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Lecture 3 Overview
Simple Linear Regression
Model predicting Y from X using linear equation.
Standardised Regression Coefficient (𝛽)
Coefficient reflecting effect size in standardised units.
Total Variance Decomposition
Breakdown of variance in dependent variable Y.
R-squared (𝑅²)
Proportion of variance in Y explained by X.
Psychopathy
Personality disorder characterized by antisocial behavior.
Sadism
Pleasure derived from inflicting pain on others.
Linear Model
Mathematical representation of relationship between X and Y.
Regression Coefficient (𝑏₁)
Change in Y for a one-unit change in X.
Descriptive Statistics
Summary statistics describing characteristics of data.
Predicted Y (𝑌')
Estimated value of Y based on X's score.
Mean of X
Average value of independent variable X.
Mean of Y
Average value of dependent variable Y.
Z-scores
Standardized scores indicating distance from mean.
Hours Studied
Independent variable affecting exam grades in regression.
Exam Grade
Dependent variable predicted by hours studied.
Bitter Taste Preference
Associated with higher antisocial personality traits.
Prediction Error
Difference between observed and predicted values.
Graphic Representation
Visual depiction of regression analysis results.
Population Generalization
Applying sample results to the larger population.
Mean
Average value of a dataset.
Variance
Measure of data spread around the mean.
Z-score
Standardized score indicating relative position.
Unstandardized Coefficient (b)
Coefficient representing change in Y per unit change in X.
Standardized Coefficient (β)
Coefficient representing change in Y per standard deviation change in X.
Linear Regression Model
Predicts Y based on X using coefficients.
Intercept (b₀)
Value of Y when X is zero.
Regression Equation
Mathematical representation of the relationship between variables.
SPSS Output
Statistical software output for regression analysis.
R-squared (R²)
Proportion of variance explained by the model.
Adjusted R-squared
R-squared adjusted for number of predictors.
Numeric Example
Illustrative data used for calculations.
Correlation
Statistical measure of relationship between two variables.
Coefficient of Determination
R-squared value indicating model fit.
Effect Size
Magnitude of the relationship between variables.
Predictor Variable
Independent variable used to predict outcomes.
Standardized Model
Model without intercept, averages are zero.
Research Methods
Techniques for collecting and analyzing data.
Data Spread
Distribution of values in a dataset.
Statistical Significance
Likelihood that a result is not due to chance.
Independent Variable
Variable influencing the dependent variable, denoted as X.
Sum of Squares
Total variance in the dependent variable.
Regression
Statistical method for predicting Y from X.
Residual Variance
Variance not explained by the independent variable.
Mean Square
Average of squared deviations from the mean.
Significance Level (Sig.)
Probability of observing results by chance.
Regression Coefficient (B)
Change in Y for a one-unit change in X.
Standard Error
Estimate of the standard deviation of coefficients.
Standardized Coefficient (Beta)
Coefficient adjusted for variable scale differences.
Total Variance (S²)
Overall variance of the dependent variable.
Variance Explained (S² regression)
Variance in Y explained by X.
Variance Unexplained (S² residual)
Variance in Y not explained by X.
t-value
Ratio of the difference between group means.
Predicted Value (Y′)
Estimated value of Y based on regression.
Error (e)
Difference between observed and predicted values.
ANOVA Table
Summary of variance analysis results.
Correlational Research
Study of relationships between variables without manipulation.
Regression Analysis
Statistical method to predict dependent variable from independent.
Residuals
Differences between observed and predicted values.
Sum of Squares (SS)
Total variation in data, used in ANOVA.
SS Regression
Variation explained by the regression model.
SS Residual
Variation not explained by the regression model.
Standard Deviation (SD)
Square root of variance, indicates data spread.
ANOVA
Analysis of variance, compares means across groups.
F-test
Statistical test to compare variances of populations.
R Square
Proportion of variance explained by the model.
Adjusted R Square
R Square adjusted for number of predictors.
Degrees of Freedom (df)
Number of independent values in a calculation.
Predictors
Independent variables used in regression analysis.
Dependent Variable
Outcome variable predicted by independent variables.
Hypothesis Testing
Procedure to determine if a hypothesis is true.
Null Hypothesis (H0)
Assumes no effect or relationship exists.
Alternative Hypothesis (H1)
Assumes there is an effect or relationship.
Bitter Preference Scale
Scale measuring preference for bitter foods (1-6).
Psychopathy Scale
Measure of psychopathic traits in individuals.
Coefficients
Values that represent the relationship strength in regression.
Model Summary
Overview of regression model statistics and performance.
Note 
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