WorksheetsLinear Regression Quiz
Total questions: 10
Worksheet time: 6mins
In simple linear regression, the slope coefficient (m) represents:
Average change in Y for one-unit change in X
Intercept value when X = 0
Strength of correlation between X and Y
The residual error
Which assumption of linear regression ensures that the relationship between X and Y is straight-line?
Non linearity
Linearity
Independence of errors
Normality of errors
Given: Y = 50 + 2X. If X increases by 5 units, Y will:
Increase by 2
Increase by 10
Increase by 55
Stay unchanged
A regression equation is: Ŷ = 120 - 3X. The slope (-3) means:
Y decreases by 3 for each unit increase in X
Y increases by 3 for each unit increase in X
Y intercept is -3
X decreases by 3 when Y increases by 1
If the regression equation is Ŷ = 100 + 0.8X, then when X = 50, predicted Y = ?
40
100
140
200
A bank builds a regression model: Ŷ = 20 + 5 × Income - 2 × Age. Interpretation of coefficient of Age is:
For every additional year, approval score decreases by 2, holding income constant
For every additional year, approval score increases by 2, holding income constant
Age and Income are independent
Approval score is 2 when Age = 0
If regression line is perfectly horizontal, slope = 0. This means:
No relationship between X and Y
Perfect negative correlation
Perfect positive correlation
Regression cannot be calculated
The value of R² in regression indicates:
Proportion of variance in Y explained by X
Strength of correlation between X and Y
The slope of the regression line
The error variance
If a regression model's RMSE = 0, it implies:
Model perfectly predicts training data
Errors are normally distributed
Multicollinearity exists
Regression cannot be used
Suppose we fit Y = 10 + 0.5X₁ + 0.2X₂. If X₁ increases by 4 units while X₂ is fixed, predicted Y changes by:
0.5
2
0.2
10
