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.
Describe the impact of skewness and kurtosis on parametric testing.
- They can improve the accuracy of parametric testing.
- They can invalidate the results of parametric testing.
- They can reduce the variance in parametric testing.
- They do not impact parametric testing.
Skewness and kurtosis can invalidate the results of parametric testing. Many parametric tests assume that the data follows a normal distribution. If the data is highly skewed or has high kurtosis, these assumptions are violated, and the test results may not be valid.
If a distribution is leptokurtic, what does it signify about the data?
- The data has a high variance.
- The data is heavily tailed with potential outliers.
- The data is less outlier-prone.
- The data is normally distributed.
Leptokurtic distribution signifies that the data has heavy tails and a sharp peak, meaning there are substantial outliers (or extreme values). This kind of distribution often indicates that the data may have more frequent large jumps away from the mean.
A potential drawback of the Z-score method for outlier detection is that it assumes the data is _______ distributed.
- exponentially
- logistically
- normally
- uniformly
The Z-score method assumes that the data is normally distributed, which may not be the case with all datasets, and is a drawback.