Correlation and Regression flashcards

15 practice cards drawn from the Correlation and Regression lessons. Tap a card to turn it over. Every answer is checked against the lesson it came from.

Read the Correlation and Regression lessons in full →

Which value of r fits these dots best?

0.05

from “Pearson’s Correlation Coefficient”

With n = 20 the critical value is 0.444, and r comes to 0.564. What follows?

reject H₀: the correlation is real

from “Critical Values of r”

With n = 10 the critical value is 0.632, and r comes to 0.512. What follows?

do not reject H₀: it could be chance

from “Critical Values of r”

Which total does the regression line of y on x make smallest?

the squared vertical gaps

from “The Least-Squares Regression Line”

Cost = 8n + 35 was fitted to n items. What is the 35?

the cost before any items are made

from “The Least-Squares Regression Line”

The fitted line is y = 0.5x + 1.5. Predict y when x is 3.

3

from “Predicting from a Regression Line”

The fitted line is y = 0.5x + 3. Predict y when x is 4.

5

from “Predicting from a Regression Line”

Two values tie for the places 2 and 3. What rank does each take?

2.5

from “Spearman’s Rank Correlation”

Ranked smallest first, what rank does the marked value get?

3

from “Spearman’s Rank Correlation”

Where do the line of y on x and the line of x on y always meet?

at the mean of x and the mean of y

from “The Regression Line of x on y”

You know a score and want the hours behind it. Which line?

x on y

from “The Regression Line of x on y”

The squared gaps to the mean total 25 and the squared gaps to the line total 4. What is R squared?

0.84

from “The Coefficient of Determination”

A model reports R² = 0.81. What does that say?

81 percent of the variation in y is accounted for

from “The Coefficient of Determination”

Which quantity does least squares make smallest when it fits a curve?

the total of the squared vertical gaps

from “Non-Linear Regression”

Counts that double every two weeks are fitted best by which family?

exponential

from “Non-Linear Regression”

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