Grade 12 Data Management: Exam
MDM4U – Grade 12 Data Management – Exam
Unit 1: One Variable Analysis
Types of Data
Numerical Data
Discrete: consists of whole numbers
Ie. Number of trucks.
Continuous: measured using real numbers
Ie, Measuring temperature.
Categorical Data: cannot be qualitatively measured
Nominal: Data which any order presented makes sense
Ie, Eye Colour, Hair Colour.
Ordinal Data: better if sorted or ordered
Ie, Date and Time, scalar options
Collecting Data
Primary: collected by yourself
Secondary: collected by someone else
Organizing Data
Micro Data: information about an individual
Aggregate Data: grouped data about a group; summarized data.
Data collection
Observational Data: group of people by characteristic, then observe
Group by adult/children then look at sunlight’s effect on them
Experimental Data: create groups and impose some treatment on them
Create experimental groups then apply placebo drug treatments on them.
Other Terms
Population: entire group of people being studied
Sample: the part of the population being studied
Inference: conclusion made about the population based on the sample
Binary Data: only 2 choices/outcomes
Non-Binary: more than 2 outcomes
Sampling Techniques
Characteristics of a good sample
-Each person must have an equal chance to be in the sample
-Sample must be vast enough to represent
Simple Random: each member has equal chance of being selected
Ie, picking members randomly apartments
Sequential Random: go through population sequentially and select members
Ie, Selecting every 5th person
Stratified Sampling: a strata is a group of people that share common charactoristics
Constraints the proportion of members in the strata from the population in the sample
Ie, Each strata is represented based on their proportion in the population
Cluster Sampling: random sample of 2 representative group
Ie, picking 1 floor of people and survey them
Multi-Stage Sampling: several levels of sampling
Ie, Randomly
Unit 1: One Variable Analysis
Types of Data
Numerical Data
Discrete: consists of whole numbers
Ie. Number of trucks.
Continuous: measured using real numbers
Ie, Measuring temperature.
Categorical Data: cannot be qualitatively measured
Nominal: Data which any order presented makes sense
Ie, Eye Colour, Hair Colour.
Ordinal Data: better if sorted or ordered
Ie, Date and Time, scalar options
Collecting Data
Primary: collected by yourself
Secondary: collected by someone else
Organizing Data
Micro Data: information about an individual
Aggregate Data: grouped data about a group; summarized data.
Data collection
Observational Data: group of people by characteristic, then observe
Group by adult/children then look at sunlight’s effect on them
Experimental Data: create groups and impose some treatment on them
Create experimental groups then apply placebo drug treatments on them.
Other Terms
Population: entire group of people being studied
Sample: the part of the population being studied
Inference: conclusion made about the population based on the sample
Binary Data: only 2 choices/outcomes
Non-Binary: more than 2 outcomes
Sampling Techniques
Characteristics of a good sample
-Each person must have an equal chance to be in the sample
-Sample must be vast enough to represent
Simple Random: each member has equal chance of being selected
Ie, picking members randomly apartments
Sequential Random: go through population sequentially and select members
Ie, Selecting every 5th person
Stratified Sampling: a strata is a group of people that share common charactoristics
Constraints the proportion of members in the strata from the population in the sample
Ie, Each strata is represented based on their proportion in the population
Cluster Sampling: random sample of 2 representative group
Ie, picking 1 floor of people and survey them
Multi-Stage Sampling: several levels of sampling
Ie, Randomly