wgu: Domain: Mathematics Content (5-9) Subdomain: Part IV: Statistics and Probab
Quiz yourself by thinking what should be in
each of the black spaces below before clicking
on it to display the answer.
Help!
|
|
||||
---|---|---|---|---|---|
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)
🗑
|
Review the information in the table. When you are ready to quiz yourself you can hide individual columns or the entire table. Then you can click on the empty cells to reveal the answer. Try to recall what will be displayed before clicking the empty cell.
To hide a column, click on the column name.
To hide the entire table, click on the "Hide All" button.
You may also shuffle the rows of the table by clicking on the "Shuffle" button.
Or sort by any of the columns using the down arrow next to any column heading.
If you know all the data on any row, you can temporarily remove it by tapping the trash can to the right of the row.
To hide a column, click on the column name.
To hide the entire table, click on the "Hide All" button.
You may also shuffle the rows of the table by clicking on the "Shuffle" button.
Or sort by any of the columns using the down arrow next to any column heading.
If you know all the data on any row, you can temporarily remove it by tapping the trash can to the right of the row.
Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.
Normal Size Small Size show me how
Normal Size Small Size show me how
Created by:
hrichey@my.wgu.edu
Popular Math sets