How does standardization (z-score) affect the distribution of data?
- It doesn't affect the shape of the distribution
- It makes the distribution normal
- It makes the distribution uniform
- It skews the distribution
Standardization does not change the shape of the distribution of the feature; rather, it standardizes the scale. This means that it doesn't change the distribution's skewness or kurtosis but it does center the data around zero with a standard deviation of 1.
You are analyzing the number of calls received by a call center per hour. Which distribution would be most suitable for modeling this data and why?
- Binomial Distribution because it represents the number of successes in a given number of trials
- Normal Distribution because it represents continuous data
- Poisson Distribution because it models the number of events occurring in a fixed interval of time
- Uniform Distribution because all outcomes are equally likely
The Poisson Distribution is most suitable for modeling the number of calls received by a call center per hour because it models the number of events (calls) occurring in a fixed interval of time (per hour).
Consider a data distribution with a positive skewness and a high kurtosis. What does this scenario indicate about the distribution?
- It has a symmetrical distribution.
- It has evenly spread out values.
- It has many values clustered around the left tail with potential outliers.
- It has many values clustered around the right tail with potential outliers.
Positive skewness and high kurtosis imply that the data is heavily tailed to the right and the peak is sharp. Most of the data values are concentrated around the left tail, but there are potential outliers towards the more positive values.
What range of values does a dataset typically have after Min-Max scaling?
- -1 to 1
- 0 to 1
- Depends on the dataset
- Depends on the feature
Min-Max scaling transforms features by scaling each feature to a given range. The default range for the Min-Max scaling technique is 0 to 1. Therefore, after Min-Max scaling, the dataset will typically have values ranging from 0 to 1.
What is the term for the measure of how spread out the values in a data set are?
- Central Tendency
- Dispersion
- Kurtosis
- Skewness
The term for the measure of how spread out the values in a data set are is called "Dispersion". It includes range, interquartile range (IQR), variance, and standard deviation.
You've created a histogram of your data and you notice a few bars standing alone far from the main distribution. What might this suggest?
- Data is evenly distributed
- Normal distribution
- Outliers
- Skewness
In a histogram, bars that stand alone far from the main distribution often suggest the presence of outliers.
You have a dataset where the relationships between variables are not linear. Which correlation method is better to use and why?
- Covariance
- Kendall's Tau
- Pearson's correlation coefficient
- Spearman's correlation coefficient
For non-linear relationships between variables, Spearman's correlation coefficient would be a better choice. This is because Spearman's correlation measures the monotonic relationship between two variables and does not require the relationship to be linear.
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.