When we use Min-Max scaling, the transformed data will fall into the range of ____ to ____.
- -1 to 1
- -5 to 5
- 0 to 1
- 0 to 10
In Min-Max scaling, the transformed data will fall into the range of 0 to 1. This is because Min-Max scaling subtracts the minimum value from each data point and then divides by the range of the data (maximum - minimum).
What do we call the technique of deleting pairs of data where one is missing in a pairwise analysis?
- Listwise Deletion
- Mean Imputation
- Pairwise Deletion
- Regression Imputation
The technique of deleting pairs of data where one is missing in a pairwise analysis is called 'Pairwise Deletion'. This method maximizes the amount of data retained, as it only removes the specific pairs with a missing value rather than the entire row, but it can lead to inconsistent results due to different pairs being used in different analyses.
What kind of data visualization would be most suitable for high-dimensional datasets?
- Bar chart
- Parallel coordinates or a scatter plot matrix
- Pie chart
- Scatter plot
Visualizing high-dimensional datasets (those with many variables) can be challenging. However, techniques like parallel coordinates or a scatter plot matrix can help. Parallel coordinates plot each variable on a separate column, and lines connecting the columns represent individual data points. A scatter plot matrix shows all pairwise scatter plots of the variables.
In a scenario where you have to visualize real-time data for a live audience, what factors would you consider in your data visualization strategy?
- Complexity of the graph, because it needs to impress the audience
- Simplicity and clarity, because the audience needs to understand the data quickly
- The amount of data, because more data is always better
- The color scheme, because it needs to be eye-catching
When visualizing real-time data for a live audience, simplicity and clarity are key factors. The audience needs to understand the data quickly as it updates in real time. A clear and straightforward graph type, simple labels, and a thoughtful color scheme can help achieve this.
Why might you prefer to use multiple imputation over a simpler method like mean imputation?
- Mean imputation always leads to bias
- Multiple imputation is easier to use
- Multiple imputation is quicker
- Multiple imputation provides more accurate estimates
You might prefer to use multiple imputation over a simpler method like mean imputation because multiple imputation provides more accurate estimates. This is because it estimates multiple values for each missing value, reflecting the uncertainty around the true value. It also better preserves the relationships between variables.
_______ is a type of data analysis that helps in formulating hypotheses while the primary purpose of _______ is to test the formulated hypotheses.
- CDA, EDA
- EDA, CDA
- EDA, Predictive Modeling
- Predictive Modeling, EDA
EDA (Exploratory Data Analysis) is used to understand data patterns or trends and to formulate hypotheses, while CDA (Confirmatory Data Analysis) is applied to test those formulated hypotheses.
What is the name of the statistical measure that shows the degree of the relationship between two variables?
- Correlation coefficient
- Mean
- Standard deviation
- Variance
The statistical measure that shows the degree of relationship between two variables is called the correlation coefficient. It quantifies the direction and strength of the relationship between pairs of variables. Values range between -1 and +1, with -1 indicating a perfect negative correlation, +1 a perfect positive correlation, and 0 no correlation.
In a scatter plot, outliers often appear as points that are far removed from the ___________.
- axes
- main concentration of data
- origin
- trend line
In a scatter plot, outliers are often represented as points that are far removed from the main concentration of data.
________ is a measure of dispersion that is particularly useful when the data set has outliers.
- Interquartile Range
- Range
- Standard Deviation
- Variance
The "Interquartile Range (IQR)" is particularly useful when the dataset has outliers because it only considers the middle 50% of the data. This makes it a robust measure of dispersion.
In a situation where the initial 'questioning' phase did not yield actionable insights, what might be the next step in the EDA process?
- Jump to the concluding phase to draw insights
- Proceed to the exploring phase without adjustment
- Revisit the questioning phase to refine or develop new questions
- Skip to the communication phase
If the initial 'questioning' phase does not yield actionable insights, it is necessary to revisit the questioning phase to refine or develop new questions. The questions set the direction of the analysis and are crucial for subsequent steps. If the questions are not well defined or not actionable, it could lead to an ineffective analysis.