Biostats Module 3
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Questions and Answers

What does a correlation coefficient of -1.00 indicate?

  • Moderate positive correlation
  • No correlation
  • Perfect negative correlation (correct)
  • Perfect positive correlation

    • Which test does not rely on the normality assumption?

  • Spearman Rank Correlation
  • Pearson Correlation Coefficient
  • Simple Linear Regression
  • Nonparametric tests (correct)

    • What does the Spearman Rank Correlation assess?

  • Direction of relationship
  • Strength and direction of a relationship
  • Normality of data
  • Independence of variables (correct)

    • In regression, what is the coefficient of determination (r^2) used for?

    <p>Explaining the percentage of variance explained by the independent variable</p> Signup and view all the answers

    What is a core assumption for Simple Linear Regression?

    <p>Homogeneity of variances</p> Signup and view all the answers

    When two variables move in opposite directions, this is known as:

    <p>Perfect negative correlation</p> Signup and view all the answers

    Which statement about nonparametric tests is true?

    <p>They are less powerful than parametric tests</p> Signup and view all the answers

    What type of data is suitable for the Mann-Whitney U test?

    <p>Ordinal</p> Signup and view all the answers

    In what situation would you use the Wilcoxon Signed-Rank Test?

    <p>Evaluating ranks for paired measurements</p> Signup and view all the answers

    When conducting the Kruskal-Wallis test, how many groups are being compared?

    <p>3 groups</p> Signup and view all the answers

    What is a key assumption for the Chi-Square test?

    <p>Independent and categorical data</p> Signup and view all the answers

    Under what condition should Fisher's Exact test be used?

    <p>When more than 20% of cells have expected counts less than 5 in the Chi-Square test</p> Signup and view all the answers

    Which test is suitable for comparing medians between two independent groups?

    <p>Mann-Whitney U test</p> Signup and view all the answers

    What assumption is common between the Kruskal-Wallis and Mann-Whitney U tests?

    <p>Random samples</p> Signup and view all the answers

    Which correlation coefficient indicates the weakest relationship between two variables?

    <p>0.25</p> Signup and view all the answers

    What is the primary difference between Pearson and Spearman correlation coefficients?

    <p>Pearson requires normality and interval/ratio data, while Spearman does not.</p> Signup and view all the answers

    Which of the following indicates the percentage of variance in the dependent variable that can be explained by the independent variable?

    <p>Coefficient of determination (r^2)</p> Signup and view all the answers

    What is a disadvantage of nonparametric tests compared to parametric tests?

    <p>Nonparametric tests are less powerful.</p> Signup and view all the answers

    Which of the following best describes the difference between correlation and regression?

    <p>Correlation examines the relationship between variables, while regression uses the relationship for prediction.</p> Signup and view all the answers

    Under which condition would simple linear regression be appropriate for analyzing a dataset?

    <p>When variables are interval/ratio level data, independent, and linearly correlated.</p> Signup and view all the answers

    Which of the following is an assumption of both Pearson and Spearman correlation coefficients?

    <p>Independence</p> Signup and view all the answers

    What is a key difference between the Mann-Whitney U test and the Wilcoxon Signed-Rank Test?

    <p>The Wilcoxon Signed-Rank Test evaluates ranks for differences in measurements while the Mann-Whitney U test evaluates medians.</p> Signup and view all the answers

    What distinguishes Kruskal-Wallis test from Mann-Whitney U test?

    <p>Mann-Whitney U test evaluates differences between two groups while Kruskal-Wallis test compares three or more groups.</p> Signup and view all the answers

    Which assumption is unique to the Chi-Square test compared to other nonparametric tests?

    <p>Chi-Square needs expected counts to be greater than 1 and no more than 20% of cells less than 5.</p> Signup and view all the answers

    When would you use Fisher's Exact test instead of the Chi-Square test?

    <p>When expected counts are not met for Chi-Square assumptions.</p> Signup and view all the answers

    What distinguishes the Wilcoxon Signed-Rank Test from the Mann-Whitney U test in terms of data requirements?

    <p>Wilcoxon Signed-Rank Test requires ordinal/interval/ratio data while Mann-Whitney U does not have this requirement.</p> Signup and view all the answers

    What assumption differentiates the Kruskal-Wallis test from the Chi-Square test?

    <p>Kruskal-Wallis requires ordinal/interval/ratio level data, unlike Chi-Square.</p> Signup and view all the answers

    What makes Fisher's Exact test unique compared to other nonparametric tests?

    <p>Fisher's Exact test can be used with very small sample sizes where Chi-Square assumptions are violated.</p> Signup and view all the answers

    What distinguishes the Wilcoxon Signed-Rank Test from the Kruskal-Wallis test in terms of their application?

    <p>Wilcoxon Signed-Rank Test compares ranks within a group, while Kruskal-Wallis compares ranks between groups.</p> Signup and view all the answers

    Study Notes

    Correlation

    • Evaluates the relationship between variables and direction of relationship
    • Positively related: both move in the same direction
    • Negatively related: move in opposite directions
    • Correlation does not equal causation
    • Correlation Coefficient: describes the strength and direction of a relationship, ranges from -1.00 to +1.00
    • -1.00 = perfect negative correlation
    • +1.00 = perfect positive correlation

    Pearson Correlation Coefficient

    • Assumptions: independence, normality, variables are interval/ratio level data

    Spearman Rank Correlation

    • Assumptions: independence

    Regression

    • Uses the relationship between variables as a basis for prediction
    • Takes correlation a step further
    • Simple Linear Regression: examines two variables that are linearly correlated
    • Simple Linear Regression assumptions: normality, homogeneity of variances (Levene's test), and dependent variable is interval/ratio level data
    • Regression Line formula: Y = a + bX
    • Y = dependent variable
    • a and b = constants
    • X = independent variable
    • r = correlation coefficient
    • r2 = coefficient of determination (percentage of the variance in the dependent variable that can be explained by the independent variable)

    Nonparametric Tests

    • Do not rely on probability distribution (normality assumption)
    • Do not have homogeneity of variances
    • Can be used to examine nominal or ordinal level data
    • Less powerful than parametric tests

    Mann-Whitney U Test

    • Evaluates whether medians differ significantly between two groups
    • Assumptions: random samples, independence, and dependent variable is ordinal/interval/ratio level

    Wilcoxon Signed-Rank Test

    • Evaluates ranks for the difference between the measurements (use a before and after score and subtract to find rank)
    • Cannot be used with ordinal data
    • Assumptions: random samples and dependent variable is interval/ratio level

    Kruskal-Wallis Test

    • Evaluates whether medians differ significantly between 3+ groups
    • Assumptions: random samples, independence, and dependent variable is ordinal/interval/ratio level data

    Chi-Square Test

    • Evaluates expected frequencies and observed frequencies of categorical (ordinal and nominal) data
    • Assumptions: independence, expected counts are greater than 1 and no more than 20% of cells are less than 5

    Fisher's Exact Test

    • Used if Chi-Square assumptions are not met

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    Test your knowledge on correlation, correlation coefficient, and the evaluation of relationships between variables. Understand the concepts of positive and negative correlation, and learn that correlation does not imply causation.

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