Podcast
Questions and Answers
Which type of data can be measured and assumes infinite values within a specified interval?
Which type of data can be measured and assumes infinite values within a specified interval?
What is the main difference between nominal data and ordinal data?
What is the main difference between nominal data and ordinal data?
Which of the following is an example of discrete data?
Which of the following is an example of discrete data?
Which type of data is collected as labels or names without any intrinsic ordering?
Which type of data is collected as labels or names without any intrinsic ordering?
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Which of the following is an example of continuous data?
Which of the following is an example of continuous data?
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Which type of data is the lowest level of measurement and cannot be measured?
Which type of data is the lowest level of measurement and cannot be measured?
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Which of the following is NOT a characteristic of interval data?
Which of the following is NOT a characteristic of interval data?
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Which sampling technique involves selecting every kth member of the population with the starting point determined at random?
Which sampling technique involves selecting every kth member of the population with the starting point determined at random?
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In the data analysis process, what is the step that involves preventing and correcting errors, such as incomplete data, duplicates, or mistakes?
In the data analysis process, what is the step that involves preventing and correcting errors, such as incomplete data, duplicates, or mistakes?
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Which of the following is NOT a measure of central tendency commonly used in data analysis?
Which of the following is NOT a measure of central tendency commonly used in data analysis?
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Which level of measurement is higher than ordinal data and allows for the ranking of variables?
Which level of measurement is higher than ordinal data and allows for the ranking of variables?
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In cluster sampling, what is selected instead of individual members of the population?
In cluster sampling, what is selected instead of individual members of the population?
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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.
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Description
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.
