Questions and Answers
What is statistics?
The science of conducting studies to collect, organize, summarize, analyze and draw conclusions from data.
What constitutes a population in research?
All subjects (human or otherwise) that are being studied.
What is a census?
Collection of data from every member of the population.
Define a sample.
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What is a statistic?
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What is a parameter?
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What does qualitative data represent?
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Define quantitative data.
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What is quantitative discrete data?
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Define quantitative continuous data.
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What is nominal data?
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Define ordinal data.
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What does interval data measure?
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Define ratio data.
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What is a random sample?
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Define simple random sampling.
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What is stratified sampling?
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Explain systematic sampling.
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What is cluster sampling?
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Define convenience sampling.
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Study Notes
Statistics
- The science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.
- Distinguishes between inferential statistics (making predictions) and descriptive statistics (summarizing data).
Population
- Represents all subjects under study, such as US citizens or students from Reedley College.
Census
- The collection of data from every member of the population, ensuring complete coverage.
Sample
- A subgroup of the population, exemplified by CA residents or females at Reedley College.
Statistic
- A computed number derived from sample data, denoted with symbols like X for sample mean and S for sample standard deviation.
Parameter
- A number characterizing the population, such as M for population mean and σ for population standard deviation.
Qualitative Data
- Categorical data that identifies group membership or characteristics, like hair color, eye color, or race.
Quantitative Data
- Numerical data suitable for arithmetic operations, representing measurable quantities like height, weight, or age.
Quantitative Discrete
- Results from countable values, exemplified by the number of customers visiting a Starbucks in a day.
Quantitative Continuous
- Data arising from infinitely many possible values, such as weight (e.g., 3.2 lbs, 4.97 kg) or height (e.g., 5.5 ft, 10.21 m).
Nominal Level
- Classifies data into distinct, non-overlapping categories without any intrinsic ordering, such as hair color (brown, blonde, black).
Ordinal Level
- Categorizes data that can be ranked but lacks precise differences between ranks, such as social status (upper, middle, lower).
Interval Level
- Ranks data with meaningful differences between measurements but no true zero, exemplified by temperature or historical timelines.
Ratio Level
- Contains characteristics of interval measurement with a true zero, allowing for true ratios, like weight (0 lbs, 20 lbs).
Random Sampling
- Ensures each population member has an equal chance of being selected for the sample.
Simple Random Sampling
- A sampling method where every possible sample of a specified size has the same chance of selection.
Stratified Sampling
- Involves dividing the population into strata (subgroups) and randomly selecting samples from each stratum to ensure representation (e.g., by major).
Systematic Sampling
- Selects samples based on a fixed interval after a random starting point, such as picking every 10th item on an assembly line.
Cluster Sampling
- The population is divided into clusters, and a random selection of whole clusters is sampled, including all members of those clusters.
Convenience Sampling
- Relies on easy-to-access data or participants for sample collection, like stopping every 5th driver at a checkpoint.
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Description
Explore key concepts in statistics with these flashcards from Math 11 Chapter 1. Each card presents essential terminology and definitions that will enhance your understanding of data collection, population studies, and census methodologies. Perfect for revision and self-testing!