PCA assumes that the data follows a _______ distribution.
- Poisson
- binomial
- normal
- uniform
PCA makes the assumption that the data follows a multivariate normal distribution. This means that all linear combinations of the original variables also follow a normal distribution.
How does the concept of conditional probability relate to the Multiplication Rule?
- Conditional probabilities are the inverse of the Multiplication Rule
- The Multiplication Rule calculates conditional probabilities
- The Multiplication Rule can be rewritten using conditional probabilities
- They are unrelated concepts
Conditional probability and the Multiplication Rule are interconnected. The Multiplication Rule can be rewritten using conditional probabilities. Specifically, the Multiplication Rule states that the probability of two events A and B occurring (P(A ∩ B)) equals the probability of A given B (P(A
A ________ ANOVA is used when we want to compare more than two groups, and we have one categorical variable.
- Factorial
- One-way
- Three-way
- Two-way
A one-way ANOVA is used when we want to compare more than two groups, and we have one categorical variable. The 'one-way' refers to one independent variable or factor.
What happens to the width of a confidence interval as the confidence level increases?
- It decreases
- It fluctuates unpredictably
- It increases
- It stays the same
The width of a confidence interval increases as the confidence level increases. A higher confidence level means that you want to be more sure that you are capturing the true population parameter, which requires a wider interval.
What is the Central Limit Theorem and how does it relate to point and interval estimation?
- It implies that every data set is symmetrically distributed, which affects the reliability of point and interval estimations
- It suggests that all data has a central tendency and this affects the point and interval estimations
- It suggests that as sample size increases, the distribution of sample means approaches a normal distribution, which affects how we estimate population parameters
- It suggests that every large enough dataset is normally distributed, which is the foundation of point and interval estimations
The Central Limit Theorem states that when you have a sufficiently large sample, the distribution of the sample mean approximates a normal distribution, regardless of the shape of the population distribution. This allows us to make inferences about the population parameters using the sample mean and the standard error, which form the basis of point and interval estimation.
An event that cannot possibly occur has a probability of ________.
- -1
- 0
- 0.5
- 1
An event that cannot possibly occur is said to be impossible and has a probability of 0. This is in line with the definition of probability as a measure that takes values between 0 and 1, inclusive.
Bayes' theorem combines our prior knowledge about an event with evidence from data to provide a ________ probability.
- joint
- marginal
- posterior
- prior
The theorem combines our prior knowledge (the prior probability) and evidence (the likelihood) to provide a new, updated probability of an event (the posterior probability).
What are the components of a confidence interval?
- The population mean, the margin of error, and the level of confidence
- The population mean, the sample size, and the standard error
- The sample mean, the margin of error, and the level of confidence
- The sample mean, the population size, and the standard deviation
A confidence interval is composed of three parts: a point estimate (the sample mean), a margin of error (which depends on the standard error and the Z-value or T-value), and the level of confidence (which indicates the probability that the interval estimate contains the population parameter).
What does it mean when we say a non-parametric test makes fewer assumptions about the data distribution?
- The data distribution must be known
- The data does not have to follow a specific distribution, such as normal
- The data must be normally distributed
- The data must be uniformly distributed
When we say a non-parametric test makes fewer assumptions about the data distribution, we mean that the data does not have to follow a specific distribution, such as the normal distribution. Non-parametric tests are distribution-free tests and make no assumption about the probability distribution of the variables.
The Pearson's Correlation Coefficient measures the ________ between two variables.
- causal relationship
- linear correlation
- percentage similarity
- rank
Pearson's Correlation Coefficient measures the linear correlation between two variables. It quantifies the degree to which two variables are related to each other.