What is a primary assumption when using regression imputation?
- All data is normally distributed
- Missing data is missing completely at random (MCAR)
- Missing values are negligible
- The relationship between variables is linear
A primary assumption when using regression imputation is that the relationship between variables is linear. This is because regression imputation uses a regression model to predict missing values, and the basic form of regression models assumes a linear relationship between predictor and response variables.
You are working on a dataset and found that the model performance is poor. On further inspection, you found some data points that are far from the rest. What could be a possible reason for the poor performance of your model?
- Outliers
- Overfitting
- Underfitting
- nan
The poor performance of the model might be due to outliers in the dataset. Outliers can have a significant impact on the performance of machine learning models.
As a data scientist, you've realized that your dataset contains missing values. How would you handle this situation as part of your EDA process?
- Always replace missing values with the mean or median
- Choose an appropriate imputation method depending on the nature of the data and the type of missingness
- Ignore the missing values and proceed with analysis
- Remove all instances with missing values
Handling missing values is an important part of the EDA process. The method used to handle them depends on the nature of the data and the type of missingness (MCAR, MAR, or NMAR). Various imputation methods can be used, such as mean/median/mode imputation for MCAR or MAR data, and advanced imputation methods like regression imputation, multiple imputation, or model-based methods for NMAR data.
If the variance of a data set is zero, then all data points are ________.
- Equal
- Infinite
- Negative
- Positive
If the "Variance" of a data set is zero, then all data points are "Equal". Variance is a measure of how far a set of numbers is spread out from their average value. A variance of zero indicates that all the values within a set of data are identical.
A market research survey collects data on customer age, gender, and preference for a product (Yes/No). Identify the types of data present in this survey.
- Age: continuous, Gender: nominal, Preference: ordinal
- Age: nominal, Gender: ordinal, Preference: interval
- Age: ordinal, Gender: interval, Preference: ratio
- Age: ratio, Gender: ordinal, Preference: nominal
Age is a continuous data type because it can take on any value within a range. Gender is nominal as it's categorical with no order or priority. Preference is ordinal as it's categorical with a clear order (Yes is preferred to No).
Which measure of dispersion is defined as the difference between the largest and smallest values in a data set?
- Interquartile Range (IQR)
- Range
- Standard Deviation
- Variance
The "Range" is the measure of dispersion that is defined as the difference between the largest and smallest values in a data set.
The missing data mechanism where missingness is related only to the observed data is referred to as _________.
- All missing data
- MAR
- MCAR
- NMAR
In MAR (Missing at Random), the missingness is related only to the observed data.
You are given a dataset for an upcoming data analysis project. What initial EDA steps would you take before moving to model building?
- Explore the structure of the dataset, summarize the data, and create visualizations
- Perform a detailed statistical analysis
- Run a quick ML model to test the data
- Start cleaning and wrangling the data
Before moving to model building, it's important to first understand the dataset you're working with. The initial EDA steps would typically include exploring the structure of the dataset, summarizing the data (such as calculating central tendency measures and dispersion), and creating visualizations to uncover patterns, trends, and relationships.
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).