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18 Cards in this Set
 Front
 Back
r Correlation Coefficient

Measure of strength and direction of the linear relationship between 2 quantitative variables


Simple Random Sample (SRS)

An SRS of size n is chosen by a method where every possible group of n has an equal likelyhood of being chosen.


Experimental Design Principle

Control= for lurking variables, ensure that only systematic differences occur in treatments
Random assignment= treatments are randomly assigned to experimental units Replication= enough experimental units are used in each group so effects can be distinguished 

Random Variable Probability Distribution

The list of outcomes, possible values, and probabilities


Random Variable

Numerical result of a random phenomenon


Sampling Distribution

Distribution of values taken by the statistic in all possible samples of the same size from the same population


Binomial Setting

Binary= possible outcome a "success" or "failure"
Independent= results don't effect each other Number= number of trials n is defined Success= probability of success for each trial is the same 

Outlier in Distribution

An observation is an outlier if it falls more than 1.5xIQR above Q3 or below Q1


Density Curve

Always above or on the x axis
Area under curve=1 

Residual

DIfference between the observed y and the predicted y (yyhat)


Least Squares Regression

LSR of y on x is the line that makes the sum of the squared residuals as small as possible


rsquared Coefficient of Determination

% variability y explained by a linear relationship to x


Outlier in a Regression

An observation that lies outside overall pattern of the other observations. Points are outliers in the y direction but not x have large residuals, others may not.


Influential Point

If point was removed it would change result of the calculation. Outliers in the x direction.


Independence

A and B are independent if knowing one occurs tells you nothing about the other


Parameter

Constant that describes the population


Statistic

Random variable that describes a sample (used to estimate parameters)


N>10n condition

Population (N) must be more than 10 times the sample size in order to use a binomial distribution
