Module 6: Correlation and Regression Flashcards

1
Q

Simpson’s Paradox

A

A counterintuitive situation in which a trend in different groups of data disappears or reverses when the groups are combined.

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2
Q

coordinate plane

A

A tool for graphing consisting of a horizontal x-axis and a vertical y-axis.

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3
Q

simple linear regression

A

The prediction of one response variable’s value from one or more explanatory variables’ value when there is a linear relationship between the two variables.

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4
Q

sampling frame

A

The list of all people or things that may be included in the statistical study.

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5
Q

significance level

A

The p-value cutoff for statistical significance. Any p-value below the set significance level is considered statistically significant.

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6
Q

cluster sample

A

Similar to stratified sample, but researchers select entire chunks or clusters of the population to obtain the study sample.

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7
Q

observational study

A

The researcher observes if there is an association between variables. There is no treatment or control group.

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8
Q

significant difference

A

A measurable difference between two groups or samples that reflects a real difference, rather than the difference being by chance.

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9
Q

correlation

A

An observed relationship between two quantitative variables. While this is most commonly a linear relationship, it does not need to be. Note that observing a relationship does NOT imply that there is a meaningful causal link between the variables.

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10
Q

p-value

A

The probability that a result was caused by chance.

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11
Q

regression analysis

A

A statistical analysis tool that quantifies the relationship between a response variable and one or more explanatory variables.

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12
Q

sampling method

A

The technique used to select people within the sampling frame.

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13
Q

causal relationship

A

A relationship between two variables that can be classified as cause-and-effect.

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14
Q

representative sample

A

A subset of the population with similar characteristics to the entire population.

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15
Q

population

A

All subjects in the study which have the characteristics being evaluated.

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16
Q

slope-intercept form

A

A common format for the equation of a line: y = mx + b, where m is the slope and b is the y-intercept.

17
Q

voluntary sample

A

Researchers invite everyone in the sampling frame to participate. Individuals who voluntarily respond comprise the study sample.

18
Q

scatterplot

A

A graph that uses dots on a coordinate plane to show the relationship between variables.

19
Q

lurking variable

A

A variable that is not included in an analysis but that is related to two (or more) other associated variables which were analyzed.

20
Q

hypothesis test

A

A statistical test that tells us whether a result is significant.

21
Q

sample

A

The subset of the study population that is being studied.

22
Q

linear interpolation

A

Estimation using the linear regression equation in between known data points.

23
Q

correlation coefficient

A

A measure of the linear relationship between two attributes. The numerical value demonstrates how closely the attributes vary together. Correlation coefficients near -1 and +1 have strong linear correlation, while a correlation coefficient near 0 has weak (or no) linear correlation.

24
Q

least squares

A

A technique for finding the regression line.

25
Q

positive correlation

A

A linear relationship between two quantitative variables in which the dependent variable increases as the independent variable increases.

26
Q

regression line

A

The line of best fit to show the relationship between variables, the one that minimizes distance from each data point to the line.

27
Q

association

A

A pattern or relationship between two variables.

28
Q

experimental study

A

The researcher applies a treatment to one group and no treatment (or placebo) to a control group, to determine if there is causation between variables.

29
Q

extrapolate

A

Using information from a data set to make predictions about data outside of the original set.

30
Q

causation

A

A relationship of cause and effect between two or more variables.

31
Q

regression equation

A

An equation used to model the relationship between the response and explanatory variables in a regression.

32
Q

linear extrapolation

A

Estimation using the linear regression equation is made outside known data points.

33
Q

degree

A

The largest exponent in a mathematical expression or equation.

34
Q

statistically significant

A

The presumption that a given result or relationship is caused by more than just random chance.

35
Q

negative correlation

A

A linear relationship between two quantitative variables in which the dependent variable increases as the independent variable decreases.