Regression analysis

Regression analysis

- Nguyễn Đăng Hải HUF04 の投稿

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The residuals appear to be approximately normally distributed, as indicated by the bell-shaped histogram and the close alignment of points along the diagonal line...

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The residuals appear to be approximately normally distributed, as indicated by the bell-shaped histogram and the close alignment of points along the diagonal line in the P–P plot. The scatterplot of standardized residuals versus predicted values shows a random dispersion of points without a clear pattern, suggesting that the assumptions of linearity and homoscedasticity are met. In addition, multicollinearity does not appear to be a concern, as all VIF values are low (VIF = 1.385) and tolerance values are above the acceptable threshold (Tolerance = .722). The overall model is statistically significant (F = 184.486, p < .001) and explains a substantial proportion of variance in perceived stress (R² = .466). Therefore, the regression model can be considered appropriate and reliable for interpretation.

Regression analysis

- Trần Huỳnh Gia Hân HUF04 の投稿
Your analysis of the significance and R SQUARE is clear; however, it might be worth double-checking the scatterplot for potential heteroscedasticity. If the points show a ...

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Your analysis of the significance and R SQUARE is clear; however, it might be worth double-checking the scatterplot for potential heteroscedasticity. If the points show a 'fan' or 'funnel' shape rather than random dispersion, the assumption of equal variance may be violated, which can lead to biased p-values. If you do find a pattern there, a possible remedy would be to apply a logarithmic or square root transformation to the dependent variable (perceived stress) to stabilize the variance. Additionally, checking for influential outliers using Cook’s Distance might help ensure that a few extreme cases aren't disproportionately driving that high F-statistic.

Regression analysis

- Hạnh HUF04 Võ Thị Bích の投稿
Your interpretation is clear and well-structured. You correctly explained the key regression assumptions, including normality, linearity, homoscedasticity, and ...

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Your interpretation is clear and well-structured. You correctly explained the key regression assumptions, including normality, linearity, homoscedasticity, and multicollinearity, using appropriate evidence from the plots and statistics. The use of specific values such as VIF, tolerance, R², and F-test strengthens your explanation and shows a good understanding of the results.One suggestion for improvement is to briefly mention the direction and significance of individual predictors (e.g., beta coefficients) to make the interpretation more complete. Additionally, you could simplify some sentences to improve readability.

Regression analysis

- Nguyễn Ngọc Quỳnh Như HUF04 の投稿
You did a great job explaining the results. Your writing is clear, and you used the charts and numbers perfectly to show that your data follows all the right rules for ...

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You did a great job explaining the results. Your writing is clear, and you used the charts and numbers perfectly to show that your data follows all the right rules for regression.

Regression analysis

- Nguyễn Trúc Thanh Vy HUF04 の投稿
Your interpretation is well-structured and accurately covers key assumptions such as normality, linearity, homoscedasticity, and multicollinearity. The use of statistical ...

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Your interpretation is well-structured and accurately covers key assumptions such as normality, linearity, homoscedasticity, and multicollinearity. The use of statistical evidence (e.g., VIF, tolerance, F, R²) is clear and appropriate.You could also strengthen your analysis by briefly mentioning the significance and direction of the predictors. Overall, this is a strong and reliable interpretation with only minor adjustments needed.