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0.1123 is a large p-value, so this means the coefficient 0.0036 may not be correct and it might as well be equal to zero. This is what the null hypothesis was saying, that H0: b1 = 0, so you go with the null, i.e. you can’t reject it…tau281290 wrote:
So which numbers do I use to test the hypothesis? For dependent variable is it 0.0036 < 0.1123? So reject? Not reject?
1.591 is the t-value and it doesn’t say much on its own. You need to compare this to the critical t value, which is not given.tau281290 wrote:
Oh so significance of t is also known as p- value? That might be he confusion I was having.
So for t test it is 0.0036< 1.591 so don’t reject.
So for the next one it is -0.432>-5.846 so reject
am I right?
Yes. Defective assemblies per hour has a large p-value, which means its t-stat (1.591) is insignificant.tau281290 wrote:
So that Means we have to just use p value to answer this question?
This is a common misunderstanding of a p-value; it is not the probability that you are wrong. It does not tell you the probability that the null is true given the data and similarly, 1-pvalue does not tell you the probability that the alternative is true (or null is false). The p-value is the probability of observing a test statistic at least as extreme as the current one, assuming that the null is true. This is quite different from saying that the p-value is the probability that you are wrong. The p-value by itself can not answer questions regarding probabilities surrounding hypotheses.krokodilizm wrote:
There is yet another way to judge coefficients and that is the p-values. If a coefficient has a very small p value (close to zero) then you can again claim your findings are significant (p is a probability of being wrong, so a small probability, usually up to 0.05 as a rule of thumb, means small chance of being wrong). If your p value is greater than 0.05 you may start doubting your regression can be trusted. Whenever you doubt your findings, you can’t reject the null hypothesis.
Again, see my comment above. A p-value of 0.1123 indicates that if the null is true, then there is an 11.23% chance of observing results at least as extreme as the current ones. It does not tell you the chance that “our model sucks” or that we are making a mistake.krokodilizm wrote:
0.1123 means there is 11.36% chance our model sucks and this probability alone is enough to doubt our findings.
This is another thing to be wary of when looking at p-values. A small p-value doesn’t indicate how “good” a finding is– it only indicates whether that result is statistically significant at the pre-specified level of alpha. A very small p-value can be attached to a result that has little practical value (i.e. small p-value, “bad” result).krokodilizm wrote:Note the other two p-values are 0 (or very small) which means they are good findings.
No problem… Real statisticians (or even people who have a decent background) can use much more than straight lines– they try to actually model what’s going on in a process. The CFAI books are very, very limited in what they cover. Linearity in OLS means only in the parameters (i.e. the beta estimates), but you could introduce nonlinearity by adding x-squared, ln(x), 1/x, and many others!krokodilizm wrote:
Thanks for clarifications. Just like statisticians try to simplify everything and cram everything into straight line (whereas the relationship may be far from a straight line), I too try to simplify the thought process.
Anarchist.tickersu wrote:… you could introduce nonlinearity by adding x-squared, ln(x), 1/x, and many others!
LOL. I remember during undergrad econometrics class I asked the professor why force the data into a straight line, why not “let the findings speak for themselves” he said shut up and sit down you fool.S2000magician wrote:
Anarchist.tickersu wrote:… you could introduce nonlinearity by adding x-squared, ln(x), 1/x, and many others!