In a _____ plot, the width of the "violin" indicates the frequency or density of data.
- Bar
- Box
- Scatter
- Violin
In a Violin plot, the width of the "violin" (or the density plot on each side) varies with the estimated density of data points at a given level. The wider the plot, the higher the density of data points at that value.
During an experiment, you discover that a certain variable is presenting a high number of outliers. What might this suggest about your data collection process?
- Both are possible
- Data collection process is accurate
- Data collection process is flawed
- Neither of these is possible
A high number of outliers might suggest that there are issues with the data collection process, such as measurement errors or other issues.
What is the primary cause of outliers in normally distributed data?
- All of these
- Data entry errors
- Data processing errors
- Measurement errors
Outliers in normally distributed data can be a result of various factors such as data entry errors, measurement errors, or errors in data processing.
In Plotly, the ________ object is the top-level container for all plot attributes.
- Diagram
- Figure
- Graph
- Plot
In Plotly, the 'Figure' object is the top-level container in which all plot-related attributes such as data and layout are stored.
How does EDA help in understanding the underlying structure of data?
- By cleaning data
- By modelling data
- By summarizing data
- By visualizing data
EDA, particularly data visualization, plays a crucial role in understanding the underlying structure of data. Visual techniques such as histograms, scatterplots, or box plots, can uncover patterns, trends, relationships, or outliers that would remain hidden in raw, numerical data. Visual exploration can guide statistical analysis and predictive modeling by revealing the underlying structure and suggesting hypotheses.
A market research survey collects data on customer age, gender, and preference for a product (Yes/No). Identify the types of data present in this survey.
- Age: continuous, Gender: nominal, Preference: ordinal
- Age: nominal, Gender: ordinal, Preference: interval
- Age: ordinal, Gender: interval, Preference: ratio
- Age: ratio, Gender: ordinal, Preference: nominal
Age is a continuous data type because it can take on any value within a range. Gender is nominal as it's categorical with no order or priority. Preference is ordinal as it's categorical with a clear order (Yes is preferred to No).
You notice that using the Z-score method for a particular data set is yielding too many outliers. What modifications can you make to the method to reduce the number of outliers detected?
- Decrease the Z-score threshold
- Increase the Z-score threshold
- Use the IQR method instead
- Use the modified Z-score method instead
Increasing the Z-score threshold will mean fewer points will exceed it, thus fewer outliers will be identified.
How can EDA assist in identifying errors or anomalies in the dataset?
- By conducting a statistical test of normality
- By creating a correlation matrix of the variables
- By running the dataset through a predefined ML model
- By summarizing and visualizing the data, which can reveal unexpected values or patterns
EDA, especially through summarizing and visualizing data, can assist in identifying errors or anomalies in the dataset. Graphical representations of data often make it easier to spot unexpected values, patterns, or aberrations that may not be apparent in the raw data.
When applying regression imputation, what factors need to be taken into consideration?
- Both dependent and independent variables
- None of the variables
- Only the dependent variable
- Only the independent variables
When applying regression imputation, both dependent and independent variables need to be taken into consideration. A regression model is built using the complete cases and then this model is used to predict the missing values in the incomplete cases. Therefore, it is important to carefully consider which variables to include in the regression model.
When would it be appropriate to use 'transformation' as an outlier handling method?
- When the outliers are a result of data duplication
- When the outliers are errors in data collection
- When the outliers are extreme but legitimate data points
- When the outliers do not significantly impact the data analysis
Transformation is appropriate to use as an outlier handling method when the outliers are extreme but legitimate data points that carry valuable information.