Questions and Answers
What is a categorical variable?
What is a numerical variable?
Variables that take on values that are indicated by numbers.
Define a continuous variable.
A type of numerical value that has an infinite amount of possible values (within a specified range).
Define a discrete variable.
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What is a nominal variable?
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What is an ordinal variable?
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What does skewed mean in data analysis?
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What is standard deviation?
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Define clusters in data.
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What is segmentation in marketing?
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What are behavioral considerations?
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Define bias in statistics.
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What is the selection process in sampling?
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Define a biased selection process.
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What is an estimate?
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Define inference in statistics.
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What does parameter mean?
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What are interviewer effects?
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What is margin of error?
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Define sample size (n).
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What is population size (N)?
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What is nonresponse bias?
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Define a pilot survey.
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What is a poll?
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Define population in statistics.
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What does precision refer to in statistics?
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What are question effects?
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Define random (chance) error.
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What is random sampling?
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Define a representative sample.
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What is a sample?
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What is sample design?
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Define sampling frame.
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What is sampling variation?
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Define sampling with replacement.
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What does sampling without replacement mean?
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What is selection bias?
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Study Notes
Categorical and Numerical Variables
- Categorical variables are identified by names or labels, lacking numerical significance.
- Numerical variables represent values through numbers, classified further into continuous and discrete types.
Continuous and Discrete Variables
- Continuous variables can take an infinite number of values within a specified range, allowing for fractional values.
- Discrete variables have a limited set of specific values, often whole numbers or counts.
Nominal and Ordinal Variables
- Nominal variables do not have an inherent order and categorize data without ranking.
- Ordinal variables possess a natural order, allowing for meaningful comparisons between categories.
Data Skewness and Measurement
- Skewed data features a long "tail" extending either to the right or left, indicating imbalance in data distribution.
- Standard deviation indicates the degree of variability or dispersion of scores around the mean score.
Data Clusters and Segmentation
- Clusters represent groups of data points that are in proximity to each other, highlighting patterns.
- Segmentation involves categorizing customers with shared characteristics, predicting similar purchasing behaviors.
Behavioural Considerations and Bias
- Behavioural considerations highlight that individuals may tailor their answers to align with social expectations, introducing potential bias.
- Bias refers to a systematic overestimation or underestimation in measurement.
Sampling Processes
- The selection process determines how participants are chosen for a sample, influencing the results' reliability.
- Biased selection can lead to systematic errors, undermining the study’s validity and representativeness.
Estimates and Inference
- Estimates are numerical approximations derived from data, helping infer unknown parameter values.
- Inference utilizes sample statistics to draw conclusions about broader population parameters.
Population and Sample Sizes
- The population size (N) is the total number of individuals in a defined group, while the sample size (n) refers to the selected subset.
- Nonresponse bias occurs when a significant portion of the sampled population does not respond, skewing results.
Surveys and Polls
- Pilot surveys are preliminary studies to identify potential issues before conducting the main survey.
- Polls gauge public opinion or behaviors by collecting data from a subset, inferring insights about the entire population.
Precision and Errors in Sampling
- Precision describes the variability of estimates when samples are repeatedly drawn, indicating reliability.
- Random (chance) errors result from the sample being an imperfect reflection of the overall population.
Sampling Techniques
- Random sampling ensures each subject is selected purely by chance, aiming for an unbiased representation.
- Sampling can be done with or without replacement, affecting the likelihood of reselecting elements in the sample.
Selection Bias and Sampling Variation
- Selection bias arises when the sample diverges from the actual population, compromising the study's validity.
- Sampling variation accounts for differences in statistical outcomes across different samples due to random selection.
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Description
Test your knowledge with these flashcards on fundamental statistical concepts. From categorical to discrete variables, this quiz covers essential definitions that every statistics student should know. Perfect for reviewing key terms in Statistics 101.