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.
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.
Under what conditions would the median be a better measure of central tendency than the mean?
- When the data has an even number of observations
- When the data has outliers or is skewed
- When the data is normally distributed
- When the data is uniformly distributed
The median would be a better measure of central tendency than the mean when the data has outliers or is skewed. In these cases, the mean can be heavily influenced by the extreme values, while the median, being the middle value, remains more robust and representative of the central location of the data.
How does the 'subplot' function in Matplotlib differ from 'FacetGrid' in Seaborn?
- FacetGrid allows the creation of multi-plot grids based on row and column-wise grouping of the data
- FacetGrid supports interactive plotting
- Subplot can create only single plots
- Subplot does not allow the sharing of axes
The 'subplot' function in Matplotlib is used for creating sub-plots in a single figure, but it does not allow easy creation of grids of plots based on categorical variables. On the other hand, 'FacetGrid' in Seaborn allows the creation of complex multi-plot grids based on row and column-wise grouping of the data.
_____ data is numerical in nature and can be ordered or measured.
- Nominal
- Ordinal
- Qualitative
- Quantitative
Quantitative data is numerical, measurable, and can be used with mathematical operations.
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.
________ 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 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.
How does the role of data visualization differ in EDA, CDA, and Predictive Modeling?
- Data visualization is not essential in any of these processes.
- Data visualization is only used in EDA.
- Data visualization is used in EDA to explore, in CDA to confirm, and in Predictive Modeling to represent the final model.
- Data visualization plays the same role in EDA, CDA, and Predictive Modeling.
Data visualization plays different roles in each of these processes. In EDA, it is used to explore data and identify initial patterns or anomalies. In CDA, it can be used to represent statistical tests and confirm hypotheses. In Predictive Modeling, it is often used to represent the final model or visualize prediction results.
Can the Binomial Distribution be used to model the number of successes in a fixed number of Bernoulli trials?
- No
- Only for large sample sizes
- Only for small sample sizes
- Yes
Yes, the Binomial Distribution is used exactly for this purpose. It models the number of successes in a fixed number of independent Bernoulli trials each with the same probability of success.
How are outliers usually represented in a boxplot?
- As points outside the box
- As points outside the whiskers
- As the median of the boxplot
- As the quartiles of the boxplot
In a boxplot, outliers are typically represented as points that fall outside of the whiskers (the lines extending from the box, indicating variability outside the upper and lower quartiles).
Can you describe the basic idea behind the Interquartile Range (IQR) method for outlier detection?
- It calculates the difference between the 75th and 25th percentile
- It involves the calculation of Z-scores
- It is based on mean
- It is based on standard deviation
The basic idea behind the Interquartile Range (IQR) method for outlier detection is that it calculates the difference between the 75th percentile (Q3) and the 25th percentile (Q1). This range represents the middle 50% of the data.