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Chapters 9 and 14 Concept Map - Coggle Diagram
Chapters 9 and 14 Concept Map
Chapter 14: Correlation and Regression
When and Why Correlations are Used
1.) Prediction: If two variables are known to be related in some systematic way, it is possible to use one of the variables to make accurate predictions about the other
2.) Validity: Correlations are used to demonstrate validity.
3.) Reliability: Correlations are used to determine reliability.
4.) Theory Verification: Prediction of a theory can be tested by determining the correlation between the two variables
Correlation describes the relationship between two variables
The value of the correlation can be affected greatly by the range of scores in the data
outliers are extreme scores that can have a dramatic effect on the value of the correlation
Coefficient of determination: measures the proportion of variability in one variable that can be determined from the relationship with the other variable
correlation matrix is a table used to show results from multiple correlations
Pearson correlation: measure the degree of linear relationship between two variables when the data (X and Y values) consist of numerical scores from an interval or ratio scale of measurement
Spearman correlation: result of the Pearson correlation formula being used with data from an ordinal scale (ranks)
point-biserial correlation: used to measure the relationship between two variables in situations in which one variable consists of regular, numerical scores, but the second variable has only two values
dichotomous variable: a variable with only two values
phi-coefficient: correlation for when both variables (X and Y) are dichotomous
linear relationship: Y = bX + a
slope: (b), determines how much the Y variable changes when X is increased by one point
Y intercept: value of a in the equation
regression: statistical technique for finding the best-fitting straight line for a set of data
regression line: resulting straight line from regression
standard error of estimate: gives a measure of the standard distance between the predicted Y values on the regression line and the actual Y values in the data
analysis of regression: process of testing the significance of a regression equation
Chapter 9: Introduction to the
t
Statistic
t
statistic: used to test the hypothesis about an unknown population mean, when the value of
s
is unknown
estimated standard error: used as an estimate of the actual standard error when the value of
s
is unknown
degrees of freedom: describe the number of scores in a sample that are independent and free to vary
t
distribution: complete set of
t
values computed for every possible random sample for specific sample size (
n
) or a specific degrees of freedom (
df
)
Assumptions of the t test: 1) The values in the sample must consist of independent observations. 2) The population sampled must be normal.
confidence interval: an interval, or range of values centered around a sample statistic