Study your flashcards anywhere!

Download the official Cram app for free >

  • Shuffle
    Toggle On
    Toggle Off
  • Alphabetize
    Toggle On
    Toggle Off
  • Front First
    Toggle On
    Toggle Off
  • Both Sides
    Toggle On
    Toggle Off
  • Read
    Toggle On
    Toggle Off

How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key


Play button


Play button




Click to flip

25 Cards in this Set

  • Front
  • Back
Measure of association
A general term that refers toa number of bivariate statistical techniques used to measure the strength of a relationship between two variables
correlation coefficient
a statistical measure of the covariation, or association, between two at least interval variables
extent to which two variables are associated systematically with each other
Negative (inverse) relationship
Covariation in which the association between variables is in the opposite direciton. As one goes up, the other goes down
Does covariation in and of itself establish causality?
No. Think of the example of the covariation between ice cream sales and drownings; or the roosters crow and the rising sun
Coefficient of Determination (R^2)
A measure obtained by squaring the correlation coefficient; the proporion of the total variance of a variable accounted for by another value of another value.

Measures that part of the variance of Y that is accounted for by knowing the value of X
In the example about unemployment and hours worked, r = -.635; therefore r^2=0.403

How much of the variance in unemployment can be explained by the variance in hours worked?
About 40% of the variance in unemployment can be explained by the variance in hours worked.
Correlation Matrix
The standard form for reporting observed correlations among multiple variables. Although any number of variables can be displayed in a correlation matrix, each entry represents the bivariate relationship between a pair of variables.
What is the procedure for determining statistical significance?
The procedure for determining statistical significance is the t-test of the significance of a correlation coefficient
Simple (Bivariate) Linear Regression
A measure of linear association that investigates straight line relationships between a continuous dependent variable and an independent variable that is usually continuous, but can be a categorical dummy variable
In simple linear regression, what do the following symbols stand for:

(Y = (alpha) + (beta)X
(alpha) represents the Y intercept, or where the line crosses the y-axis

(beta) is the slope coefficient

The slope is the change in Y associated with a change of one unit in X. Slope may also be thought of as rise over run.
True/False: Beta provides the strength and direction of the relationship between the independent and dependent variable
True/False: (alpha) Y intercept is a fixed point that is considered a constant
Standardized regression coefficient
Estimated coefficient of the strength of relationship between the independent and dependent variables

Expressed on a standardized scale where higher absolute values indicate stronger relationships (between -1 to 1)
Raw Regression Estimates (b1)
Raw regression weights have theadvantage of retaining the scale metric (also their key disadvantage). Used if the purpose of the regression analysis is forecasting.
Standard Regression estimates ((Beta)1)
Have the advantage of a constant scale. Should be used when the research is testing explanatory hypothesis
Ordinary Least Squares
Generates a straight line that minimizes the sum of squared deviations of the actual values from this predicted regression line.
The equation for the ordinary least squares means that X estimated how?
The equation menas that the predicted value for any value of X is determined as a funciton of the estimated slope coefficient, plus the estimated intercept coefficient + some error
Where does the explanatory power of regression lie?
The explanatory power of regression lies in hypothesis testing
What two conditions must be satisfied for the outcome of the ypothesis test?
The regression weight must be in the hypothesized direction. (positivie relationships require a positive coefficient and negative relationships require a negative one)

The t-test associated with the regression weight must be significant
Multiple Regression Analysis
An analysis of association in which the effects of two or more independent variables on a single, intervalscaled dependent variable are investigated simultaneously
Dummy Variable
The way a dichotomous (two group) independent variable is represnted in regression analysis by assigning 0 to one group and 1 to another
Partial correlation
The correlation between two variables after taking into account the fact that they are correlated with other variables too
R^2 in multiple Regression
The coefficient of multiple determination in multiple regression indicates the percentage of variation in Y explained by all independent variables
Tests statistical significance by comparing the variation explained by the regression equation to the residual error variation .

Allows for testing of the relative magnitudes of the sum of squares due to the regression and the error sum of squares