Which technique for handling missing data replaces missing values with the median of the available data?
- Listwise Deletion
- Median Imputation
- Mode Imputation
- Regression Imputation
'Median Imputation' is a method that replaces missing values with the median of the available data. This technique is useful because it is not influenced by outliers, but it can potentially distort the original distribution of data.
How does EDA contribute to the model building process in Machine Learning?
- By defining the ML algorithm to be used
- By fine-tuning the hyperparameters of the ML model
- By providing insights into the nature of data, and identifying trends and outliers
- By testing the performance of the ML model
EDA is integral to the model-building process in Machine Learning as it provides insights into the nature of the data, and identifies trends, patterns and outliers. These insights help to determine which Machine Learning models might be most appropriate to apply and can guide the feature engineering process.
In a longitudinal study on childhood development, some data points are missing randomly due to logistical issues during data collection. How would you classify this missing data?
- MAR
- MCAR
- NMAR
- Not missing data
This would be MCAR (Missing Completely at Random) because the reason for the missing data (logistical issues) has nothing to do with the observed or unobserved data. It's entirely random.
You're visualizing a bivariate data set using a scatter plot and notice an isolated group of points far from the main concentration of data. How would you categorize these points?
- Negative correlation
- Normal data points
- Outliers
- Positive correlation
In a scatter plot, a group of points that are isolated from the main concentration of data could be categorized as outliers.
Multicollinearity refers to a situation where _________.
- All variables in a model are perfectly uncorrelated
- Two or more predictors in a regression model are highly correlated
- Two variables are uncorrelated
- Two variables have a correlation coefficient of zero
Multicollinearity refers to a situation in which two or more predictors in a regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy.
You have to present the sales data of a company over 10 years to the board of directors. What type of graph should you choose and why?
- Histogram, because it shows distributions
- Line graph, because it shows trends over time
- Pie chart, because it shows proportions
- Scatter plot, because it shows relationships between variables
A line graph would be the most suitable choice for presenting sales data over time. Line graphs are excellent for showing continuous data over time set at equal intervals, like years in this case. This would allow the board of directors to easily see trends, patterns, and fluctuations in sales data.
The _____ is the most appropriate measure of central tendency when the distribution of data is heavily skewed.
- Mean
- Median
- Mode
- Standard Deviation
The "Median" is the most appropriate measure of central tendency when the distribution of data is heavily skewed. This is because it is less affected by outliers and skewed data.
The kernel in a kernel density plot is a _____ function.
- Exponential
- Linear
- Logarithmic
- Smoothing
In a kernel density plot, the kernel is a smoothing function. It takes the raw data and smooths it into a continuous curve, providing a clear picture of the distribution and density of data.
Consider a situation where a Spearman's correlation coefficient between two variables was found to be significantly different from zero, but the Pearson's correlation was not. How would you explain this?
- Data has outliers
- Data is normally distributed
- Relationship between variables is linear
- Relationship between variables is non-linear
If Spearman's correlation is significantly different from zero, but the Pearson's correlation is not, it suggests that the relationship between the variables is not linear. Spearman's correlation, unlike Pearson's, can capture monotonic relationships that are not necessarily linear.
Which type of missing data relies on information that is not included in the dataset?
- MAR
- MCAR
- NMAR
- nan
NMAR (Not Missing At Random) type of missing data relies on information that is not included in the dataset.