Probability and Statistics, WGU C955

Scheduled maintenance: 13 January 2026 from 09:00 to 11:00 Probability and Statistics, WGU C955 4.0 (1 review) Students also studied Terms in this set (51) Social SciencesEconomics Econometrics Save WGU D774 Intro to Business Accoun...70 terms Glenna_Ballek Preview C957 121 terms alymarie83Preview C268 Spreadsheets 74 terms that-studying-cat Preview WGU C 83 terms qizl Practice questions for this set Learn1 / 7Study using Learn A question with limited choices, e.g. multiple choice or yes/no. Easier to analyze statistically.Boxplotan image that has min, Q1, median, Q3, max HistogramA graphical representation -- bars, measuring the frequency within each interval Choose an answer 1Boxplot2Linear relationship 3Closed question4Measures of spread Don't know?

Skewed rightNot a symmetric distribution, the tail is on the right, i.e. extra stuff on the right Measures of centerMedian, the mean (and mode) Measures of spreadRange, IQR & standard deviation Standard Deviation Rule68% of the data are within 1 standard deviation, 95% are within 2, 99.7 are within 3 standard deviations from the mean.For skewed data, use these for center and spread In this situation, we use median (for center) & IQR (for spread) Explanatory variableIn a study, what we think is the "cause" Response variableIn a study, what we think is the "effect" Scatter plotA graphical representation of Q -> Q Two way tableA graphical representation of C -> C Side-by side boxA graphical representation of C -> Q Linear relationship"shaped like a line" Correlation coefficient, rBetween -1 and 1; measures how close the points are to the line and if the trend is uphill (positive) or downhill (negative).r = -0.2, for exampleThis is an example of a correlation coefficient that represents a weak negative correlation.r = 0.9, for exampleThis is an example of a correlation coefficient that represents a strong positive correlation.Linear regression lineA line that fits the data as close as possible, used to make predictions InterpolationMaking predictions within the range of your data. This is usually accurate.ExtrapolationMaking predictions outside of the range of your data. This is generally a bad idea.Simpson's ParadoxWhen split up, each data set can have a pattern which goes away when all the data is combined.Only way to prove causationExperiments, because they account for lurking variables Observational studyA type of study where we measure or survey members of a sample without trying to affect them. Cannot prove causation.Experimental studyA study where you split subjects up randomly and impose a change on one group to study the effect; can prove causation

Prospective studyIs a study that's done over time to find results / A study watching for outcomes during the study period Retrospective studyA study that looks backwards to assess outcomes and possible causes after the fact Control grouprandomly assign people or things to groups. One group receives a treatment and the other group does not. This is the group that does not receive treatment PlaceboA substance or procedure that has no effect used for comparison to the real substance or procedure Placebo effectA beneficial effect produced by the belief of the patient/subject, not by the intervention itself.Experimenter effectWhen the persons running an experiment affects its results by influencing the subjects inadvertently Open questionA question that gives the responder freedom to answer in many different ways -- harder to analyze with statistics.Closed questionA question with limited choices, e.g. multiple choice or yes/no. Easier to analyze statistically.Unbalanced responseGiving more options that are negative than positive options (or vice versa) which biases the responses towards the more common option Matched pairsGrouping two similar subjects and giving different treatments/procedures to each in order to compare the differences. For example, having one twin take a medication while the other twin does not.Blind experimental studyWhen information of a study isn't revealed to the participants Double Blind StudyWhen information of study is hidden from the researcher and the participant.PopulationThe entire group you are trying to describe or understand.Sampling frameList of group from which you choose your sample.SampleThe group that is actually picked to be included in a study Simple random samplemaking a selection by following a random pattern and selecting without replacement. Unbiased.Systematic sampleSample is selected by listing the sampling frame, then making a selection by following a simple pattern (eg. Every 20th name). Unbiased.Voluntary sampleMembers of the sample may choose not to respond. Similar to Non Response.

Convenience sampleParticipants are easy for researcher to access. Tends to increase bias.Cluster sampleSample frame is divided into groups, we select a few groups, then selecting ALL of the members of those groups.Stratified sampleSample frame is divided into groups. Then we choose a random sample (usually the same size) from within EVERY group.Multi-stage sampleMultiple rounds of randomness and grouping. For example: randomly selecting a few groups, then choosing a small random sample just those selected groups.Often a combination of Cluster and Stratified Sampling.The formula for all simple probabilities(number of possible outcomes for the specific event) / (total number of possible outcomes) For independent events, P(A and B) = ?P(A)*P(B) For independent events, P(A|B) = ?P(A) [this is the right side of which probability formula?] P(A|B) = ?P(A and B) ÷ P(B) P(A or B) = ?P(A) + P(B) - P(A and B) In general, P(A and B) = ?P(A)*P(B|A)