I am trying to figure out if there is a way that we can perform some statistical test to check the interaction between two independent continuous variables and a dependent variable in R.

I have three variables course completion rate, interaction coefficient of participants, and sentiment scores. I want to check if there exists any relationship:

If the course completion rate depends on the interaction coefficient and sentiment scores.

I have checked for the normality of the variables, and they follow a normal distribution using a qq-plot.

I have done something like using anova:

model <- aov(completion_rate ~ eta * synergy_scores,
             data = eta_scores)

summary(model)

Is it the right way to compare two continuous variable like this?

I am trying to figure out if there is a way that we can perform some statistical test to check the interaction between two independent continuous variables and a dependent variable in R.

I have three variables course completion rate, interaction coefficient of participants, and sentiment scores. I want to check if there exists any relationship:

If the course completion rate depends on the interaction coefficient and sentiment scores.

I have checked for the normality of the variables, and they follow a normal distribution using a qq-plot.

I have done something like using anova:

model <- aov(completion_rate ~ eta * synergy_scores,
             data = eta_scores)

summary(model)

I get an output like:

                   Df Sum Sq Mean Sq F value Pr(>F)
eta                 1  0.064 0.06394   1.413  0.238
synergy_scores      1  0.065 0.06481   1.432  0.235
eta:synergy_scores  1  0.005 0.00512   0.113  0.737
Residuals          88  3.982 0.04525      

Is it the right way to compare two continuous variable like this?

I am trying to figure out if there is a way that we can perform some statistical test to check the interaction between two independent continuous variables and a dependent variable in R.

I have three variables course completion rate, interaction coefficient of participants, and sentiment scores. I want to check if there exists any relationship:

If the course completion rate depends on the interaction coefficient and sentiment scores.

I have checked for the normality of the variables, and they follow a normal distribution using a qq-plot.

I am trying to figure out if there is a way that we can perform some statistical test to check the interaction between two independent continuous variables and a dependent variable in R.

I have three variables course completion rate, interaction coefficient of participants, and sentiment scores. I want to check if there exists any relationship:

If the course completion rate depends on the interaction coefficient and sentiment scores.

I have checked for the normality of the variables, and they follow a normal distribution using a qq-plot.

I have done something like using anova:

model <- aov(completion_rate ~ eta * synergy_scores,
             data = eta_scores)

summary(model)

Is it the right way to compare two continuous variable like this?