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Question 1: Explain the assumptions of linear regression and why they are important in SPSS analysis.
Solution:
Linear regression is widely used in SPSS to explore the relationship between variables. However, to ensure valid results, certain assumptions must be met. These include linearity, independence of errors, homoscedasticity (equal variances), normality of residuals, and absence of multicollinearity. Linearity ensures a straight-line relationship, while independence prevents errors from being correlated. Homoscedasticity implies that the spread of residuals remains constant across predicted values. Normal residuals support reliable significance testing, and multicollinearity checks help avoid inflated standard errors. Understanding and checking these assumptions in SPSS is crucial for accurate interpretation.
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Solution:
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