During the 'communicate' step of the EDA process, your audience is having difficulty understanding your conclusions. How could you address this issue?
- Adjust your communication approach to better meet the audience's understanding.
- Clarify their doubts during the communication phase.
- Ignore their difficulty and continue with the communication.
- Tell them to refer to the raw data for clarification.
If the audience is having difficulty understanding the conclusions during the 'communicate' phase, the best approach would be to adjust your communication to better meet the audience's understanding. This might involve simplifying complex concepts, using more visual aids, or providing more contextual explanations. Effective communication is key to ensuring the insights from the analysis are understood and can be acted upon.
How does the probability mass function of a Binomial Distribution change with different parameters?
- All of the above
- It alters the skewness and kurtosis
- It changes the range of possible outcomes
- It impacts the center of the distribution
The probability mass function of a Binomial Distribution changes with different parameters. Specifically, it alters the possible range of outcomes (the number of trials), and the probability of success in each trial.
What type of plot is ideal for visualizing relationships among more than two variables?
- Bar plot
- Box plot
- Pairplot
- Scatter plot
Pairplot is a type of plot that is ideal for visualizing relationships among more than two variables. It creates a grid of Axes such that each variable in your data is shared in the y-axis across a single row and in the x-axis across a single column.
How does the uncertainty level differ in EDA, CDA, and Predictive Modeling?
- Uncertainty is equally distributed among all three.
- Uncertainty is highest in CDA, lower in Predictive Modeling, and lowest in EDA.
- Uncertainty is highest in EDA, lower in CDA, and lowest in Predictive Modeling.
- Uncertainty is highest in Predictive Modeling, lower in CDA, and lowest in EDA.
In EDA, where the primary aim is to explore patterns and relationships in the data, the level of uncertainty is highest. This reduces in CDA, which seeks to confirm the hypotheses generated during EDA. The uncertainty level is lowest in Predictive Modeling as it builds on the outcomes of EDA and CDA to make future predictions.
Can the steps of the EDA process be re-ordered or are they strictly sequential?
- Some steps can be reordered, but not all.
- The order of steps depends on the data set size.
- They are strictly sequential and cannot be reordered.
- They can be reordered based on the analysis needs.
The EDA process is generally sequential, starting from questioning and ending in communication. However, depending on the nature and needs of the analysis, some steps might be revisited. For instance, new questions might emerge during the explore phase, necessitating going back to the questioning phase. Or, additional data wrangling might be needed after exploring the data.
In what scenarios would it be more appropriate to use Kendall's Tau over Spearman's correlation coefficient?
- Datasets with many tied ranks
- Datasets with normally distributed data
- Datasets without outliers
- Large datasets with ordinal data
It might be more appropriate to use Kendall's Tau over Spearman's correlation coefficient in scenarios with datasets with many tied ranks. Kendall's Tau is better at handling ties than Spearman's correlation coefficient. It's often used in scenarios where the data have many tied ranks.
How does the curse of dimensionality relate to feature selection?
- It can cause overfitting
- It can make visualizing data difficult
- It increases computational complexity
- It reduces the effectiveness of distance-based methods
The curse of dimensionality refers to the various problems that arise when dealing with high-dimensional data. In the context of feature selection, high dimensionality can reduce the effectiveness of distance-based methods, as distances in high-dimensional space become less meaningful.
When the correlation coefficient is close to 1, it implies a strong ________ relationship between the two variables.
- Negative
- Neutral
- Positive
- Zero
When the correlation coefficient is close to 1, it implies a strong positive relationship between the two variables. This means as one variable increases, the other also increases.
_____ plots can give a high-level view of a single continuous variable but may hide details about the distribution.
- Bar
- Box
- Histogram
- Scatter
Histograms can provide a high-level view of a single continuous variable by showing the frequency of data points in different bins. However, due to the binning process, some details about the distribution might be hidden.
You've identified several outliers using the modified Z-score method in your dataset. What could be the possible reasons for their existence?
- All of these
- The data may have been corrupted
- The dataset may contain measurement errors
- The dataset may have a complex, multi-modal distribution
All these reasons could lead to the existence of outliers in a dataset.