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17) the definition of model c ensures that the coefficients of clr will be equal to b1, b2 etc. (for clr 2: 2*0.05=0.010 etc). And model c is only a specific version of the more general model d where y0 = 0 and y1=0.005. So, c is the correct answer
18) you can only use the partial f-test if one of your models is the reduced version of a complete model. This the case for every option except a)
19) basically its just plugging in the values for a partial f-test ((129.562-73.908)/(6-1))/(73.908/(12-6) = 0.9 which is between 0.75 and 1
k: number of coefficients in the complete model
g: number of coefficients in the reduced model
- what counts as a coefficient? The intercept (constant) + all explanatory variables on the right hand side of the regression model. So a model [y = b0 + b1*x + b2*x ] would have k = 3 coefficients. If you now wish to compare this model to a reduced model using a partial F-test, you could omit one of the predictor variables, say, b2, and the model reduces to [y = b0 + b1*x] with g = 2 coefficients. Hope this comment helps.
As you see from the question "L) iii)" our significance level is 10%. Here we are testing the variables "Break" and "BreakMarket_Interact", so we oberserve significance levels of 0.035 and 0.107. So for 0.035 we can safely reject the null as it is below our 10% however for the other one the p-value is 10.7% which just above 10% we cannot reject the null at the 10% significance level even if it is very close.
You are testing if the intercept is equal to 0 in other words if the intercept has any influence on the model. You can get all the data which she used from the SPSS output. The formula to calculate the value (point estimate-hypothesised value)/SE. The corresponding t value is higher than 1.653 which means we can reject Ho at alpha=0.05
i made a mistake here, 1986-1998 are 13 years instead of 12 :D and there are 24037 firm observations whcih means we have 24037/13=1849 firms in our data set, 4069/13= 313 of them adopted the SAP module.
This mistake does not affect any other task, just change these numbers :)
Question g ii) I was wondering whether the claim that the constant alpha appears to be zero should rather be non-zero, since we can clearly reject our null hypothesis that alpha january is equal to zero
everyone who checked the same value for the two questions is probably a joker. Therefore, you go along the diagonal line of 1/1, 2/2 etc. that's 5+0+0+31+1+3. disregard the 31 because we cannot be as sure about them and you get a total of 9.
Here's a better explanation for c). We can with a certainty of 0.693 say that the variances of the Dutch and the German are equal. In this case we have to look at the Sig. (2-sided) value of that row, which is 0.000. This means that we can with high certainty say that the true means of the two population groups are not the same (Reject the H0 hypothesis). Hope its clearer
I made a mistake here, just figured it out myself. Its actually the opposite. With all significance values (except Q9_6) below the 5% threshold, we can assume that the true means differ from the benchmark mean value of 3. For Q9_6 on the other hand, we can with certainty say that the true mean is 3 for the population
The whole idea is kind of that we want to be able to compare people birthdays, and do that by calculating how far away from the beginning of the respective year their birthday is. If you're birthday is on the 15th of April then that's 4-1=3 fully completed months and 15-1=14 fully completed days away from the beginning of the year. So your birthday is after 3 full months and 14 full days, meaning during the 4th month and 15th day. Hope that makes any sense
Instead of writing the same thing 20 times I just marked the correlations in the matrix that have a significantly high correlation and then sorted them whether they make sense to be positive or negative.
Q18 asks "which option is not correct" in version SZ the answer should be C (looking at the grid) but the next sentence in the explanation says "The correctness of a) and c) should be obvious"????? can anyone explain?
Can anyone explain Q13 step by step? I just don't get how you're supposed to get to the model solution by substitution. If I substitute y2= 1-y1 in the model, there's no more assurance there, how am I supposed to substract it then? And even if I get the first step, y1 and -1 equal themselves out don't they?
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exam 2016/2017 first sit question 25:
Equation: log ( market value ) = β0 + β1start + β2complete + α1 log(book value) + dummies + ε
As discussed halfway Case 6), the following pair of hypotheses in terms of α1, the coefficient of the
predictor variable log ( book value ) in model 2), is of considerable interest: H0:a1=1 vs. HA:a1„1
Now consider the following two claims about model 2):
I) If H0 does not hold, the effects of the start- and complete-coefficients cannot be interpreted in
terms of a firm’s Tobin’s q; only in terms of its market value.
II) When H0 holds, Tobin’s q will not be influenced by a firm’s book value.
a) Claim I) is correct, claim II) is incorrect
b) Claim I) is incorrect, claim II) is correct.
c) Both claims I) and II) are correct.
d) Both claims I) and II) are incorrect.
Hey @ Anonymous Pile of Poo
What one should do, is create the Tobin's Q Fraction on the left hand side --> to verify the respective effect
One can rewrite the equation like this: --> subtract log(book value) on both sides
log(market value) – log(book value) = β0 + β1start + β2complete + (α1–1)log(book value) + dummies +ε
The formula of Tobin’s q -->the ratio of market value and book value
Market Value / Book Value Assets
Further, one can still remember from QM1 that log(x) - log(y) = log(x/y) --> as a result, the left hand side of the above equation log(market value) – log(book value) --> log(Tobin’s q)
The final equation would be:
log(Tobin’s q) = β0 + β1start + β2complete + (α1–1)log(book value) + dummies +ε
Finally, based on the coefficients of the other independent variables (including book value), the coefficients of both dummies (start- and complete-dummies) can be interpreted in terms of Tobin’s q and market value.
The respective values do not matter (e.g. H0:a1=1 vs. HA:a1„1 )
Claim I) is incorrect
However, one can see that the above equation implies that Tobin’s q does, in fact, depend on the book value, unless –1 equals 0, i.e. the coefficient of book value is equal to 1 (H0:a1=1)
Claim II) is correct: coefficient of log(book value) has to be equal to 1 (as predicted to by the null hypothesis).
I hope this helps.
Your Success Formula Team.
WHAT'S UP WITH THATKARMA?