What kind of relationship does Pearson's Correlation Coefficient measure?
- Exponential
- Linear
- Monotonic
- Non-linear
Pearson's correlation coefficient measures linear relationships between variables. It measures the degree to which pairs of data for these two variables lie on a line.
In hypothesis testing, a Type II error is committed when the null hypothesis is ______ but we ______ to reject it.
- False, fail to reject
- False, reject
- True, fail to reject
- True, reject
A Type II error, also known as a false negative, occurs when we fail to reject a false null hypothesis. This means we've missed evidence of an effect or difference that truly exists.
What conditions must be met for the Central Limit Theorem to hold true?
- The data must be collected without any bias.
- The data must be normally distributed.
- The sample must be a simple random sample, and the sample size must be sufficiently large (typically n > 30).
- The sample size must be less than 30.
The Central Limit Theorem generally applies when the following conditions are met: 1) The data should be sampled randomly, 2) The sample values must be independent of each other, and 3) The sample size should be sufficiently large (typically, n > 30 is considered sufficient).
Spearman's Rank Correlation is a ________ method that does not make assumptions about the distribution of data.
- Non-parametric
- Parametric
- Quantitative
- Univariate
Spearman's Rank Correlation is a non-parametric method, which means it does not make assumptions about the distribution of data. This makes it more flexible and robust than some parametric methods.
What is the role of standard error in interval estimation?
- Standard error determines the shape of the distribution of the sample means
- Standard error is not related to interval estimation
- Standard error is used to calculate the margin of error, which determines the width of the confidence interval
- Standard error is used to calculate the sample mean, which is the center of the confidence interval
The standard error plays a crucial role in interval estimation. It is used to calculate the margin of error, which determines the width of the confidence interval. The standard error measures the variability of the sample mean around the population mean. A smaller standard error will result in a narrower confidence interval, assuming the confidence level is constant.
What is the effect of having small expected frequencies in a Chi-square test?
- It does not affect the test
- It increases the power of the test
- It invalidates the test
- It reduces the power of the test
In a Chi-square test, having small expected frequencies can reduce the power of the test and potentially lead to erroneous conclusions. This is because the Chi-square test is based on the assumption that the expected frequency of each category is at least 5.
A probability must be a number between ________ and ________.
- #NAME?
- -1, 1
- 0, 1
- 1, 100
By definition, the probability of an event is a number between 0 and 1. A probability of 0 means the event will never occur, and a probability of 1 means the event is certain to occur.
A Chi-square test for independence is used to determine if there is a significant relationship between two ________ variables.
- categorical
- continuous
- nominal
- ordinal
A Chi-square test for independence is used to determine if there is a significant relationship between two categorical variables. It is not applicable for continuous, ordinal, or nominal variables.
How does the presence of multicollinearity affect the standard errors of the regression coefficients?
- It does not affect the standard errors.
- It makes them equal to zero.
- It makes them larger.
- It makes them smaller.
In the presence of multicollinearity, the standard errors of the affected coefficients are inflated, leading to less precise and unstable estimates of the regression coefficients.
How can the effects of interaction be investigated in a two-way ANOVA?
- By calculating the F-statistic for each independent variable
- By checking the interaction term in the ANOVA table
- By conducting a one-way ANOVA
- By conducting post-hoc tests
The effects of interaction in a two-way ANOVA can be investigated by examining the interaction term in the ANOVA table. If the p-value for the interaction term is significant, it suggests that the effect of one independent variable on the dependent variable depends on the level of the other independent variable. Post-hoc tests can then be conducted to investigate the interaction effects further.
How is the 'mean' calculated for a data set?
- By arranging the values in ascending order
- By finding the middle value
- By finding the most frequent value
- By summing all values and dividing by the number of values
The mean of a data set is calculated by summing all the values and then dividing by the number of values. It gives the 'average' of the data and can be used for both discrete and continuous data sets. However, it can be heavily influenced by outliers or extreme values.
What is a uniform distribution?
- A distribution in which all outcomes are equally likely
- A distribution in which all outcomes follow a linear pattern
- A distribution where outcomes are not related
- A distribution where outcomes have a bell-shaped pattern
A uniform distribution, sometimes also known as a rectangular distribution, is a distribution that has constant probability. This distribution is often used in the cases where all outcomes are equally likely.