Hypothesis 0 against Hypothesis 1.

6 steps when it comes to t-testing / f-testing in Econometrics.

1) Compare hypotheses.
2) Calculate T/F-Stats
3) Calculate T/F-Crit
4) Decision rule - Reject H0 (null hypothesis) when T-stats > T-crit (or the other way, if H1 is a negative integer.)
5) Conclude if H0 should be rejected at a significance level.
6) Conclude with econometrics/economics sense.

In my case:

1) H0: B = Study against H1: B =/= Study. (Therefore, 2-tail test)
2) Calculate T-Stats.
3) Calculate T-Stats
4) Decision rule - Reject H0 when T-stats > T-crit.
** At this point, I can assure you that the my T-stats calculation falls in the rejection region in the drafted graph.
5) Since T-stats > T-crit, we can reject H0 at a 10% level of significance.
6) We can conclude that studies are not required when approaching certain subjects.

Why? Simple.

For the tests I've studied in Management, Tests 1, 2 and 3.. I had scores of 9/10, 7/10, 7/10 respectively.

When I slack off and lose confidence in the topic I didn't know of..

I scored a 10.











A wise nigga one said, "DAMN STRAIGHT, NIGGA!"











But a wiser nigga said with a confident voice...

"..17 more days." :)