A researcher collected self-report data on life satisfaction, social support, and depression. She found the following...
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A researcher collected self-report data on life satisfaction, social support, and depression. She found the following correlations: (1) life satisfaction and depression, (8) = -.77, p < .01; (2) social support and depression, (8)=-43, p > .05; (3) social support and life satisfaction. r(8) = .65, p < .05. When she regressed depression on life satisfaction, she found the following unstandardized simple linear regression equation: =-33x+4.124. The standardized linear regression equation for life satisfaction and depression is = -.77xz. A. Simple Linear Regression: 1. How can we interpret b, the unstandardized coefficient (unstandardized slope)? Use the variable names to describe how the criterion changes for every 1 unit change in the predictor. The sentence should roughly follow the following format: "For every one unit change in [predictor], [criterion] will change by b units. 2. How can we interpret , the standardized coefficient (standardized slope)? Use the variable names to describe how the criterion changes for every 1 standard deviation change in the predictor. The sentence should roughly follow the following format: "For every one standard deviation change in [predictor] [criterion] will change by standard deviations." 3. Given the simple linear regression equation, = -.33x+4.124, if you know someone has a life satisfaction score of 4, what would be their predicted depression score? 4. The actual depression score for the participant with a life satisfaction score of 4 was 2.62. What is the residual (the difference between the actual value of Y and the predicted value of Y)? B. Multiple Regression: The researcher now wants to look at the relationships among all three variables. In this example, depression was regressed on life satisfaction and social support. Fill in the variable labels and correlations in the diagram below. corr. Predictor 1: corr. fill in predictor here Predictor 2: fill in predictor here corr. Criterion: fill in criterion here 1. R for this model was .611. Based on the ANOVA table below, is this significant? Explain how you know. ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression .449 2 .225 5.494 .037b Residual .286 7 .041 Total .736 9 a. Dependent Variable: Depression b. Predictors: (Constant), Social Support, Life Satisfaction 2. How would you interpret this R value of .611 in terms of the variables in the model? (Hint: convert R into a percentage - this the amount of variability in the criterion explained by the predictors). 4. Based on the coefficients table below, which predictors explained a significant amount of variance in the criterion? Coefficients Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 4.080 .368 11.083 .000 Life Satisfaction -.370 .133 -.866 -2.776 .027 Social Support .031 .069 .140 .447 .668 a. Dependent Variable: Depression 5. How can we interpret , the standardized coefficient (standardized slope), for Life Satisfaction? Use the variable names to describe how the criterion changes for every 1 standard deviation change in life satisfaction, controlling for the other predictor. The sentence should roughly follow one of the following formats: "Controlling for [predictor 2], for every one standard deviation change in [predictor ], [criterion] will change by standard deviations." OR "If [predictor 2] is kept constant, for every one standard deviation change in [predictor ], [criterion] will change by standard deviations." A researcher collected self-report data on life satisfaction, social support, and depression. She found the following correlations: (1) life satisfaction and depression, (8) = -.77, p < .01; (2) social support and depression, (8)=-43, p > .05; (3) social support and life satisfaction. r(8) = .65, p < .05. When she regressed depression on life satisfaction, she found the following unstandardized simple linear regression equation: =-33x+4.124. The standardized linear regression equation for life satisfaction and depression is = -.77xz. A. Simple Linear Regression: 1. How can we interpret b, the unstandardized coefficient (unstandardized slope)? Use the variable names to describe how the criterion changes for every 1 unit change in the predictor. The sentence should roughly follow the following format: "For every one unit change in [predictor], [criterion] will change by b units. 2. How can we interpret , the standardized coefficient (standardized slope)? Use the variable names to describe how the criterion changes for every 1 standard deviation change in the predictor. The sentence should roughly follow the following format: "For every one standard deviation change in [predictor] [criterion] will change by standard deviations." 3. Given the simple linear regression equation, = -.33x+4.124, if you know someone has a life satisfaction score of 4, what would be their predicted depression score? 4. The actual depression score for the participant with a life satisfaction score of 4 was 2.62. What is the residual (the difference between the actual value of Y and the predicted value of Y)? B. Multiple Regression: The researcher now wants to look at the relationships among all three variables. In this example, depression was regressed on life satisfaction and social support. Fill in the variable labels and correlations in the diagram below. corr. Predictor 1: corr. fill in predictor here Predictor 2: fill in predictor here corr. Criterion: fill in criterion here 1. R for this model was .611. Based on the ANOVA table below, is this significant? Explain how you know. ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression .449 2 .225 5.494 .037b Residual .286 7 .041 Total .736 9 a. Dependent Variable: Depression b. Predictors: (Constant), Social Support, Life Satisfaction 2. How would you interpret this R value of .611 in terms of the variables in the model? (Hint: convert R into a percentage - this the amount of variability in the criterion explained by the predictors). 4. Based on the coefficients table below, which predictors explained a significant amount of variance in the criterion? Coefficients Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 4.080 .368 11.083 .000 Life Satisfaction -.370 .133 -.866 -2.776 .027 Social Support .031 .069 .140 .447 .668 a. Dependent Variable: Depression 5. How can we interpret , the standardized coefficient (standardized slope), for Life Satisfaction? Use the variable names to describe how the criterion changes for every 1 standard deviation change in life satisfaction, controlling for the other predictor. The sentence should roughly follow one of the following formats: "Controlling for [predictor 2], for every one standard deviation change in [predictor ], [criterion] will change by standard deviations." OR "If [predictor 2] is kept constant, for every one standard deviation change in [predictor ], [criterion] will change by standard deviations."
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Accounting Principles Part 1
ISBN: 978-1118306789
6th Canadian edition
Authors: Jerry J. Weygandt, Donald E. Kieso, Paul D. Kimmel, Barbara Trenholm, Valerie Kinnear, Joan E. Barlow
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