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Questions and Answers
What is the purpose of descriptive statistics?
What is the purpose of descriptive statistics?
What is a population in statistics?
What is a population in statistics?
The entire group of objects, organisms, or events we are interested in.
What defines a sample?
What defines a sample?
A subset of the population meant to represent the population.
Probabilistic sampling gives every member of the population an equal chance of being selected.
Probabilistic sampling gives every member of the population an equal chance of being selected.
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A variable can be a constant if there is no variance.
A variable can be a constant if there is no variance.
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Match the levels of measurement to their definitions:
Match the levels of measurement to their definitions:
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Which of the following is an example of a dependent variable?
Which of the following is an example of a dependent variable?
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Define a vector in statistics.
Define a vector in statistics.
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The operation resulting in 5+2 is ____.
The operation resulting in 5+2 is ____.
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Which arithmetic operation is represented by the symbol '%',?
Which arithmetic operation is represented by the symbol '%',?
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Study Notes
Descriptive Statistics
- Organizing, summarizing, and representing data using numeric, tabular, and graphic methods.
- Numeric: Measures of central tendency (like mean, median, mode) and measures of dispersion (like range, variance, standard deviation).
- Tabular: Frequency tables and cross tabulation/contingency tables.
- Graphic: Plots and graphs.
Inferential Statistics
- Generalizing findings from data to make predictions and perform hypothesis tests.
Population
- Entire group of interest with specific characteristics.
- The group about which conclusions are drawn.
- Clearly defined to determine inclusion/exclusion.
Sample
- A subset of the population representing the whole.
- Used when the population is too large to study directly.
- Should be representative of the population.
Probabilistic Sampling
- Each member of the population has an equal probability of being selected.
Non-Probabilistic Sampling
- Contains biases, often favoring specific demographics.
- "Convenience sampling" is an example.
Variable
- A measurable characteristic that varies.
- Can change between groups, individuals, or within an individual over time.
- If there is no variance, it is a constant.
- Examples: age, gender, IQ, income, political affiliation.
Levels of Measurement
- Nominal: Categorizes attributes with no intrinsic order.
- Ordinal: Categories with an intrinsic order, but no defined intervals.
- Interval: Numeric scales with known intervals but no absolute zero.
- Ratio: Highest level with known intervals and an absolute zero.
Dependent Variable
- The variable of interest that we aim to explain or predict.
- Relies on other variables for explanation.
- Also known as the "outcome" variable.
Independent Variable
- Variables used to predict or explain the dependent variable.
Vector
- A sequence of data elements of the same type.
- Can contain numeric values, logical values, or string values.
Operations
- Addition: +
- Subtraction: -
- Multiplication: *
- Division: /
- Exponentiation: ^
- Modulo: %%
Comparison Operators
- Greater than: >
- Less than: <
- Greater than or equal to: >=
- Less than or equal to: <=
Mode
- The most frequent observation in a dataset.
- If there are multiple options, use the lowest value as a rule of thumb.
- Applies to nominal, ordinal, interval, and ratio variables.
Nominal Variable
- Any variable with only two types of observation is by definition a nominal variable.
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Description
This quiz covers the foundational concepts of descriptive and inferential statistics, including measures of central tendency and sampling methods. Understand how to organize and represent data effectively, as well as generalizing findings from a sample to a larger population. Perfect for students wanting to solidify their grasp of statistical principles.
