Can the IQR method be applied to multimodal data sets for outlier detection? Explain.
- No, it can only be applied to normally distributed data
- No, it only works with unimodal distributions
- Yes, but it may not be effective
- Yes, it works well with any distribution
The IQR method can be applied to multimodal datasets for outlier detection, but it may not be effective as it's based on percentiles which can be influenced by multiple modes.
In _____ scaling, we scale the data between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).
- Min-Max
- Robust
- Standard
- Z-score
In Robust scaling, we scale the data between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). This approach minimizes the impact of outliers.
What is the primary goal of Exploratory Data Analysis (EDA)?
- To confirm a pre-existing hypothesis
- To create an aesthetic representation of the data
- To make precise predictions about future events
- To understand the underlying structure of the data
The primary goal of EDA is to understand the underlying structure of the data, including distribution, variability, and relationships among variables. EDA allows analysts to make informed decisions about further data processing steps and analysis.
Data that follows a _____ Distribution has its values spread evenly across the range of possible outcomes.
- Binomial
- Normal
- Poisson
- Uniform
Data that follows a Uniform Distribution has its values spread evenly across the range of possible outcomes.
What is a key difference between qualitative data and quantitative data when it comes to analysis methods?
- All types of data are analyzed in the same way
- Qualitative data is always easier to analyze
- Qualitative data typically requires textual analysis, while quantitative data can be analyzed mathematically
- Quantitative data can't be used for statistical analysis
Qualitative data often requires textual or thematic analysis, categorizing the data based on traits or characteristics. Quantitative data, being numerical, can be analyzed using mathematical or statistical methods.
The _________ method in regression analysis can help reduce the impact of Multicollinearity.
- Chi-Square
- Least squares
- Logistic Regression
- Ridge Regression
Ridge Regression is a regularization technique that can help reduce the impact of multicollinearity. It adds a penalty equivalent to square of the magnitude of coefficients to the loss function, thereby shrinking the coefficients of correlated predictors and reducing their impact.
Which measure of central tendency divides a data set into two equal halves?
- Mean
- Median
- Mode
- nan
The "Median" is the measure of central tendency that divides a data set into two equal halves. It is the middle score for a set of ordered data such that 50% of the scores are above it, and 50% are below it.
__________ missing data occurs when the probability of an observation being missing depends on both observed and unobserved data.
- All missing data
- MAR
- MCAR
- NMAR
NMAR (Not Missing at Random) missing data occurs when the missingness depends on both observed and unobserved data.
How does the assumption of MAR differ from MCAR in terms of data missingness?
- MAR assumes the missingness is only related to the observed data
- MAR assumes the missingness is related to the unobserved data
- MAR assumes the missingness is unrelated to any variable
- There's no difference between MAR and MCAR
In MCAR, the missingness is completely random and doesn't depend on any variable. In MAR, the missingness is not random but is related only to the observed data, not the unobserved (missing) data.
What is the effect of 'binning' on the overall variance of the dataset?
- It can either increase or decrease the variance
- It decreases the variance
- It does not affect the variance
- It increases the variance
Binning reduces the variance of a dataset by replacing outlier values with summary statistics like the bin mean or median, hence, reducing the spread of data.