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.
The Z-score and T-score are both types of _______ scores, which measure the number of standard deviations an observation is from the mean.
- mean
- median
- standard
- variance
The Z-score and T-score are both types of standard scores. They measure the number of standard deviations an observation or statistic is from the mean.
A ________ ANOVA is used when we have two independent variables and want to understand if there is an interaction between them.
- Factorial
- One-way
- Three-way
- Two-way
A two-way ANOVA is used when there are two independent variables. This type of ANOVA assesses the main effects of each independent variable and the interaction effect between the variables.
Can Pearson's Correlation Coefficient determine causality?
- No, never
- Yes, always
- Yes, but additional information is required
- Yes, but only if the coefficient is 1 or -1
No, Pearson's Correlation Coefficient cannot determine causality. It can only measure the degree of linear correlation between two variables. While two variables may be correlated, it does not mean that changes in one variable cause changes in the other. Correlation does not imply causation.
What assumptions are made when conducting an ANOVA test?
- Independent observations, no outliers, equal sample sizes
- Independent observations, normal distribution of variables, no outliers
- Independent observations, normally distributed residuals, homoscedasticity
- No missing data, normally distributed residuals, no outliers
ANOVA makes three key assumptions: 1) Observations are independent. 2) Residuals (the differences between the observed and predicted values) are normally distributed. 3) The variance of the residuals is the same for all groups (homoscedasticity).
What does a scatter plot with points clustered tightly around a line indicate?
- A strong correlation
- A weak correlation
- An undefined correlation
- No correlation
When points in a scatter plot are clustered tightly around a line, it indicates a strong correlation between the two variables. The line is typically a line of best fit or regression line.
The _________ states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger—no matter what the shape of the population distribution.
- Central Limit Theorem
- Law of Large Numbers
- Probability Rule
- Sampling Distribution
The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger—no matter what the shape of the population distribution. This allows us to apply normal probability calculations to situations that might not initially seem appropriate for them.