Regression analysis

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Hi

I have conducted 2 Two separate Multiple Linear Regression Model test the relationship between the outcome variables, which was self-esteem and depression, with the predictor variables being; Instagram Intensity, social comparison orientation (SCO).

My results are as followed:

The first multiple linear regression was carried out to investigate whether Instagram Intensity score, and SCO could significantly predict participants self-esteem scores. It yielded a significant model that accounted for a larger proportion of the variance in the self-esteem score F (3,49) = 9.78, MSE = 113.78, p < .05, R² = .38. The results of the regression indicated that the model explained 39.8% of the variance. Whilst SCO contributed significantly to the model (b = - 2.195, p < .05) and was reliable, Instagram usage did not (b = - 0.09, p > .05). Here, as SCO increases self-esteem decreases. The interaction between SCO and Instagram usage was not significant.

The second multiple regression was carried out to investigate whether Instagram usage score, and SCO could significantly predict participants depression scores. It generated a significant model that accounted for a larger proportion of the variance in the depression scores F (3,49) = 6.04, MSE = 1228.3, p < .05, R² = 27%. The results of the regression indicated that the model explained 27% of the variance. Whilst SCO contributed significantly to the model (b = 1.25, p < .05) and was reliable, Instagram usage did not (b = .010, p > .05). Here, as social comparison increases, depression increases. The interaction between SCO and Instagram usage was significant (p <.05). This means that the regression model is statistically significant and a good predictor of the depression scores.

Essentially what are these both showing?
Any help would be much appreciated.
 
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