12 Questions
Which type of data can be measured and assumes infinite values within a specified interval?
Continuous data
What is the main difference between nominal data and ordinal data?
Ordinal data has an implied ordering, while nominal data does not.
Which of the following is an example of discrete data?
The number of students in a class
Which type of data is collected as labels or names without any intrinsic ordering?
Nominal data
Which of the following is an example of continuous data?
The age of a person in years, months, and days
Which type of data is the lowest level of measurement and cannot be measured?
Nominal data
Which of the following is NOT a characteristic of interval data?
It has a true zero point
Which sampling technique involves selecting every kth member of the population with the starting point determined at random?
Systematic sampling
In the data analysis process, what is the step that involves preventing and correcting errors, such as incomplete data, duplicates, or mistakes?
Data cleaning
Which of the following is NOT a measure of central tendency commonly used in data analysis?
Variance
Which level of measurement is higher than ordinal data and allows for the ranking of variables?
Interval data
In cluster sampling, what is selected instead of individual members of the population?
Clusters
Study Notes
Types of Data
- Quantitative Data: Numerical in nature, allowing for arithmetic operations, e.g. number of students in a class, numerical grade of students, number of heartbeats in a specific time.
- Discrete Data: Assumes only exact values, obtained by counting, e.g. number of siblings, total houses in a barangay, number of families.
- Continuous Data: Assumes infinite values within a specified interval, obtained by measurement, e.g. height, average grade, age (in years, months, weeks, days, hours, minutes, seconds).
- Nominal Data: Collected labels or names with no intrinsic ordering, e.g. brand of laundry soap, name of dishes, name of fast-food chain, local celebrities.
- Ordinal Data: Similar to categorical variables, with implied ordering, e.g. academic rank, beauty title, military rank, level of agreement (Strongly Agree, Agree, Disagree, Strongly Disagree).
- Interval Data: Similar to ordinal variables, but with equally spaced intervals, e.g. time, age in years, number of family members.
Sampling Techniques
- Simple Random Sampling: All members of the population have a chance of being included in the sample.
- Systematic Sampling: Selecting every kth member of the population with a starting point determined at random.
- Stratified Random Sampling: Subdividing the population into smaller groups or strata, and then randomly selecting samples from each stratum.
- Cluster Sampling: Selecting groups or clusters instead of individuals, often used with large populations.
Data Analysis Process
- Data Requirements Specification: Identifying the data required for analysis based on a question or experiment.
- Data Collection: Gathering information on targeted variables.
- Data Processing: Organizing the collected data for analysis.
- Data Cleaning: Preventing and correcting errors in the processed data.
- Data Analysis: Applying various techniques to understand, interpret, and derive conclusions based on the requirements.
- Communication: Presenting the results of the data analysis.
Measures of Central Location
- Mean: The most popular measure of central location, also known as the arithmetic mean.
- Median: Used to avoid being misled by very small or very large values.
Test your knowledge on different types of quantitative data including discrete and continuous data. Learn about numerical values that can be analyzed through arithmetic operations and the characteristics of each data type.
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