MS2 MIDTERM EXAM

The degrees of freedom for Spearman's rho are calculated as:

• n - 1
• n
• (n - 1) / 2
• (n - 2)

Spearman's rho is appropriate for analyzing:

• Independent samples
• Matched pairs
• One categorical variable and one continuous variable
• Two independent continuous variables

Spearman's rho can detect:

• Only linear relationships
• Only nonlinear relationships
• Both linear and nonlinear relationships
• Neither linear nor nonlinear relationships

If Spearman's rho is close to 0, it indicates:

• A strong positive correlation
• A strong negative correlation
• No correlation
• A weak positive correlation

The interpretation of Spearman's rho depends on:

• Sample size
• Variability of the data
• Skewness of the data
• Context and domain knowledge

Spearman's rho can be calculated using:

• Pearson correlation coefficient
• Chi-square test
• Mann-Whitney U test
• Kendall's tau

In Spearman's rho, tied ranks are handled by:

• Assigning random ranks
• Using average ranks
• Ignoring tied ranks
• Subtracting 1 from each tied rank

Spearman's rho is a nonparametric measure, which means:

• It assumes a normal distribution of the data
• It makes no assumptions about the distribution of the data
• It assumes equal variances in the groups
• It assumes linearity between the variables

Spearman's rho can be used to compare rankings between:

• Two groups
• Three or more groups
• Independent variables
• Categorical variables

Spearman's rho is named after:

• A famous statistician
• A Greek mathematician
• A French scientist
• An Italian mathematician