The ________ in Spearman's Rank Correlation indicates the strength and direction of association between two ranked variables.
- Coefficient
- Median
- P-value
- Rank
The coefficient in Spearman's Rank Correlation indicates the strength and direction of the association between two ranked variables. This coefficient can range from -1 (perfect negative correlation) to 1 (perfect positive correlation).
What is the purpose of a scatter plot?
- To compare two numerical variables
- To display a distribution
- To show the relationship between three variables
- To visualize categorical variables
A scatter plot is a graphical representation that uses dots to represent the values obtained for two different variables - one plotted along the x-axis and the other plotted along the y-axis. It helps to identify the type of relationship (if any) between two numerical variables.
Does PCA require the features to be on the same scale?
- Depends on the algorithm used
- Depends on the data
- No
- Yes
Yes, PCA requires the features to be on the same scale. If features are on different scales, PCA might end up giving higher weightage to features with higher variance, which could lead to incorrect principal components. So, it's typically a good practice to standardize the data before applying PCA.
What are communalities in factor analysis?
- They are the shared variance between variables
- They are the unique variances of variables
- They are the variances of the factors after rotation
- They represent the total variance of the factors
In factor analysis, communalities are the proportion of variance in each variable that is accounted for, or shared among the factors. They represent the shared variance between variables.
What is a Type I error in the context of hypothesis testing?
- Accepting a false null hypothesis
- Accepting a true null hypothesis
- Rejecting a false null hypothesis
- Rejecting a true null hypothesis
A Type I error occurs when the null hypothesis is true, but it is rejected. It is also known as a "false positive" result.
How does the power of a test relate to Type II errors?
- The power of a test is the probability of making a Type II error
- The power of a test is the probability of not making a Type II error
- The power of a test is unrelated to Type II errors
- nan
The power of a test is the probability that it correctly rejects a false null hypothesis, i.e., it is the probability of not making a Type II error.
What happens to the range of a dataset if an outlier is added?
- The effect on the range is unpredictable
- The range decreases
- The range increases
- The range remains the same
If an outlier is added to a dataset, it can significantly increase the range, as the range is calculated as the difference between the maximum and minimum values in the dataset.
When are the Addition and Multiplication Rules of Probability applicable?
- Both are used for mutually exclusive events
- Only for dependent events
- Only for independent events
- The Addition Rule is for mutually exclusive events and the Multiplication Rule is for independent events
The Addition Rule is applicable when calculating the probability of the occurrence of at least one of two mutually exclusive events, while the Multiplication Rule is used to calculate the probability of two independent events both occurring.
A numerical summary of a sample, as opposed to a population, is known as a ________.
- mean
- mode
- parameter
- statistic
In the field of statistics, a statistic is a numerical summary of a sample, as opposed to a population. It's a measure that is calculated from the sample data. For example, if we have data for a certain number of individuals from a larger group, the average of this data is a statistic.
How can undercoverage bias occur during sampling?
- By including every individual in the population in the sample
- By not including certain segments of the population in the sample
- By selecting too large of a sample
- By selecting too small of a sample
Undercoverage bias can occur during sampling if certain segments of the population are not included in the sample or are represented less than they should be. This can result in a sample that is not representative of the population, leading to biased estimates.