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.
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.
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.
What type of bias could be introduced by mean/median/mode imputation, particularly if the data is not missing at random?
- Confirmation bias
- Overfitting bias
- Selection bias
- Underfitting bias
Mean/Median/Mode Imputation, particularly when data is not missing at random, could introduce a type of bias known as 'Selection Bias'. This is because it might lead to incorrect estimation of variability and distorted representation of true relationships between variables, as the substituted values may not accurately reflect the reasons behind the missingness.
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.
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 does the number of imputations affect the accuracy of multiple imputation?
- More imputations, less accuracy
- More imputations, more accuracy
- Number of imputations doesn't affect accuracy
- Only one imputation is needed for full accuracy
The number of imputations directly affects the accuracy of multiple imputation. More imputations result in more accurate estimates, up to a point. Although the exact number depends on the proportion and nature of the missing data, often 20 to 100 imputations are recommended in the literature.
In data analysis, EDA stands for _______.
- Empirical Data Assessment
- Exploratory Data Analysis
- Exponential Data Analysis
- Expressive Data Assimilation
In data analysis, EDA stands for Exploratory Data Analysis. It is an approach to analyzing data sets to summarize their main characteristics, often with visual methods.
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).
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.