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# Elementary Statistic

### wgu: Domain: Mathematics Content (5-9) Subdomain: Part IV: Statistics and Probab

Discrete Data the number of possible values is either finite number or a countable number # of eggs laid by a chicken
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
Quantative data representing counts or measurements. weights of super models
Qualatative (categorical or attribute) data different categories distinguished into different categories by non numeric characteristic. gender of professional atheletes
Parameter Numerical measurement describing some characteristic of a population. Count entire population the number of redlights working and not in a city
Statistic a numerical measurement describing some chacteristic of a sample 60% of 800 bell employees have 401K
Census Collection of data from every element in a population
Sample Subset of a population
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
Data observations (such as measurements, genders, survey responses) that have been collected
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
Census Collection of data from every member of a population
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.
Parameter a numerical measurement describing some characteristic of a population.
Statistic a numerical measurement describing some characteristic of a sample.
Quantitative data numbers representing counts or measurements. Example: The weights of supermodels
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
Quantitative data can further be described by distinguishing between discrete and continuous types.
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
Sample Subcollection of members selected from a population
Nomial Level measurement characterized by data that consit of names labels no ordering scheme Ex. yes, no, undecided
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
Interval level of measurement like ordinal but diffence in values is meanighfyl no natura zero Ex years 1000,2000,1776,1492
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)
Nominal categories only
Ordinal categories with some order
Interval differences but no natural starting point
Ratio differences and a natural starting point
Voluntary Response Sample VRS one in which respondents themselves decide whether to be included Ex: mail in, internet poll
Small Samples Size Conclusion should not be made on small samole size EX: Suspension rate based only on three students
Misuse of percentages 100% is 100% no such thing as 110%
Observartional Study observing and measuring specific characteristics withour attemping to midify the subjects being studied.
Experiment apply some treatment and observe its effects on the subjects;
Experimental units subjects in experiment
Cross sectional study data are observed, measued, and collected at one point in time
Retrisoectuve (case Control) study data are collected from the past by going back in time.
Prospective (longitudinal or cohort) study data are collected in the future from groups (called cohorst )sharing common factors.
Confounding occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors.
Blinding subject does not know if he is receiving a treatment or a placebo.
Blocks groups of subjects with similar characteristics
Completely Randomized Experimental Design subjecrts are put into blocks thrught a process of random selection.
Rigorously Controlled Design Subjects are very carefully chosen.
Replication Repetition of an experiment when there are enough subjects to recognize the differences from different treatments.
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
Random Sample members of the population are selected in such a way that each individual member has an equal chance of being selected
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
Systematic Sampling Starting point and select every kth element Ex: start at 14 and chose every 5th member after.
Convience Sampling use results that are easy to obtain
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
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.
Methods of sampling Random Systematic Convenience Stratified Cluster
Sampling error The difference between a smple result and the true population result; error results from chance sample fluctuations
Nonsampling error Sample data incorrectly collectd, recorded, or analyzed Ex: (biased sample, defectve instrument, copying data incorrectly)