Linear Regression Slope Formula:
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The linear regression coefficient (slope) measures the relationship between two variables in a linear regression model. It represents how much the dependent variable (y) changes for each unit change in the independent variable (x).
The calculator uses the linear regression slope formula:
Where:
Explanation: The formula calculates the best-fit line slope that minimizes the sum of squared errors between observed and predicted values.
Details: The slope coefficient is fundamental in statistical analysis, helping to understand relationships between variables, make predictions, and test hypotheses in various fields including economics, science, and social research.
Tips: Enter x and y values as comma-separated numbers. Both arrays must have the same number of values. Ensure data represents a linear relationship for meaningful results.
Q1: What does a positive slope indicate?
A: A positive slope indicates a positive relationship - as x increases, y tends to increase.
Q2: What does a negative slope indicate?
A: A negative slope indicates an inverse relationship - as x increases, y tends to decrease.
Q3: How is slope different from correlation?
A: Slope measures the rate of change, while correlation measures the strength and direction of the linear relationship.
Q4: What is the range of possible slope values?
A: Slope can be any real number from negative to positive infinity, depending on the data relationship.
Q5: When is slope undefined?
A: Slope is undefined when all x values are identical (zero variance in x), causing division by zero in the denominator.