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Sampling Methods & Data Types: Statistics Overview

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SAMPLING METHODS
Simple Random – Everyone in the population has an equal chance of selection.
Example: Picking older cardiac patients in Toronto via randomization algorithm after contacting local GPs.
Voluntary & Convenience – Participants are the most accessible or self-selected.
Example: Recruiting the first 40 patients entering a hospital’s emergency department.
Stratified Random – Population divided into subgroups, then sampled randomly within each.
Example: Studying newcomers’ housing experiences by sampling within nationality groups.
Cluster Random – Random sampling within naturally occurring groups.
Example: Surveying patient satisfaction across different hospital departments.
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DATA
CASE=ROW // VARIABLE NAMES=HEADERS // VARIABLE=COLUMN // RELATIONAL DB= links db’s
cases through id# (like merging by ping_id in powerquery)​
Primary Data = Data we collect ex: from survey​
Secondary Data = Data collected by another party ex: stats canada
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VARIABLES​
Qualitative Var / Categorical Var = answers how ?s​
Quantitative Var = Measures numerical values with units ​
Some variables can be both categorical and quantitative
How data are classified depends on Why we are collecting the data​
Identifier Variable = unique identified linked to individual or item in group (ID# or PRODUCT#)​
Nominal Variable = Qualitative vvars used only to name categories ​
Ordinal Variable = When data values can be ordered EX:employees can be ranked according to the
number of days worked in the company.​
Time Series = Variables that are measured at regular intervals over time EX:the share price of the Royal
Bank of Canada at the end of each day for the past year.​
Cross-Sectional Data – Multiple variables measured at the same point in time. Ex:Tracking sales
revenue, customer count, and expenses for each Starbucks location in the past month.
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UNITS​
Some quantitative units indicate how value was measured, the corresponding scale of measurement,
how much of something we have, how far apart two values are​
Other quantitative variables have no units, such as: Number of visits to a web site, or the number of
shares of a company traded in Toronto Stock Exchange
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Variables can be qualitative and quantitative, depending on the purpose of data collection.​
For example:​
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- Age as a quantitative variable – Measured in years to calculate the average age of customers.​
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- Age as a categorical variable – Grouped (Child, Teen, Adult, Senior) to tailor music offers
(folk, jazz, hip-hop, reggae).
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Counts = 1. Summarize qualitative var 2. To measure the amount of things​
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Quantitative variables differ based on whether zero has a defined value:
Interval Scale = No true zero (e.g., 80°F isn’t twice as hot as 40°F).
Ratio Scale = Has a true zero, allowing meaningful comparisons.
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Statistics Process = 0. Determine analysis objectives/goals 1.Data Collection 2.Data Organizing 3.Data
Analysis 4.Results Interpretation 5.Results Presentation
Data Collection Process = 1.Review the analysis 2.Data Identification(What question type) 3.Data
Sources (primary or secondary) 4.Tools 5.Budget+Time​
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Population = whole group
Sample = selected group to study ​
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Subgroup = subsection of a group
Sample Unit = Sample units are the members of the population from which measurements are taken
during sampling
Creating Data Structure amd Data Input Template = 1.Developing surveys/forms 2.Determining
Sample Size 3.Collecting a pilot sample 4. Finalize surveys / forms / tools 5.Distributing surveys / forms
/ tools 6. Receiving Data, Input and start organizing​
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Survey Development = 1.Intro (purpose, who, confidentiality, assurance blah blah blah), 2. Question
Section (multiple choice, open-ended questions, closed ended questions.) 3.Ending section(thank you /
incentives?)
Sampling Errors = Sample does not represent population (wrong respondent) 1.Non sampling errors =
right respondent - wrong question / time / organization
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