STATS
CHAPTER 1
DEFINING AND COLLECTIONG DATA
STATISTICS: methods that analyse the data of the variables of interest.
DESCRIPTION STATISTICS: methods of organising, summarising, and presenting data in an
informative and convenient way.
INFERENTIAL STATISTICS: methods used to make a conclusion about a characteristic of a
population, based on a smaller sample of the population.
CLASSIFYING VARIABLES:
Categorical
- Qualitative
- (blue / green)
- “yes” or “no”
Numerical
- Quantitative
- Can be counted or measured.
- Discrete – countable
- Continuous – measuring process / uncountable
MEASUREMENT SCALES:
Nominal scale – classifies categorical data into distinct categories in which no ranking is
implied.
Ordinal scale – classifies categorical data into distinct categories in which ranking is implied.
Numerical variables use interval scale or ratio scale:
Interval scale – ordered scale in which the difference between measurements is a
meaningful quantity (no true zero)
Ratio scale – ordered scale in which the difference between the measurements is a
meaningful quantity (have a true zero point)
TYPES OF VARIABLES:
, POPULATION VS SAMPLE:
Population – all items or individuals of interest that you seek to study.
Sample – a portion of the population of interest / items or individuals.
WHY SAMPLING:
- Less time consuming
- Less costly
- Less cumbersome and more practical
Population parameter – summarises the value of a specific variable for a population.
Sample statistic – summarises the value of a specific variable for sample data.
SOURCES OF DATA:
Primary sources: data collected by you (directly)
Secondary sources: data not collected by you but uses it.
SAMPLING PROCESSS BEGINS WITH A SAMPLING FRAME:
- Listing items that make up the population.
- Directories or maps
TYPES OF SAMPLES:
NONPROBABILITY SAMPLE:
Nonprobability sample – items included are chosen without regard to their probability of
occurrence.
Convenience sampling – items are selected based only on the fact that they are easy,
inexpensive, or convenient.
Judgement sample – you get the options of pre-selected experts on the subject matter.
PROBABILITY SAMPLE:
Probability sample – items are chosen on the basis of known probabilities.
CHAPTER 1
DEFINING AND COLLECTIONG DATA
STATISTICS: methods that analyse the data of the variables of interest.
DESCRIPTION STATISTICS: methods of organising, summarising, and presenting data in an
informative and convenient way.
INFERENTIAL STATISTICS: methods used to make a conclusion about a characteristic of a
population, based on a smaller sample of the population.
CLASSIFYING VARIABLES:
Categorical
- Qualitative
- (blue / green)
- “yes” or “no”
Numerical
- Quantitative
- Can be counted or measured.
- Discrete – countable
- Continuous – measuring process / uncountable
MEASUREMENT SCALES:
Nominal scale – classifies categorical data into distinct categories in which no ranking is
implied.
Ordinal scale – classifies categorical data into distinct categories in which ranking is implied.
Numerical variables use interval scale or ratio scale:
Interval scale – ordered scale in which the difference between measurements is a
meaningful quantity (no true zero)
Ratio scale – ordered scale in which the difference between the measurements is a
meaningful quantity (have a true zero point)
TYPES OF VARIABLES:
, POPULATION VS SAMPLE:
Population – all items or individuals of interest that you seek to study.
Sample – a portion of the population of interest / items or individuals.
WHY SAMPLING:
- Less time consuming
- Less costly
- Less cumbersome and more practical
Population parameter – summarises the value of a specific variable for a population.
Sample statistic – summarises the value of a specific variable for sample data.
SOURCES OF DATA:
Primary sources: data collected by you (directly)
Secondary sources: data not collected by you but uses it.
SAMPLING PROCESSS BEGINS WITH A SAMPLING FRAME:
- Listing items that make up the population.
- Directories or maps
TYPES OF SAMPLES:
NONPROBABILITY SAMPLE:
Nonprobability sample – items included are chosen without regard to their probability of
occurrence.
Convenience sampling – items are selected based only on the fact that they are easy,
inexpensive, or convenient.
Judgement sample – you get the options of pre-selected experts on the subject matter.
PROBABILITY SAMPLE:
Probability sample – items are chosen on the basis of known probabilities.
