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Gravity
Terms in this set (33)
Experiment vs. Observational Study
A study is an experiment ONLY if researchers impose a treatment upon the experimental units.
In an observational study researchers make no attempt to influence the results and cannot conclude cause-and-effect.
Sample and Sampling
A representative subset of a population, examined in hope of learning about the population
Confounding and lurking variables
A variable other than x and y that simultaneously affects both variables, accounting for the correlation between the two; A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
SRS
Simple random sample; an SRS of size n is one in which set of n elements in population have an equal chance of selection
Systematic Sampling
Select individuals systemically (every nth individual) starting with a randomly selected individual
Convenience Sampling
Made up of people who are easier to reach
Stratified Sampling
Divide the population into homogeneous groups then take an SRS from each population
Cluster Sampling
Divide the population into subgroup clusters (similar to the population) then select clusters at random and perform a census of the clusters selected
Re-expressing data
Taking the logarithm, the square toot, the reciprocal, or some other mathematical operation on all values in the data set
Simulation
Models random events by using random numbers to specify event outcomes with relative frequencies that correspond to the true real-world relative frequencies we are trying to model
Statistic vs. Parameter
A statistic is any quantity calculated from data whereas a parameter is a numerically valued attribute of a model for a population
Quantitative vs. Qualitative (categorical) data
Quantitative data is data which the numbers act as numerical values vs categorical data which names categories/are not numerical; bar graphs can plot categorical data and histograms can plot quantitative data
Continuous vs. Discrete Data
Continuous data can take any value and can continue to increase or decrease whereas discrete data can only take certain values
Univariate Data
data with one variable/data which consists of observations on only a single characteristic (ie single observation not compared with/against anything)
Bivariate Data
Data with two variables/each value of one of the variables is paired with a value of the other variable (ie ordered pairs)
Multivariate Data
More than two variables are measured on a single experimental unit/a set of statistical models that examine patterns in multidimensional data by considering several data variables (ie Scatter plot)
Frequency Distribution
A graphical representation of measurements arranged by the number of times each measurement was made
Mean
The average of a set of numbers
Median
The middle score in a distribution; half the scores are above it and half are below it
Standard Deviation
A measure of variability that describes an average distance of every score from the mean
Range
The difference between the highest and lowest scores in a distribution
Variance
The sum of squared deviations from the mean, divided by the count minus one
Interquartile Range (IQR)
The difference between the first and third quartiles. IQR=Q3-Q1
Outlier Rule
Upper Cutoff = Q3 + 1.5(IQR)
Lower Cutoff = Q1 - 1.5(IQR)
Any value that is above 1.5 times the IQR or that is below 1.5 times the IQR
Z Score
How many standard deviations an observation is above or below the mean (z=(observation-mean)/(standard deviation))
Empirical Rule
In a normal model, about 68% of values fall within 1 Sx of the mean, about 95% fall within 2 Sx of the mean, and about 99.7% fall within 3 Sx of the mean
Normal Distribution
Describes a distribution that is unimodal and symmetric (bell-shaped curve) and where the mean is equal to the median
Correlation Coefficient (r)
Measures the strength of a linear association/relationship
Coefficient of Determination (r^2)
The square of the correlation between y and x; ___% of the variation in y can be explained by LSRL relating x and y
Residual
The observed value minus the predicted value
LSRL
A unique best-fit line that minimizes the variance of the residuals or, equivalently, the sum of the squared residuals; y=a+bx
Slope of the Regression Line (b)
Gives a value in "y-units per x-unit." Changes in one unit of x are associated but changes of b1 units in predicted values of y; For every 1 additional x, y is PREDICTED to increase by b
Law of Large Numbers
The long-run relative frequency of repeated independent events settles down to its true probability as the number of trials increases
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