What to know
Identify the rule, formula, or decision criterion before reading the answer choices. CFA Level I distractors often use the right vocabulary with the wrong condition.
Quantitative Methods
Parametric and Non-Parametric Tests of Independence practice questions
Parametric and Non-Parametric Tests of Independence is part of CFA Level I Quantitative Methods. Quantitative Methods questions emphasize time value of money, probability, sampling, hypothesis testing, regression, and return statistics. Use this page to review the controlling ideas, then work through 9 questions with answer explanations and common traps.
Review the worked explanations before moving into adaptive practice. The app version can mix this topic with due reviews and weak related concepts.
Practice this topicHow to practice
Work each item under time pressure, then compare your reasoning with the step-by-step explanation and key takeaway.
Review signal
Missed questions should become scheduled reviews when the error comes from a concept gap, formula setup, or answer-choice trap.
Quantitative Methods
Parametric and Non-Parametric Tests of Independence
A hypothesis test of whether the population correlation coefficient equals zero has the null hypothesis:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
For a contingency table with 3 rows and 4 columns, the degrees of freedom for a chi-square independence test are:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
In a chi-square test of independence, expected frequency for a cell equals:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
A sample correlation is 0.40 based on 25 paired observations. The test statistic for H0: rho = 0 is closest to:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
A chi-square test of independence rejects the null hypothesis. The most accurate interpretation is that the two classifications are:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
For ordinal credit ratings and ordinal analyst recommendation categories, a nonparametric independence test is most likely appropriate because the data are:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
A correlation of 0.75 between factor exposure and fund return is statistically significant. The most accurate conclusion is that:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
A 2 x 2 contingency table has observed counts [30, 20; 20, 30]. Row totals and column totals are each 50, and the grand total is 100. The chi-square statistic is closest to:
View sampleQuantitative Methods
Parametric and Non-Parametric Tests of Independence
A parametric correlation test is least appropriate when:
View sample