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1.Which of the following defines a linear relationship between variables?

  • As one variable rises and falls, the other does likewise.
  • The relationship is positive or negative, but not both.
  • The relationship between variables depends upon the range of the data.
  • As one variable increases or decreases, the other tends to level out.

2. Theoretically, the standard error of the estimate is ___________.

  • the mean of all possible error scores
  • the sum of all possible error scores
  • the standard deviation of all error scores
  • the sum of all error score variances

3. As sample size grows, the magnitude of the correlation required for significance increases.

  • True
  • False

4. What does r2 accomplish that r does not?

  • It quantifies the x/y relationship.
  • It normalizes the data involved in the relationship.
  • It provides an answer to the statistical hypotheses.
  • It makes the increments between tenths equal.

5. Which of the following defines a bivariate correlation?

  • A correlation value that can vary
  • Correlating groups of variables
  • A correlation between two variables
  • Two variables correlated with a third

6. A dichotomous variable is ____________.

  • a variable with only two levels
  • an ordinal scale variable
  • a variable that can be positive or negative
  • a variable which is difficult to measure

7. How are degrees of freedom related to critical values of rxy?

  • Critical values increase in direct proportion to degrees of freedom.
  • Critical values decline as degrees of freedom increase.
  • Critical values are such that significance is difficult to establish with large samples.
  • Critical values allow degrees of freedom to be ignored in large samples.

8. The size of the standard error of the estimate is increased by the following except:

  • Weaker x/y correlations
  • Larger sample sizes
  • More variability in the criterion variable
  • Random sampling

9. The Spearman's rho requires that the both sets of data be ranked.

  • True
  • False

10. Error in regression is a function of the correlation between x and y.

  • True
  • False

Science, Academics

  • Category:- Science
  • Reference No.:- M9781080

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