Post on 13-Jul-2020
ReviewSession 6
1
The residuals for the red estimation are …
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33%
0%0%
33%33%
1.
2.
3. log log 4. Noneoftheabove5. Idon’t know
The residuals for the green estimation are …
31 2 3 4 5
0% 0%0%
100%
0%
1.
2.
3. log log 4. Noneoftheabove5. Idon’t know
If the error termsare not homoscedastic, itis better to …
41 2 3 4
29%
14%14%
43%
1. Useℓ minimization rather than ℓ2. Rescale tomake theerror term
homoscedastic3. Useℓ minimization rather than ℓ4. Idonotknow
We want to fit a data set to a polynomial of degree 2:
.
Is this a linearregression model ?
51 2 3
0%
13%
88%
1. Yes2. No3. Idon’t know
The green estimation corresponds to assuming that the errorterms (blue dot green curve) are iidlognormal.
61 2 3 4
33%
22%
33%
11%
1. True2. Almost true3. False4. Idon’t know
Solution
7
The green estimation corresponds to assuming that the relative error terms(blue dot green curve) are iid normal.
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44%
0%
33%
22%
1. True2. Almost true3. False4. Idon’t know
Solution
9
The matrix for the model
has full rank.
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33%
11%11%
33%
11%
1. Yes2. No3. Itdependsonthe4. Itdependsonthe ′5. Idon’t know
We fit the model
using least squares.
The obtained line is such that the average distance from the points to the line is 0.
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22%
0%
22%
56%
1. True2. False3. Itdepends onthedata4. Idon’t know
Solution
12
We fit the model
using normminimization.
The obtained line leaves an equalnumber of points on each side
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78%
0%0%
22%
1. True2. False3. Itdepends onthedata4. Idon’t know
Solution
14
Find the parameter for each of thesestandard Pareto PDFs
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75%
0%0%0%
25%
1. 0.5; 1; 2; 32. 3; 2; 1; 0.53. 0.5; 2; 1; 34. 1; 2; 3; 0.55. Idon’t know
The skewnessindices S1, S2 and the Kurtosisindices K1, K2 are…
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0% 0%
13% 13%
25%25%
0%
13%13%
1. S1>0,K1>0,S2>0,K2>02. S1<0,K1>0,S2>0,K2<03. S1>0,K1>0,S2=0,K2>04. S1<0,K1>0,S2=0,K2<05. S1>0,K1<0,S2>0,K2>06. S1<0,K1<0,S2>0,K2<07. S1>0,K1<0,S2=0,K2>08. S1>0,K1<0,S2=0,K2<09. Idon’t know