I am not sure if this is the appropriate place to ask this question. If not, please let me know and I will delete it.
I have estimated, for example, the following model to capture increasing/decreasing marginal effect of x on y:
\[ y=\alpha + \beta_1x+ \beta_2x^2 +e \]
where \[ \beta_1 \] is statistically insignificant, but \[ \beta_2 \] is statistically significant.
My questions are as follows:
1. What's the implication of these statistical significance and insignificance in terms of interpreting the coefficients?
2. Can I still meaningfully calculate the turning point of $x$ by using the formula $x*=\frac{\beta_1}{2\beta_2}$?
Thanks.
I have estimated, for example, the following model to capture increasing/decreasing marginal effect of x on y:
\[ y=\alpha + \beta_1x+ \beta_2x^2 +e \]
where \[ \beta_1 \] is statistically insignificant, but \[ \beta_2 \] is statistically significant.
My questions are as follows:
1. What's the implication of these statistical significance and insignificance in terms of interpreting the coefficients?
2. Can I still meaningfully calculate the turning point of $x$ by using the formula $x*=\frac{\beta_1}{2\beta_2}$?
Thanks.
Comment