What is the implication of multicollinearity in polynomial regression?

  • It increases the fit of the model to the training data
  • It increases the interpretability of the model
  • It reduces the complexity of the model
  • It reduces the precision of coefficient estimates
Multicollinearity in polynomial regression can reduce the precision of the coefficient estimates and cause them to be highly sensitive to minor changes in the model. This can lead to unstable and unreliable estimates, making it difficult to interpret the model and infer about the relationships between variables.

The Mann-Whitney U test is used when data is ________, which means it can't be reasonably fit to a normal distribution.

  • non-parametric
  • normally distributed
  • parametric
  • skewed
The Mann-Whitney U test is a non-parametric test, meaning it can be used when data can't be reasonably fit to a normal distribution.

What is the primary objective of statistics in data science?

  • Data storage
  • Data visualization
  • To make decisions based on data analysis
  • Web design
The primary goal of statistics in data science is to provide a foundation for decision making based on data analysis. It is a discipline that provides tools and methods to interpret and understand data, answer specific questions, and visualize data in a meaningful way. This field of study is crucial in areas where constructing decisions are essential, such as business strategies, scientific research, policy making, etc.

Does the Central Limit Theorem apply to all distributions?

  • No, it only applies to normal distributions.
  • No, it only applies to uniform distributions.
  • Yes, but only when the sample size is sufficiently large and the distribution has finite variance.
  • Yes, regardless of the sample size.
The Central Limit Theorem (CLT) applies to the sampling distribution of the mean for a wide range of underlying distributions, provided the sample size is sufficiently large and the underlying distribution has finite variance.

What is the purpose of sampling in statistical analysis?

  • To create charts and graphs
  • To estimate population parameters
  • To gather data from every member of a population
  • To increase the variability of data
Sampling in statistical analysis is primarily used to estimate population parameters. Since it's often impractical or impossible to gather data from every individual in a population, we use samples to make inferences about the population as a whole.

What is the null hypothesis of the Spearman's Rank Correlation test?

  • The variables are not related
  • The variables have a negative correlation
  • The variables have a positive correlation
  • There is no monotonic relationship between the variables
The null hypothesis of the Spearman's Rank Correlation test is that there is no monotonic relationship between the variables. That is, changes in one variable do not consistently correspond to changes in the other variable.

What is 'dendrogram' in hierarchical clustering?

  • A diagram showing the change in the number of clusters
  • A graph showing the distribution of clusters
  • A tree-like diagram that represents the hierarchy of clusters
  • The center point of a cluster
A dendrogram is a tree-like diagram that is used in hierarchical clustering to represent the hierarchy of clusters. Each join in the dendrogram represents the two clusters merging, and the height of the join is the distance between those clusters.

When a data distribution is skewed, which measure of central tendency is typically the most reliable?

  • Mean
  • Median
  • Mode
  • nan
The median is usually the most reliable measure of central tendency when a data distribution is skewed. Unlike the mean, the median isn't influenced by extreme values. Therefore, in a skewed distribution, the median generally gives a better idea of the typical value than the mean.

Polynomial regression allows us to model a relationship between the dependent variable and independent variables as a _________.

  • High
  • Linear equation
  • Non-linear equation
  • Straight line
Polynomial regression allows us to model the relationship between the dependent variable and independent variables as a non-linear equation. This is achieved by raising independent variables to a power, allowing the model to fit more complex data patterns.

How does the sample size affect the power of the Kruskal-Wallis Test?

  • It depends on the data
  • Larger sample sizes decrease power
  • Larger sample sizes increase power
  • Sample size has no effect on power
Larger sample sizes increase the power of the Kruskal-Wallis Test. Power is the ability of a test to detect a true effect when there is one.