Question

Table 1 provides a summary of the regression analysis for the variable predicting happiness. 

What can the researcher say about the variable sense of humor?

1. The variable sense of humor does not contribute to the model for predicting happiness.
2. The variable sense of humor does contribute to the model for predicting happiness.
3. The variable sense of humor is not considered in the model for predicting happiness.

2 points  QUESTION 2

1. A Multiple Linear Regression analysis generates an equation to describe the statistical relationship between one or more predictor variables and the response variable. The t-statistic and its corresponding p-value for each term tests the null hypothesis that the coefficient is equal to zero.
2.  True
3.  False

2 points  QUESTION 3

1. The “line of best fit” that represents a straight line drawn through a scatterplot is called a:
3. Covariance line
4. Regression line
5. Curvilinear line
6. Coefficient line

2 points  QUESTION 4

1. A Multiple Linear Regression was conducted to evaluate if (sleep, diet, exercise, age, gender) can predict the criterion variable (GPA). The results of the regression model were F(5, 94) = 5.18, p < .05. The predictor variables had the corresponding p-values:
2. Sleep (p = .01)
3. Diet (p = .03)
4. Exercise (p = .08)
5. Age (p = .14)
6. Gender (p = .45)
7. Based on this model, what are the best predictors of GPA?
8. Exercise, Age, and Gender
9. Sleep and Diet
10. Exercise, Sleep, and Diet

2 points  QUESTION 5

1. To evaluate whether average life satisfaction self reported by residents of 119 nations was predictable from each nations’ GNP per capita, a bivariate linear regression was performed. Because of the small N (only 119 countries), the distributions of scores on GNP and life satisfaction did not correspond closely to an ideal normal distribution. The results of the overall regression equation were, F(1, 117) = 4.04, p = .067. The equation to predict Life Satisfaction from GNP in raw score units was: Sat’ = 6.59 + 0.00 * GNP (the b slope coefficient was zero to at least three decimal places). The standardized equation to predict z scores on life satisfaction from z scores on GNP was z_y’ = .44 * z_x. Based on these results, what can the researcher conclude?
2. The prediction model was not statistically significant at the conventional a = .05 level, each nations’ GNP per capita can not predicts average life satisfaction
3. The prediction model was statistically significant at the conventional a = .05 level, each nations’ GNP per capita predicts average life satisfaction
4. The prediction model was not statistically significant at the conventional a = .05 level, each nations’ GNP per capita predicts average life satisfaction
5. The prediction model was statistically significant at the conventional a = .05 level, each nations’ GNP per capita can not predicts average life satisfaction

2 points  QUESTION 6

1. A Bivariate Regression was conducted to evaluate the predictive relationship between total years of schooling and annual income. The results of the regression model were F(1,88) = 4.1, p < .05. What can be concluded about these results?
2. total years of schooling is a predictor of annual income.
3. total years of schooling is not predictor of annual income.

2 points  QUESTION 7

1. In bivariate regression, the difference between the obtained value and the predicted value of Y, is:
3. Intercept (b₀)
4. Residual
5. Slope (b₁)

2 points  QUESTION 8

1. In the equation, Y’ = b₀ + b₁X, b₀ represents the:
2. correlation between X and Y.
3. slope coefficient of the regression line.
4. intercept of the regression line with the Y axis.
5. predicted value of Y from knowing X.

2 points  QUESTION 9

1. In bivariate regression, the value of Y when X equals 0 is:
3. Intercept (b₀)
4. Residual
5. Slope (b₁)

2 points  QUESTION 10

1.  r² = .547 be interpreted as  54.7% of the variance in the outcome or criterion is explained by the predictor.
2. True
3. False

2 points  QUESTION 11

1. What assumption is not required for a bivariate regression to be a valid description of the relationship between X and Y?
2. Normality
3. Curvilinearity
4. Independent observations
5. Linearity

2 points  QUESTION 12

1. Given Y’ = b₀ + b₁X.
2. If b₀ = 0 and b₁ = 5 and X = 1, Y will equal:
3. 2.5
4. 5
5. 10
6. 20

2 points  QUESTION 13

1. In a correlation analysis, we examine scatterplots and “imagine” a line running through the datapoints that characterizes the general linear pattern of the data. We add a number, the Pearson correlation, which summarized how tightly clustered the points would be around that imaginary line. The process of placing a line onto the scatterplots is called ________.
2. Correlation coiefficient
3. Regression
4. Beta
5. F ratio

2 points  QUESTION 14

1. In a bivariate regression, the variable from which a prediction is made is called the predictor variable, whereas the variable to be predicted or (the outcome) is called the:
3. predictor variable.
4. criterion variable.
5. coefficient variable.
6. covariate variable.

2 points  QUESTION 15

 

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