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.
Which type of graph would be most suitable for showing the relationship between two variables?
- Bar graph
- Histogram
- Pie chart
- Scatter plot
A scatter plot is most suitable for showing the relationship between two variables. Each point on the plot corresponds to two data values, with the position along the X and Y-axis representing the values of the two variables. This allows patterns and relationships to be identified visually.
You are required to create a complex statistical plot to identify and present possible correlations between multiple variables in your dataset. Which Python library would be the most appropriate for this task?
- Bokeh
- Matplotlib
- Plotly
- Seaborn
Seaborn is best suited for creating complex statistical plots. It provides high-level, attractive statistical plots and integrates well with pandas DataFrames, allowing direct use of column names for the axes and other arguments.
How does kurtosis impact the interpretation of data distribution?
- It affects how we perceive the outliers and tail risks.
- It affects the reliability of the mean.
- It changes the standard deviation of the dataset.
- It influences the choice of graph to use.
Kurtosis impacts the interpretation of data distribution by affecting how we perceive the outliers and tail risks. High kurtosis indicates a high probability of extreme outcomes, whereas low kurtosis suggests a lower chance of extreme outcomes.
You are given the variance of a data set. How can you use this information to find the standard deviation, and why might you want to do this?
- Add up all the variances to get the standard deviation
- Divide the variance by the number of data points to get the standard deviation
- Square the variance to get the standard deviation
- Take the square root of the variance to get the standard deviation
If you are given the variance, you can "Take the square root of the variance to get the standard deviation". This is useful because the standard deviation is in the same units as the original data, making it more interpretable.
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.
When is it more appropriate to use a correlation matrix instead of a pairplot?
- When the dataset is very large
- When the dataset is very small
- When the variables are not numeric
- When there are only two variables
When dealing with a large number of variables, a correlation matrix can be a more appropriate choice than a pairplot. This is because pairplots can become too complex and unreadable when the number of variables increases.
What is variance in the context of a data set?
- The average deviation from the mean
- The average squared deviation from the mean
- The range of the data
- The square root of the average deviation from the mean
"Variance" in the context of a data set is the "Average squared deviation from the mean". It gives a measure of how data points vary from the mean and is used to calculate the standard deviation.
What does Min-Max scaling do to the dataset?
- It reduces the dimensionality of the dataset
- It removes the mean and scales the data to unit variance
- It scales the data based on median and interquartile range
- It scales the dataset so that all feature values are in the range 0 to 1
Min-Max scaling, also known as normalization, transforms features by scaling each feature to a specific range, typically 0 to 1. This is done using the values of the minimum and maximum feature in the dataset.
How does the 'hue' parameter in Seaborn alter the visual presentation of data?
- Changes the color of elements
- Changes the shape of markers
- Changes the size of markers
- Rotates the plot
In Seaborn, the 'hue' parameter changes the color of elements. It is used to provide a color encoding for a third (typically categorical) variable in addition to two numeric variables.