You are dealing with a dataset where outliers significantly affect the mean of the distribution but not the median. What approach would you suggest to handle these outliers?
- Binning
- Removal
- Transformation
- nan
In this case, a transformation such as a log or square root transformation might be suitable. These transformations pull in high values, thereby reducing their impact on the mean.
The process of replacing each missing data point with a set of plausible values creating multiple complete data sets is known as ____________.
- Mean Imputation
- Mode Imputation
- Multiple Imputation
- Regression Imputation
This process is called multiple imputation. It generates several different plausible imputed datasets and the results from these are combined to produce the final analysis.
What is the relationship between the Z-score of a data point and its distance from the mean?
- The Z-score is independent of the distance from the mean
- The higher the Z-score, the closer the data point is to the mean
- The higher the Z-score, the further the data point is from the mean
- The lower the Z-score, the further the data point is from the mean
The higher the Z-score, the further the data point is from the mean. A Z-score of 0 indicates that the data point is identical to the mean score.
Using the ________ method for handling outliers, extreme values are grouped together and treated as a single entity.
- Binning
- Imputation
- Removal
- Transformation
The binning method involves grouping extreme values (outliers) together and treating them as a single entity by replacing them with a summary statistic like mean, median, or mode.
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 the context of data visualization, what is a pairplot primarily used for?
- Comparing multiple variables at once
- Showing the correlation between two variables
- Visualizing the distribution of a single variable
- Visualizing the relationship between two variables
Pairplots are primarily used for comparing multiple variables at once. It creates a grid of scatter plots for each pair of variables, which helps in understanding the relationships between all variables.
Which category of missing data implies that the probability of missingness is related to the observed data?
- MAR
- MCAR
- NMAR
- nan
MAR, which stands for Missing At Random, implies that the probability of missingness is related to the observed data.
A company has asked you to build a model that can predict customer churn based on a set of features. Which type of data analysis will you perform?
- All are equally suitable
- CDA
- EDA
- Predictive Modeling
Predictive Modeling would be most suitable in this case. It involves the application of machine learning algorithms to the data in order to make predictions about future outcomes, in this case, customer churn.
How does the choice of model in a model-based method impact the imputation process?
- The choice of model can cause overfitting
- The choice of model can influence the accuracy of the imputations
- The choice of model can introduce unnecessary complexity
- The choice of model has no impact
The choice of model in a model-based method can significantly influence the accuracy of the imputations. If the chosen model closely matches the actual data generation process, then the imputations will be accurate. However, if the model is a poor fit, the imputed values may be far from the true values, leading to biased results.
What is the biggest challenge in the 'wrangle' phase of the EDA process?
- Communicating the insights
- Dealing with missing values and other inconsistencies in the data
- Defining the right questions
- Drawing conclusions from the data
The wrangling phase of the EDA process can be challenging as it involves dealing with various data quality issues. These can include missing values, inconsistent data entries, outliers, and other anomalies. The analyst might need to make informed decisions about how to handle these issues without introducing bias or distorting the underlying information in the data.