A) The probability of obtaining results at least as extreme as the observed results, given that the null hypothesis is true B) The measure of confidence in the null hypothesis C) The population parameter being tested D) The significance level for accepting the null hypothesis
A) Mann-Whitney U test B) Kruskal-Wallis test C) Wilcoxon signed-rank test D) t-test
A) To summarize categorical data B) To test for differences in means C) To identify outliers in a dataset D) To examine the relationship between variables
A) The spread of the data B) The central tendency of a dataset C) The strength and direction of a linear relationship between two variables D) The variability within groups
A) To predict future data points B) To estimate the range within which the population parameter is likely to fall C) To determine the probability of an event occurring D) To compare two independent groups
A) Cluster sampling B) Simple random sampling C) Systematic sampling D) Convenience sampling
A) The hypothesis that the researcher believes to be true B) A statement that there is no significant difference between specified populations C) A statement that predicts an outcome in an experiment D) The hypothesis that is tested using a one-tailed test
A) Correlation indicates a relationship between variables, while causation implies one variable causes a change in the other B) Correlation is used for categorical data, while causation is used for continuous data C) Correlation refers to linear relationships, while causation refers to non-linear relationships D) Correlation measures the strength of a relationship, while causation measures the direction
A) To state that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases B) To calculate the range of a dataset C) To determine the variability within groups D) To compare two different samples
A) The measure of correlation between two variables B) The probability of rejecting the null hypothesis when it is actually true C) The level of confidence in the alternative hypothesis D) The margin of error in the sample mean
A) Regression analysis B) T-test C) Chi-square test D) ANOVA
A) Time series analysis. B) Cluster analysis. C) Factor analysis. D) Regression analysis.
A) Logistic regression. B) Linear regression. C) Ridge regression. D) Polynomial regression.
A) Regression analysis. B) T-test. C) ANOVA. D) Chi-square test.
A) Feature engineering. B) Outlier detection. C) Imputation. D) Normalization. |