What is a scatter plot used for?
B
To display relationships between two numeric variables
C
To group categorical data
Analysis & Theory
Scatter plots show the correlation or distribution between two continuous variables.
In a scatter plot, what do each of the points represent?
C
An individual observation with two variables
Analysis & Theory
Each point represents one data instance with values on the x and y axes.
What kind of relationship is shown when scatter plot points form a straight upward trend?
Analysis & Theory
A positive correlation means that as one variable increases, the other does too.
Which Matplotlib function is used to create a scatter plot in Python?
Analysis & Theory
`plt.scatter(x, y)` is the correct function for creating a scatter plot.
What does adding color to points in a scatter plot help show?
C
A third variable or category
Analysis & Theory
Color coding can represent a third variable or different groups/clusters.
What happens if all the points in a scatter plot are in a horizontal line?
A
There is no relationship between variables
B
There is a perfect negative correlation
C
The y-variable is constant
D
The x-variable is constant
Analysis & Theory
A horizontal line of points means the y-value is the same across all x-values.
How can you add a trend line to a scatter plot in Python?
B
With `np.polyfit()` and `plt.plot()`
C
By using `plt.fill_between()`
Analysis & Theory
A common method to add a trend line is fitting a line using `np.polyfit()` and plotting it.
Which argument is used to change marker style in `plt.scatter()`?
Analysis & Theory
The `marker` argument controls the shape of points in a scatter plot.
Which type of ML visualization often uses scatter plots to show clusters?
C
Clustering (e.g., K-Means)
Analysis & Theory
Scatter plots are useful in visualizing clusters, especially in unsupervised learning like K-Means.
In machine learning, what insight can a scatter plot give about two features?
A
Their correlation and linearity
Analysis & Theory
Scatter plots help explore relationships and trends (linear or non-linear) between two variables.