number is selected every k interval to produce n samples.

Question 27

What is Stratified Sampling?

Answer 27

The population is divided into groups according to some characteristics that are important to the study and then the sample is selected from each group using random or systematic sampling. The characteristics of the population are homogeneous within each group but heterogeneous among the groups.

Question 28

What is Cluster Sampling?

Answer 28

The population is divided into groups or clusters, then some of those clusters are randomly selected and all members of those selected clusters are chosen. Cluster sampling can reduce cost and time. Each cluster has heterogeneous characteristics but has homogeneous characteristics among the clusters.

Question 29

What is Raw Data?

Answer 29

Data that has been collected during the sampling process.

Question 30

What is Data Array?

Answer 30

An arrangement of data items in either ascending or descending order.

Question 31

What are the types of data?

Answer 31

Qualitative (Categorical/Attributes) and Quantitative (Numerical)

Question 32

What is Qualitative Data?

Answer 32

Data that refers to classification name according to some characteristic or attribute. Data is classified using code numbers.

Question 33

What is Quantitative Data?

Answer 33

Data can be counted or measured. Data can be ordered or ranked.

Question 34

What is Nominal Data?

Answer 34

The values cannot be ranked. Examples: Gender, race, citizenship, colour or etc

Question 35

What is Ordinal Data?

Answer 35

The values can be ranked, like scale is used. Examples: Feeling (dislike/like), colour (dark-bright), etc

Question 36

What is Discrete Data (Internal-Level) ?

Answer 36

The values can be counted and finite. Examples: Number of students, number of cats, etc

Question 37

What is Continuous Data (Ratio-level) ?

Answer 37

The values can be placed within two specified values, obtained by measuring, have boundaries and shall be rounded to require decimal places. Examples: Weight, age, salary, temperature, etc

Question 38

What is Frequency Distribution?

Answer 38

The organisation of raw data in table form, using classes and frequencies.