wgu: Domain: Mathematics Content (5-9) Subdomain: Part IV: Statistics and Probab
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| Discrete Data | the number of possible values is either finite number or a countable number # of eggs laid by a chicken
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| Continuous (numerical)data | result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, or interuptions. amount of milk from a cow
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| Quantative data | representing counts or measurements. weights of super models
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| Qualatative (categorical or attribute) data | different categories distinguished into different categories by non numeric characteristic. gender of professional atheletes
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| Parameter | Numerical measurement describing some characteristic of a population. Count entire population the number of redlights working and not in a city
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| Statistic | a numerical measurement describing some chacteristic of a sample 60% of 800 bell employees have 401K
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| Census | Collection of data from every element in a population
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| Sample | Subset of a population
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| Statistics | Statistics a collection of methods for planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data
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| Data | observations (such as measurements, genders, survey responses) that have been collected
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| Population | the complete collection of all elements (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all subjects to be studied
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| Census | Collection of data from every member of a population
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| The subject of statistics | is largely about using sample data to make inferences (or generalizations) about an entire population. It is essential to know and understand the definitions that follow.
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| Parameter | a numerical measurement describing some characteristic of a population.
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| Statistic | a numerical measurement describing some characteristic of a sample.
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| Quantitative data | numbers representing counts or measurements. Example: The weights of supermodels
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| Qualitative (or categorical or attribute) data | can be separated into different categories that are distinguished by some nonnumeric characteristic Example: The genders (male/female) of professional athletes
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| Quantitative data | can further be described by distinguishing between discrete and continuous types.
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| Discrete Data | result when the number of possible values is either a finite number or a ‘countable’ number (i.e. the number of possible values is 0, 1, 2, 3, . . .) Example: The number of eggs that a hen lays
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| Sample | Subcollection of members selected from a population
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| Nomial Level measurement | characterized by data that consit of names labels no ordering scheme Ex. yes, no, undecided
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| Ordinal level of measurement | Data that can be arranged by order but differences indata values can not be determined Ex: grades A<B<C<D<F
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| Interval level of measurement | like ordinal but diffence in values is meanighfyl no natura zero Ex years 1000,2000,1776,1492
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| Ratio Level of measurement | the interval level with the addtioional proerty there is a natural zero where zero none present Ex textbooks ($0 represents free)
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| Nominal | categories only
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| Ordinal | categories with some order
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| Interval | differences but no natural starting point
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| Ratio | differences and a natural starting point
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| Voluntary Response Sample VRS | one in which respondents themselves decide whether to be included Ex: mail in, internet poll
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| Small Samples Size | Conclusion should not be made on small samole size EX: Suspension rate based only on three students
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| Misuse of percentages | 100% is 100% no such thing as 110%
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| Observartional Study | observing and measuring specific characteristics withour attemping to midify the subjects being studied.
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| Experiment | apply some treatment and observe its effects on the subjects;
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| Experimental units | subjects in experiment
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| Cross sectional study | data are observed, measued, and collected at one point in time
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| Retrisoectuve (case Control) study | data are collected from the past by going back in time.
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| Prospective (longitudinal or cohort) study | data are collected in the future from groups (called cohorst )sharing common factors.
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| Confounding | occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors.
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| Blinding | subject does not know if he is receiving a treatment or a placebo.
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| Blocks | groups of subjects with similar characteristics
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| Completely Randomized Experimental Design | subjecrts are put into blocks thrught a process of random selection.
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| Rigorously Controlled Design | Subjects are very carefully chosen.
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| Replication | Repetition of an experiment when there are enough subjects to recognize the differences from different treatments.
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| Sample Size | use a sample size that is large enought to see the true nature of any effects andsample using appropriate method such as randomeness
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| Random Sample | members of the population are selected in such a way that each individual member has an equal chance of being selected
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| Simple Random Sample (of size n) | Subjects selected in such a way that every possible sample of the sme size n has the same chance of being chosen
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| Systematic Sampling | Starting point and select every kth element Ex: start at 14 and chose every 5th member after.
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| Convience Sampling | use results that are easy to obtain
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| Stratified Sampling | subdivide the population into at least two different subgroups that share same characteristics, then draw a sample from each subgroup (or stratum). EX men women groups
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| Cluster Sampling | divide the population into sections (clusters); randomly select some of those clusters; choose all members from selectd clusters. Ex choose 3 of 20 precints and interview every body in those precints.
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| Methods of sampling | Random Systematic Convenience Stratified Cluster
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| Sampling error | The difference between a smple result and the true population result; error results from chance sample fluctuations
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| Nonsampling error | Sample data incorrectly collectd, recorded, or analyzed Ex: (biased sample, defectve instrument, copying data incorrectly)
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Created by:
hrichey@my.wgu.edu
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