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Statistics for the Behavioral Sciences Study Set 2
Exam 13: Pearson Correlation and Regression: Inferential Aspects
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Question 101
Multiple Choice
In a regression equation,the variable being predicted (or _____) is known as a(n) _______ variable.
Question 102
Multiple Choice
A relationship might exist in a sample even though it does not exist in the population,because of:
Question 103
True/False
The mean of a sampling distribution of r is approximately equal to r.
Question 104
Multiple Choice
The strength of the relationship between two variables in a correlational analysis can be represented by:
Question 105
Multiple Choice
Which of the following is NOT one of the assumptions of the Pearson correlation?
Question 106
True/False
Perfect linear relationships between Y and X are relatively common in the behavioral sciences.
Question 107
True/False
Because the calculation of t depends only on the value of r,it is impossible to determine values of r that will lead to a rejection of the null hypothesis that ñ = 0,given a certain sample size.
Question 108
Multiple Choice
In the context of regression,the variable being predicted,Y,is formally known as the dependent or _____ variable;the variable from which predictions are made,X ,is formally known as the independent or _____ variable.
Question 109
True/False
The slope indicates the number of units Y is predicted to change given a one unit change in X.
Question 110
True/False
The variable being predicted,Y,is formally known as the dependent or criterion variable.
Question 111
Multiple Choice
The value of r
2
represents the proportion of variability _____.
Question 112
True/False
The nature of the relationship between two correlated variables is determined through examination of the sign of the correlation coefficient observed in the sample.
Question 113
Essay
How is the nature of the relationship between two variables evaluated in a correlational analysis?
Question 114
True/False
In the context of regression,prediction merely refers to the fact that we are making inferences about one variable from a second variable and does not imply that the latter variable causes the former.