Psychological Statistics || Module 9 & 10

Linear Regression

Statistical method to model relationship between variables

Prediction

Using known data to estimate unknown data

Line of Best Fit

Line expressing relationship in scatter plot data

Least Squares Method

Technique to minimize sum of squared differences

ANOVA Table

Table showing variance components in analysis

Hypothesis Testing

Statistical method to test relationships in data

Geometric Equation

Equation representing line in scatter plot

Intercept

Point where line crosses Y-axis

Slope

Steepness and direction of the line

Parameter Estimates

Approximations of true population values

Correlation

Measure of relationship between variables

Covariance

Measure of joint variability between variables

Variance

Measure of variability or spread of data

Pearson Correlation Coefficient

Measure of linear correlation between variables

Simple Regression Assumptions

Conditions for valid application of regression

Null Hypothesis

Statement of no effect or relationship in data

Alternative Hypothesis

Statement of effect or relationship in data

Critical Value

Value to determine statistical significance

Degrees of Freedom

Number of values free to vary in analysis

F Statistic

Statistic to compare variances in groups

Linear Regression Model

Statistical model explaining variance between variables

Four-Step Hypothesis-Testing Procedure

Process to test for statistically significant relationships between variables

Test Statistics

Values calculated to assess significance of regression model

Means, Standard Deviations, Sum of Products

Calculated values for X and Y variables in regression analysis

Slope of the Line

Rate of change in Y for a unit change in X

Equation for Line of Best Fit

Mathematical representation of the regression model

Effect Size in Regression

Measure of how much variance is explained by the model

Obtained F Statistic

Calculated value used to test hypothesis in ANOVA