PY 211 Exam 4 Study Guide

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35 Terms

1
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Why do we convert raw scores to Z scores when figuring a correlation coefficient?

It gives us a standardized value in which low scores are negative numbers, & high scores are positive numbers

2
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How would you describe the following correlation coefficients: -.87, 0, .90, .06?

-.87: strong negative linear correlation
0: no linear correlation
-.90: strong positive linear correlation
.06: weak positive linear correlation

3
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What does the null hypothesis state when testing the significance of a correlation coefficient?

The true correlation in the population is zero.

4
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What is the formula for degrees of freedom when conducting a t test for the correlation coefficient? If we had a study with 35 individuals, what would be the degrees of freedom?

df= N- 2
df= 33

5
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If there is a strong linear correlation between health & income, what is the direction of causality?

can't say; health could cause income (greater / less earning potential); income could cause health; a third variable could be impacting both health & income

6
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What is an outlier?

a score that has an extreme value in relation to other scores in a distribution

7
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If a professor wants to predict test grades from the hours a student studied, what is the procedure called? What is the predictor variable? What is the criterion variable?

procedure: bivariate prediction
criterion variable: test scores
predictor variable: hours studied

8
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What is a regression coefficient?

(b) in the linear prediction equation: indicates how many units of change is predicted in the criterion variable for each unit of change in the predictor variable

9
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What is the regression constant?

(a) in the linear prediction equation; the Y intercept; predicted score on the criterion variable when the score on the predictor variable is 0; the number you always start with when calculating regression/ prediction line

10
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1. An I/O psychologist studying adjustment to the job of new employees found that employees' amount of education (in number of years) predicts ratings by job supervisors two months later. If:
Regression Constant = 0.5
Regression Coefficient = 0.4
Individual has 10 years of education
-What is the predictor variable?

years of education

11
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1. An I/O psychologist studying adjustment to the job of new employees found that employees' amount of education (in number of years) predicts ratings by job supervisors two months later. If:
Regression Constant = 0.5
Regression Coefficient = 0.4
Individual has 10 years of education
- What is the criterion variable?

job's supervisor's rating

12
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1. An I/O psychologist studying adjustment to the job of new employees found that employees' amount of education (in number of years) predicts ratings by job supervisors two months later. If:
Regression Constant = 0.5
Regression Coefficient = 0.4
Individual has 10 years of education
- What is the linear prediction rule for this example?

y = 0.5 + (0.4) (10)

13
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1. An I/O psychologist studying adjustment to the job of new employees found that employees' amount of education (in number of years) predicts ratings by job supervisors two months later. If:
Regression Constant = 0.5
Regression Coefficient = 0.4
Individual has 10 years of education
-What is the predicted job rating for the employee in this example?

y = 4.5

14
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What is a scattergram and how do you interpret it?

-a graph that shows the relation of 2 variables through dots representing data points
-no correlation: dots are randomly selected with no clear line/ relationship
-curvilinear: dots arranged in a curve going up or down
-positive correlation: dots in line going up from left to right
-negative correlation: dots in line going down from left to right

15
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How do you describe the pattern of the data in a positive correlation? In a negative correlation? In a curvilinear correlation?

-positive: high scores go with high scores, low with low
-negative: high scores go with low scores, low with high
-curvilinear: data points appear arched

16
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What are the possible results of multiplying two Z scores when observing the correlation between variables?

-high scores (+) x high scores (+) = positive z scores
-low scores (-) x low scores (-) = positive z scores
-high scores (+) x low scores (-) = negative z scores

17
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How is the proportionate reduction in error figured in order to compare correlations?

by squaring each correlation coefficient

18
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Describe a correlation matrix.

In a correlation matrix, variables are listed on top and left side, and the relationship between the variables are listed in each cell

19
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What is a regression line?

Shows the relationship between predictor & criterion variable

20
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How do you calculate error in regression?

score on the criterion variable - predicted score

21
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What is the null hypothesis when conducting a bivariate linear prediction?

-null: predicted amount of change of the criterion variable is 0 when predictor variable increases by one standard deviation (B)(standardized regression coefficient=0)

22
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What is the statistical procedure for predicting a criterion variable from more than one predictor variable?

multiple regression

23
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Explain the chi-square test:
1. What kind of variables do you have?

Nominal

24
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Explain a chi-square test:
2. What is the main idea of the test?

examine if pattern is observed frequencies fits the expected pattern of frequencies

25
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Explain a chi-square test:
3. What calculation is necessary for the statistic?

take difference between observed & expected frequencies; square & sum the differences divide by expected frequency of each cell

26
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Explain the chi-square test:
4. What type of table is used?

contingency table; distribution of 2 nominal variables listed so that you have frequencies for each cell & combination / totals

27
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Explain a chi-square test:
5. How would you describe the distribution?

chi-square distributions of 2 nominal variables listed so that you have frequencies for each cell & combinations/ totals

28
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Explain a chi-square test:
6. What is the primary debate regarding chi-square tests?

small expected frequencies

29
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What is the difference between the chi-square test for goodness of fit and the chi-square test for independence?

-goodness of fit is limited to 1 variable, and independence test is not
-the independence test looks for a relationship between multiple nominal variables
-independence means no relationship between the variables

30
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For the chi-square test for goodness of fit, what is the df? What is the null hypothesis?

df= number of categories minus 1
-the null for goodness of fit is that proportion of people over categories breaks down the same for the 2 populations

31
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What is the minimum number of predicted points on a graph required for drawing a regression line?

A minimum of 2 data points is required to draw a regression line

32
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Why are regression errors squared?

We square regression errors because summing the positive and negative errors will cancel out

33
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What is the name for adding these squares together? (error in regression)

The sum of squared errors

34
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How do the correlation coefficient & regression coefficient relate in hypothesis test for a linear prediction rule?

Linear prediction, if the correlation coefficient is significant, the regression coefficient will be significant

35
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What is the formula if there are 2 predictor variables?

-y = a + b1 (x1) + b2 (x2)