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.
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.
What does the term 'joint probability' mean?
- The probability of at least one of two events
- The probability of both of two events
- The probability of two independent events
- The probability of two mutually exclusive events
Joint probability is a statistical term describing the likelihood of two events happening at the same time. It's the probability of the intersection of two or more events, often denoted as P(A ∩ B) for events A and B.
In a dataset, if the _______ is zero, then all the numbers in the set are the same.
- Mean
- Range
- Standard Deviation
- Variance
If the variance of a dataset is zero, then all the numbers in the set are the same. Variance measures how far a set of numbers is spread out from their average value. If all the numbers in the dataset are identical, there would be no dispersion and the variance would be zero.
What does a Spearman’s Rank Correlation coefficient of 0 indicate?
- Data cannot be ranked
- No correlation
- Perfect negative correlation
- Perfect positive correlation
A Spearman’s Rank Correlation coefficient of 0 indicates that there is no correlation, meaning changes in one variable do not correspond to changes in the other variable.
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.