How is model-based method different from the other two imputation methods?
- It deletes missing data
- It estimates missing values based on a statistical model
- It is not different from the others
- It uses the mode value for imputation
The model-based method is different from the other imputation methods as it estimates missing values based on a statistical model. This method assumes a specific statistical model (like a linear regression, logistic regression, etc.) that generates the data, and missing values are filled in based on this model.
In what scenario would a modified Z-score be beneficial to use for outlier detection?
- When data is bimodal
- When data is normally distributed
- When data is skewed or has outliers
- When data is uniformly distributed
A modified Z-score is beneficial to use for outlier detection when data is skewed or has outliers, as it is more robust to outliers than the traditional Z-score.
You have a dataset with a large number of missing values. What strategies can you use to depict this in your data visualization?
- Ignore the missing values, because they can't be visualized
- Only include complete cases in the visualization
- Replace all missing values with the mean
- Use a different color or pattern to indicate missing values
Missing values can be indicated in data visualizations using a different color or pattern. This strategy allows viewers to see where data is missing, which can be informative in itself. Ignoring or inaccurately replacing missing values can lead to misleading visualizations.
Which type of missing data is completely random and does not depend on any observed or unobserved data?
- MAR
- MCAR
- NMAR
- nan
MCAR (Missing Completely At Random) indicates that the missingness of data is completely random and does not depend on any observed or unobserved data.
During your EDA process, you identify several outliers in your dataset. How does this finding impact your subsequent steps in data analysis?
- You may need to collect more data
- You may need to ignore these outliers as they are anomalies
- You might consider robust methods or outlier treatment methods for your analysis
- You might decide to use a different dataset
Identifying outliers during the EDA process would influence the subsequent steps in data analysis. The outliers could indicate errors, but they could also be true data points. Depending on the context, you might need to investigate the reasons for their presence, treat them appropriately (for example, using robust statistical methods, data transformations, or outlier removal), or revise your analysis techniques to accommodate them.
In a scatter plot, outliers often appear as points that are far removed from the ___________.
- axes
- main concentration of data
- origin
- trend line
In a scatter plot, outliers are often represented as points that are far removed from the main concentration of data.
________ is a measure of dispersion that is particularly useful when the data set has outliers.
- Interquartile Range
- Range
- Standard Deviation
- Variance
The "Interquartile Range (IQR)" is particularly useful when the dataset has outliers because it only considers the middle 50% of the data. This makes it a robust measure of dispersion.
In a situation where the initial 'questioning' phase did not yield actionable insights, what might be the next step in the EDA process?
- Jump to the concluding phase to draw insights
- Proceed to the exploring phase without adjustment
- Revisit the questioning phase to refine or develop new questions
- Skip to the communication phase
If the initial 'questioning' phase does not yield actionable insights, it is necessary to revisit the questioning phase to refine or develop new questions. The questions set the direction of the analysis and are crucial for subsequent steps. If the questions are not well defined or not actionable, it could lead to an ineffective analysis.
When outliers are present, the mean can be _______ as it is sensitive to extreme values.
- Accurate
- Misleading
- Stable
- Unchanged
When outliers are present, the mean can be misleading as it is sensitive to extreme values. This is because the mean takes into account every value in the dataset, so a significantly larger or smaller outlier can skew the mean.
Multicollinearity can make the regression coefficients _________.
- Constant
- Impossible to calculate
- Unstable and highly sensitive to changes in the model
- Zero
Multicollinearity can inflate the variance of the regression coefficients, making them unstable. This means that small changes in the data can lead to large changes in the estimates of the coefficients. This instability can make interpretation of the model very difficult.