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28 Cards in this Set
- Front
- Back
Statistics
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The science of data. This involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information.
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Descriptive statistics
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utilizes numerical and graphical methods to look for patterns in a data set, to summarize the information revealed in a data set, and to present that information in a convenient form.
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Inferential statistics
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utilizes sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data.
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Experimental unit
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an object (e.g. person, thing, transaction, or event) about which we collect data.
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Population
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A set of units (usually people, objects, transactions, or events) that we are interested in studying.
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A Variable
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a characteristic or property of an individual population unit.
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Sample
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a subset of the units of a population.
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Statistical inference
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an estimate, prediction, or some other generalization about a population based on information contained in a sample.
*Note: Think about what logic this parallels and include it here. i.e. is it deductive or inductive logic (and are those the correct two) |
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A measure of reliability
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a statement (usually quantitative) about the degree of uncertainty associate with a statistical inference.
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Four Elements of Descriptive Statistical Problems
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1. The population or sample of interest
2. One or more variables (characteristics of the populations or sample units) that are to be investigated 3. Tables, graphs, or numerical summary tools 4. Identification of patterns in the data |
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Five Elements of Inferential Statistical Problems
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1. The population of interest
2. One or more variables (characteristics of the population units) that are to be investigated 3. The sample of population units 4. The inference about the population based on information contained in the sample 5. A measure of the reliability of the inference |
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Quantitative data
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are measurements that are recorded on a naturally occurring numerical scale.
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Qualitative data
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are measurements that cannot be measured on a natural numerical scale; they can only be classified into one of a group of categories.
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A Representative sample
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exhibits characteristics typical of those possessed by the target population
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A random sample
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A sample of n experimental units is a sample selected from the population in such a way that every different sample of size n has an equal chance of selection.
*Note: the n is italic |
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Statistical thinking
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Thinking that involves applying rational thought and the science of statistics to critically assess data and inferences.
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Selection bias
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results when a subset of the experimental units in the population is excluded so that these units have no chance of being selected in the sample
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Nonresponse bias
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results when the researchers conducting a servey or study are unable to obtain data on all experimental units selected for the sample
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Measurement error
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refers to inaccuracies in the values of the data recorded. In surveys, this kind of error may be due to ambigious or leading questions and the interviewer's effect on the respondent.
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Census
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a measure of a variable for every unit of a population.
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Data
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a form of numerical information
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Published source
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a place where a data set has already been collected, ex. a book, journal, or newspaper
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Designed experiment
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A method of collecting data in which the researcher exerts strict control over the units (people, object, or things) in the study.
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Survey
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A method of collecting data where a researcher samples a group of people, asks one or more questions, and records the responses.
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Observation study
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A method of collecting data where the researcher observes the experimental units in their natural setting and records the variable(s) of interest.
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Unethical Statistical Practice
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When a biased sample is intentional, with the sole purpose of misleading the public, the researcher may be considered guilty of this.
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Measurement
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the process we use to assign numbers to variables of individual population units.
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Reliability
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How good an inference is.
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