How might the transformation method for handling outliers impact the overall shape of your data distribution?
- It can introduce multimodality into the distribution
- It can make the distribution more skewed
- It can make the distribution more symmetrical
- nan
The transformation method can make the distribution more symmetrical by pulling in extreme values.
Which method of analysis focuses on the exploration of patterns and relationships in the data?
- CDA
- Data Wrangling
- EDA
- Predictive Modeling
EDA (Exploratory Data Analysis) focuses on exploring patterns and relationships in the data. It is an approach to analyzing data sets to summarize their main characteristics, often with visual methods.
While using regression imputation, you encounter a situation where the predicted value for the missing data is outside the expected range. How might you resolve this issue?
- Constrain the predictions within the expected range
- Ignore the problem
- Transform the data
- Use a different imputation method
When the predicted value for missing data is outside the expected range, you might want to constrain the predictions within the expected range. By setting logical bounds, you can make sure that the imputed values are consistent with the known characteristics of the data.
What is skewness in the context of data analysis?
- The asymmetry of the distribution.
- The peak of the distribution.
- The range of the distribution.
- The symmetry of the distribution.
Skewness refers to a distortion or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data. If the curve of a data distribution is skewed to the left or to the right, it means the data are asymmetrical.
You are given a dataset with several missing values that are missing at random. You decided to use multiple imputation. What steps will you follow in applying this method?
- Create several imputed datasets, analyze separately, then average results
- Create several imputed datasets, analyze them together, then interpret results
- Impute only once, then analyze
- Impute several times using different methods, then analyze
The correct approach for multiple imputation is to create several imputed datasets, analyze them separately, and then combine the results. This accounts for the uncertainty around the missing values and results in valid statistical inferences.
You are analyzing a dataset with a high degree of negative skewness. How might this affect your choice of machine learning model?
- It might lead to a preference for models that are based on median values.
- It might lead to a preference for models that are not sensitive to outliers.
- It might lead to a preference for models that are sensitive to outliers.
- It would not affect the choice of the machine learning model.
A high degree of negative skewness indicates the possibility of extreme values towards the negative end of the distribution. This might influence the choice of machine learning models, preferring those that are not sensitive to outliers, such as tree-based models, or those that make fewer assumptions about the data distribution.
In what way does improper handling of missing data affect regularization techniques in a machine learning model?
- Depends on the regularization technique used.
- Does not impact regularization.
- Makes regularization less effective.
- Makes regularization more effective.
If missing data are not handled correctly, it can skew the model's learning and affect its complexity, making regularization techniques (which aim to control model complexity) less effective.
How does a high kurtosis value in a data set impact the Z-score method for outlier detection?
- It decreases the number of detected outliers
- It does not impact the detection of outliers
- It improves the accuracy of outlier detection
- It increases the number of detected outliers
A high kurtosis value means that the data has heavy tails or outliers. This can impact the Z-score method by increasing the number of detected outliers as Z-score is sensitive to extreme values.
_____ data provides numerical measurements and it can be broken down into two subcategories: continuous and discrete.
- Nominal
- Ordinal
- Qualitative
- Quantitative
Quantitative data provides numerical measurements and it can be divided into two types: continuous (data that can take any value within a range) and discrete (data that can only take certain values).
In what scenario would a Poisson Distribution be a better fit than a Normal Distribution?
- When modeling the number of times an event occurs in a fixed interval
- When the data are continuous
- When the data are negatively skewed
- When the data are positively skewed
A Poisson Distribution would be a better fit when modeling the number of times an event occurs in a fixed interval of time or space. The Poisson Distribution is discrete while the Normal Distribution is continuous.