What does the 'fit_intercept' parameter in 'LinearRegression()' control?
Whether to calculate the intercept (bias) of the line.
Whether to use gradient descent for optimization.
Whether to calculate the slope of the line.
Whether to normalize the data before fitting.
Who is credited as a pioneer in developing the method of least squares, a foundational element of linear regression?
Ada Lovelace
Blaise Pascal
Carl Friedrich Gauss
Alan Turing
What is the purpose of the coefficient of determination (R-squared) in linear regression?
To identify the presence of outliers in the data.
To determine the statistical significance of the model.
To assess the linearity assumption of the model.
To measure the proportion of variation in the dependent variable explained by the independent variable(s).
A positive coefficient of the independent variable in a simple linear regression model indicates what?
The independent variable has no impact on the dependent variable.
As the independent variable increases, the dependent variable tends to increase.
As the independent variable increases, the dependent variable tends to decrease.
There is no relationship between the independent and dependent variables.
What is the ideal shape of a residual plot for a well-fitted linear regression model?
Random scatter with no discernible pattern.
A U-shape.
A straight line.
An inverted U-shape.
In the context of linear regression, what is an error term?
A mistake made in collecting or entering data.
The difference between the observed value of the dependent variable and the predicted value.
The difference between the slope and the intercept of the regression line.
The variation in the independent variable.
Which method in pandas is used to read a CSV file containing the dataset for Linear Regression?
loadtxt()
from_csv()
load()
read_csv()
Why is normality of errors an important assumption in linear regression?
It ensures the linearity of the relationship between variables
It guarantees the homoscedasticity of the errors
It is necessary for the calculation of the regression coefficients
It validates the use of hypothesis testing for the model's coefficients
Which of the following indicates a strong positive correlation between two variables?
A p-value greater than 0.05
Correlation coefficient (r) close to 0
Correlation coefficient (r) close to 1
Correlation coefficient (r) close to -1
If a Durbin-Watson test statistic is close to 2, what does it suggest about the residuals?
They are homoscedastic
They are normally distributed
They are independent
They exhibit a linear pattern