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.
What can cause the Chi-square test for goodness of fit to be biased?
- Having a large sample size
- Having a small sample size
- Having equal expected frequencies in all categories
- Having normally distributed data
A small sample size can lead to unreliable results in a Chi-square test for goodness of fit. This can be due to the fact that the test requires a sufficient number of observations in each category to provide a reliable estimate of the distribution.
A ________ distribution has a constant probability.
- Binomial
- Normal
- Poisson
- Uniform
A uniform distribution is a type of probability distribution in which all outcomes are equally likely. This implies a constant probability for all outcomes.
In the factor analysis, the _______ measures the amount of variance in all the variables which is accounted for by that factor.
- communality
- eigenvalue
- factor variance
- total variance
In the factor analysis, the eigenvalue measures the amount of variance in all the variables which is accounted for by that factor.
Why might you perform a paired t-test?
- All of the above
- To compare the means of the same group at two different times
- To compare the means of two different populations
- To compare two independent groups
A paired t-test is used to compare the means of the same group at two different times or under two different conditions. It is not used to compare independent groups or different populations.
The ________ of a random variable is the sum of the probabilities of all possible outcomes.
- Distribution
- Expected value
- Mean
- Variance
The "expected value" of a random variable is the sum of all possible values it can take, each multiplied by the probability of that outcome. It gives us the mean or average value of the random variable and is a fundamental concept in probability theory and statistics.
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.