If you are working with a large data set and need to produce interactive visualizations for a web application, which Python library would be the most suitable?
- Bokeh
- Matplotlib
- Plotly
- Seaborn
Plotly is well-suited for creating interactive visualizations and can handle large data sets efficiently. It also supports rendering in web applications, making it ideal for this scenario.
For data with outliers, the _____ is typically a better measure of central tendency as it is less sensitive to extreme values.
- Mean
- Median
- Mode
- Variance
The "Median" is less sensitive to extreme values, or outliers, in a dataset. Therefore, it's often a better measure of central tendency when outliers are present.
You are analyzing a data set that includes the number of visitors to a website per day. How would you categorize this data type?
- Continuous data
- Discrete data
- Nominal data
- Ordinal data
The number of visitors to a website per day would be discrete data as it is countable in a finite amount of time.
Which type of graph is frequently used to represent an estimate of a variable's probability density function?
- Bar chart
- Kernel Density plot
- Pie chart
- Scatter plot
A Kernel Density Plot is frequently used to represent an estimate of a variable's probability density function. This type of plot uses a smoothing kernel to create a curve and the area under the curve is equal to 1.
Why is it important to check the normality of residuals in regression analysis?
- To ensure the accuracy of the model's predictive ability
- To ensure the model is not overfitting
- To make sure the regression line is the best fit
- To satisfy one of the key assumptions of linear regression
It is important to check the normality of residuals in regression analysis because it is one of the key assumptions of linear regression. If the residuals are normally distributed, it validates the model's assumptions and ensures the accuracy of the hypothesis tests and confidence intervals.
How does incorrect imputation of missing data influence the accuracy of a predictive model?
- Decreases accuracy.
- Depends on the specific model.
- Increases accuracy.
- No effect on accuracy.
Incorrect imputation of missing data can lead to the model learning incorrect patterns, which in turn can significantly decrease the accuracy of predictions.
If a data point's Z-score is 0, it indicates that the data point is _______.
- above the mean
- an outlier
- below the mean
- on the mean
A Z-score of 0 indicates that the data point is on the mean.
What is the potential disadvantage of using listwise deletion for handling missing data?
- It causes overfitting
- It discards valuable data
- It introduces random noise
- It leads to multicollinearity
The potential disadvantage of using listwise deletion for handling missing data is that it can discard valuable data. If the missing values are not completely random, discarding the entire observation might lead to biased or incorrect results because it might exclude certain types of observations.
What type of data visualization method is typically color-coded to represent different values?
- Heatmap
- Histogram
- Line plot
- Scatter plot
Heatmaps are typically color-coded to represent different values. In a heatmap, data values are represented as colors, making it an excellent tool for visualizing large amounts of data and the correlation between different variables.
How can regularization techniques contribute to feature selection?
- By adding a penalty term to the loss function
- By avoiding overfitting
- By reducing model complexity
- By shrinking coefficients towards zero
Regularization techniques contribute to feature selection by shrinking the coefficients of less important features towards zero. This has the effect of effectively removing these features from the model, thus achieving feature selection.
You're in the 'explore' phase of the EDA process and you notice a potential error back in the 'wrangle' phase. How should you proceed?
- Conclude the analysis with the current data.
- Go back to the wrangling phase to correct the error.
- Ignore the error and continue with the exploration.
- Inform the stakeholders about the error.
If you notice a potential error in the 'wrangle' phase while you are in the 'explore' phase, you should go back to the 'wrangle' phase to correct the error. Ensuring the accuracy and quality of the data during the 'wrangle' phase is crucial for the validity of the insights drawn in subsequent phases.
Can multiple imputation be applied when data are missing completely at random (MCAR)?
- No
- Only if data is numerical
- Only in rare cases
- Yes
Yes, multiple imputation can be applied when data are missing completely at random (MCAR). In fact, it is a flexible method that can be applied in various missing data situations including MCAR, MAR (missing at random), and even NMAR (not missing at random).