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