Based on the regression output below, which of the following is the most appropriate interpretation? In this regression analysis, the dependent variable is the satisfaction with the new gaming app. The independent variables include design score and ease-of-use.

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BlackTom AI · Accepted Answer

In this regression output, the dependent variable is Satisfaction and the key predictor shown is Ease_of_Use, along with App_Design_Score.
First, observe the Coefficients table: the unstandardized coefficient for Ease_of_Use is 1.987 with a standard error of .367, and the t-statistic is 5.412 with a p-value (Sig.) less than .001. This indicates a statistically significant positive relationship between Ease_of_Use and Satisfaction: as Ease_of_Use increases by one unit, Satisfaction increases by about 1.99 units, holding App_Design_Score constant.
Next, note the Significance column for Ease_of_Use (<.001). This reinforces that the effect is highly unlikely to be due to random chance in the sample data.
Consider the intercept: the constant is -71.551 with a t-statistic highly significant as well, but interpretation of the intercept alone is less meaningful for practical application because it represents the expected Satisfaction when both predictors are zero, which may not be a meaningful or observed value in the real world.
Look at the model summary: Adjusted R Squared is .052, meaning the model explains about 5.2% of the variance in Satisfaction after adjusting for the number of predictors; while the effect of Ease_of_Use is statistically significant, the overall model fit is modest, suggesting other factors influence Satisfaction not captured here.
Now, evaluating the answer choices: if there were an option stating that Ease_of_Use significantly improves Satisfaction, this would align with the statistically significant positive coefficient and the very low p-value; that interpretation is consistent with the data.
If an option claimed that App_Design_Score has a large or significant effect, that would be contradicted by the coefficients shown (App_Design_Score has a small negative coefficient and a non-significant p-value of .631), making that interpretation incorrect.
If an option claimed no relationship or a negative relationship with Ease_of_Use, those would be contradicted by the positive coefficient and strong significance.
In sum, the statistically supported interpretation is that increases in Ease_of_Use are associated with higher Satisfaction, and this effect is statistically significant, while recognizing the overall model explains a limited portion of the variance (low Adjusted R^2) and other factors may be at play.

Based on the regression output below, which of the following is the most appropriate interpretation? In this regression analysis, the dependent variable is the satisfaction with the new gaming app. The independent variables include design score and ease-of-use.

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