Suppose you are dealing with time series data with some missing values and you decided to use regression imputation. What potential issues might arise and how could you address them?
- May lead to overfitting; Address by adding more data
- May violate independence assumption; Address by considering time dependence
- May violate uniform distribution; Address by transforming data
- No issues might arise
In time series data, observations are usually dependent on time, so the independence assumption of regression imputation may be violated. This issue can be addressed by considering time dependence in the regression model used for imputation, for example by including lagged variables.
How is Multicollinearity typically detected in a dataset?
- By calculating the Variance Inflation Factor (VIF).
- By performing a simple linear regression.
- By performing a t-test.
- By visually inspecting the data.
Multicollinearity is typically detected by calculating the Variance Inflation Factor (VIF). A high VIF indicates a high degree of multicollinearity between the independent variables.
After exploring and interpreting your data, you would '______' your findings in the EDA process.
- communicate
- conclude
- question
- wrangle
After exploring and interpreting your data, you would 'conclude' your findings in the EDA process. This is where you draw actionable insights from the data that you have analyzed and explored.
Imagine you're analyzing a dataset for a real estate company. You observe that a few houses have an extraordinarily high price compared to the rest. What would these represent in your analysis?
- Anomalies
- Data manipulation
- Errors in data collection
- Outliers
These could represent outliers. In the context of a dataset, outliers are individual data points that are distant from other observations.
How does 'questioning' in the EDA process differ from 'concluding'?
- Questioning involves data cleaning while concluding involves data visualization.
- Questioning involves defining variables, while concluding focuses on outlier detection.
- Questioning is about data transformation, while concluding is about hypothesis testing.
- Questioning sets the analysis goals, while concluding involves drawing insights from the explored data.
In the EDA process, questioning is the stage where the goals of the analysis are set. These are typically in the form of questions that the analysis aims to answer. On the other hand, concluding involves drawing meaningful insights from the data that have been analyzed in the explore phase. This could involve formal or informal hypothesis testing and aids in shaping subsequent data analysis steps, reporting, or decision-making.
What's the potential impact of incorrectly handled missing data on the convergence of a machine learning model during training?
- Depends on the missingness mechanism.
- Has no impact on convergence.
- Slows down convergence.
- Speeds up convergence.
If missing data are not correctly handled, the model may struggle to find optimal parameters, leading to slower convergence during training.
If you are to create a dashboard with multiple interlinked plots that respond dynamically to user inputs, which Python library would be most suitable for this task?
- Matplotlib
- Seaborn
- Bokeh
- Plotly
Plotly, especially when used with Dash, is a great option for creating interactive, web-based dashboards with multiple interlinked plots that respond dynamically to user inputs.
How does the 'explore' step in the EDA process aid in hypothesis generation?
- It aids in cleaning and transforming data.
- It helps in communicating the findings to stakeholders.
- It helps in defining the questions for analysis.
- It uncovers patterns, trends, relationships, and anomalies in the data.
The explore phase in the EDA process involves analyzing and investigating the data using statistical techniques and visualization methods. This step uncovers patterns, trends, relationships, and anomalies in the data, which can help in forming or refining hypotheses that could be formally tested in subsequent analysis steps.
A company surveyed its customers for their satisfaction scores, ranging from 1-10. The scores were heavily skewed to the right with a few customers giving a score of 1 or 2. Which measure of central tendency should the company use to present a typical customer experience?
- All are equally valid
- Mean
- Median
- Mode
The "Median" would be the best measure of central tendency in this scenario. Since the scores are heavily skewed to the right, the median would provide a more accurate representation of a typical customer's experience than the mean, which would be dragged down by the low scores.
The process of 'binning' to handle outliers involves grouping data into ________.
- Bins
- Deciles
- Percentiles
- Quartiles
In the process of binning, the data is grouped into 'bins', and the outliers are replaced with summary statistics like mean, median, or mode.