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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 selecting provinces, random cities, then random people. Voluntary Response Samples: invite members of the entire population to participate in the survey Ie, Sending the survey to everyone in the hotel

Convenience Sample: easily accessible members are selected
Ie, Asking people at the mall who walks closest to you
Types of Bias

Good survey Questions are simple, specific, ethical, free of bias, and respects privacy Survey questions should prevent jargon, abbreviations, negatives, leading questions, and insensitivity Sampling Bias: occurs when the chosen sample doesn’t reflect the population Ie, Asking basketball players about math issues

Non-Response Bias: occurs when particular groups are under-represented in a survey because they chose not to participate. Ie, when respondents don’t respond, it leads the surveyor to make up their own thoughts Measurement Bias: occurs when the data collection method consistently under- or overestimates a characteristic of the population Leading questions also cause data over/under estimation

Ie, police radar gun measuring for average speed of the road Response Bias: when participants in a survey give false or misleading answers Question quality might lead to response bias
Ie, A teacher asks class to raise their hands if they have completed their homework

Unit 2: Two Variable Analysis

Correlation
Scatter Plots graph data and is used to determine if there is a relation between the 2 variables Linear Correlation: changes in one variable tend to be proportional to changes in other variables The stronger the correlation, the more closely the data points cluster around the line of best fit. Correlation Coefficient ( r ): a value between -1 and 1 that provides a measure of how closely data points cluster around the line of best fit. -1 - -0.62: negative, strong correlation

-0.61 - -0.33: negative, moderate correlation
-0.32 - 0: negative, weak correlation
0 – 0.32: positive, weak correlation
0.33 – 0.61: positive, moderate...