Introduction to Statistics for Built Environment
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Questions and Answers

What type of data refers to names and classifications?

  • Qualitative Data (correct)
  • Continuous Data
  • Quantitative Data
  • Discrete Data

Quantitative data can only be discrete and not continuous.

False (B)

What is a variable in the context of statistics?

A characteristic or property of the units being studied that can fluctuate.

Data that is collected from a subset of units in a population is known as __________ data.

<p>primary</p> Signup and view all the answers

Match the following types of data with their definitions:

<p>Qualitative = Data that refers to names and classifications Quantitative = Data that represents counts or measurements Discrete = Data that is counted Continuous = Data that is measured</p> Signup and view all the answers

Which of the following is an example of quantitative data?

<p>Temperature readings (A)</p> Signup and view all the answers

Secondary data involves collecting information from every unit in the population.

<p>False (B)</p> Signup and view all the answers

What determines the quality of a statistical study?

<p>The quality of the data gathered.</p> Signup and view all the answers

What type of data consists of labels or names used to identify an attribute?

<p>Qualitative Data (A)</p> Signup and view all the answers

Quantitative data can be represented in non-numeric terms.

<p>False (B)</p> Signup and view all the answers

Give one example of qualitative data.

<p>Type of vegetation (e.g., groundcover, shrubs, trees)</p> Signup and view all the answers

Quantitative variables can be classified as __________ and continuous.

<p>discrete</p> Signup and view all the answers

Match the following terms with their descriptions:

<p>Qualitative Data = Data represented by labels or names Quantitative Data = Numeric data indicating counts or measurements Discrete Data = Variable having distinct values on a scale Continuous Data = Variable that can take any value within a range</p> Signup and view all the answers

Qualitative data can be ranked and ordered.

<p>False (B)</p> Signup and view all the answers

What is a characteristic of quantitative data?

<p>It is numeric and indicates either 'how many' or 'how much'.</p> Signup and view all the answers

An example of a continuous variable is __________.

<p>height</p> Signup and view all the answers

Which of the following does not represent qualitative data?

<p>Speed (C)</p> Signup and view all the answers

Which scale of measurement includes an identifiable absolute zero point?

<p>Ratio (B)</p> Signup and view all the answers

Ordinal data allows us to say how much more one value is compared to another.

<p>False (B)</p> Signup and view all the answers

Give an example of a nominal variable.

<p>Types of trees</p> Signup and view all the answers

The temperature measured in degrees Celsius is an example of an _____ scale.

<p>interval</p> Signup and view all the answers

Match the following scales of measurement with their appropriate descriptions:

<p>Nominal = Identification, name, categories Ordinal = Ranking without equal intervals Interval = Meaningful differences without absolute zero Ratio = Meaningful differences with absolute zero</p> Signup and view all the answers

Which of the following is a characteristic of qualitative variables?

<p>Assigned to categories or labels (C)</p> Signup and view all the answers

Bivariate analysis involves three or more variables in a data set.

<p>False (B)</p> Signup and view all the answers

What is a common example of ordinal data?

<p>Education level</p> Signup and view all the answers

The analysis that involves only one variable is called _____ analysis.

<p>univariate</p> Signup and view all the answers

Which of the following statements about interval scales is true?

<p>Differences between values are meaningful. (B)</p> Signup and view all the answers

Flashcards

Qualitative Data

Data that identifies categories or labels. It's used to categorize elements based on attributes.

Quantitative Data

Data expressed numerically, representing measurements or counts. It can be ranked and ordered.

Qualitative Variables

Variables that classify data into distinct categories. Examples: Type of vegetation, pass/fail, race.

Quantitative Variables

Variables that involve numerical measurements or counts. Examples: Height, weight, speed.

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Discrete Variables

Quantitative variables that have distinct, separate values that cannot be subdivided. Examples: Number of rooms, defects, IQ score.

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Continuous Variables

Quantitative variables that can have any value within a range, and can be subdivided. Examples: Height, weight, time.

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Qualitative Research

Data collected through observation, interviews, focus groups, etc. It's rich in detail but less structured and harder to generalize.

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Quantitative Research

Data collected through experiments, surveys, and numerical analysis. It's objective, structured, and allows for statistical analysis and generalization.

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Variable

A measurable characteristic or attribute that can vary over time and between individuals in a population.

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Data

A collection of observations or values obtained through reliable sources for a study.

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Primary Data

Information gathered directly from the source, such as through surveys, interviews, or experiments.

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Secondary Data

Information collected by others and made available for analysis, like public records, reports, or historical documents.

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Univariate Data

Data that involves a single variable, such as the average age of students in a class.

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Bivariate Data

Data that examines the relationship between two variables. Examples include the correlation between height and weight or the influence of study hours on exam scores.

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Nominal Scale

The lowest level of measurement, where data is categorized without any order or ranking. It focuses on identifying and grouping items into distinct categories.

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Ordinal Scale

A scale where data can be ranked or ordered, but the intervals between categories are not necessarily equal. It allows comparison of "more" or "less", but doesn't tell you how much more or less.

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Interval Scale

A scale where data can be ordered, and the intervals between values are equal. It allows for comparisons of differences, but zero doesn't represent the absence of the measured quality.

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Ratio Scale

The highest level of measurement, where data can be ordered, intervals are equal, and zero represents the complete absence of the measured quality. Ratios are meaningful.

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Univariate Analysis

A type of analysis where data contains only one variable. It describes the characteristics of the data without looking for relationships between variables.

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Bivariate Analysis

A type of analysis where data contains two variables. It explores the relationship between those two variables, looking for correlations or patterns.

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Multivariate Analysis

A complex type of analysis where data contains more than two variables. It investigates the relationships between all the variables simultaneously to understand their combined effects.

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Study Notes

Introduction to Statistics for Built Environment

  • Course code: AEDF 0623
  • Topic: Variables & Measurement

Contents

  • Definition of variable and data
  • Statistical data source
  • Types of data/variables
  • Levels/scales of measurement of variables
  • Univariate, bivariate, and multivariate analysis
  • Independent and dependent variables

Definition of Variable

  • Any measurable characteristic, attribute, amount, or number
  • A characteristic or property of the units being studied
  • Values may fluctuate over time and between data units in a population
  • Examples: Age, gender, revenue, costs, birth place, capital expenditure, grades, vehicle type, drainage system type

Definition of Data

  • A collection of values or observations from reliable sources
  • Arranged according to observational units (people, samples, populations) and variables
  • Quality of data collected determines the study's quality

Statistical Data Source

  • Direct Method (Primary Data):
    • Collecting data from every unit in a population (census) or a subset (sample)
    • Examples: Surveys, interviews, focus group discussions (FGDs), observational studies, experiments
  • Indirect Method (Secondary Data):
    • Data collected as part of daily processes and record-keeping by organizations
    • Examples: Historical data, public records, books, journals, reports

Types of Data & Variables

  • Qualitative (Categorical/Attribute):
    • Data representing categories/classifications only
    • Examples: Types of vegetation, education level, satisfaction level
  • Quantitative (Numerical):
    • Data representing counts or measurements
    • Discrete: Counted items (e.g., number of rooms, defects per hour, number of days)
    • Continuous: Measured characteristics (e.g., temperature, time, IQ test, CGPA, weight, height, distance)

Qualitative Data

  • Labels/names to identify attributes/characteristics of elements
  • Represented in separate categories
  • Examples: Types of vegetation, exam results (pass/fail), residents' race

Quantitative Data

  • Always numeric, indicating "how many" or "how much"
  • Measurements and counts can be ranked and ordered
  • Examples: Distance, height, weight, speed

Qualitative & Quantitative Data Example

  • Data from the presentation showing various statistics based on qualitative and quantitative variables, such as:
    • Labor force statistics, comparing female labor force participation rates by certificate level or age group
    • Job satisfaction, burnout, intention to leave data, plotted as bar graphs, with percentage based data
    • Business benefits of green building data, shown as bar charts and containing descriptive data points

Discrete & Continuous Variables

  • Discrete: Variables with countable whole numbers (e.g., number of lightbulbs, cars)
  • Continuous: Variables that can take on any value within a range (e.g., height, weight, temperature, distance)

Level/Scales of Measurement of Variables

  • Nominal: Categories/labels only (e.g., color, brand, type)
  • Ordinal: Ordered categories with meaningful differences—ranking (e.g., education level, satisfaction level)
  • Interval: Ordered categories with equal intervals between values, no true zero point (e.g., temperature, IQ test)
  • Ratio: Ordered categories with equal intervals and a true zero point (e.g., weight, height, distance, time)
    • Examples from the presentation demonstrating application of each scale include graphs and descriptions of data used.

Univariate, Bivariate & Multivariate Analysis

  • Univariate: Analysis of a single variable
    • Example: Analyzing the average height of trees of one type
  • Bivariate: Analysis of two variables to determine relationships (correlation) Example: Analyzing how surrounding temperature affects air conditioning usage
  • Multivariate: Analysis of more than two variables to understand relationships
    • Example: How weather, season, soil, etc, affect growth of plants

Independent & Dependent Variables

  • Independent Variable: Manipulated or controlled by the experimenter
  • Dependent Variable: Measured and depends on the independent variable
  • Examples from the presentation show application examples of these concepts through use cases of real-life data analysis

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Lecture 2 Variables & Measurement PDF

Description

This quiz covers the foundational concepts of variables and measurement in the context of statistics for the built environment. It includes definitions of variables and data, types of data, levels of measurement, and different analysis forms. Perfect for students enrolled in the AEDF 0623 course.

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