Applied data analysis ---->1- In which of the following situations is the assump

Question

Applied data analysis

---->1- In which of the following situations is the assumption of normality least important? A- If you want only to estimate the parameters of your model. B- If you have a small sample. C- If you want to construct confidence intervals around the parameter estimates of your model. D- If you want to compute significance tests relating to the parameter estimates of your model.

. ----> 2- A kurtosis value of -2.89 indicates: (Hint: Positive values of kurtosis indicate too many scores in the tails of the distribution and that the distribution is too peaked, whereas negative values indicate too few scores in the tails and that the distribution is quite flat.) : A- A flat and heavy-tailed distribution. B- A pointy and heavy-tailed distribution. C- A flat and light-tailed distribution. D- There is a mistake in your calculation.

---->3- Is it possible to calculate the skewness of a set of numerical scores? A- Yes. B- No. C- Only if you have a large sample size. D- Only if you have used an independent-measures design.

---->4 - Should you use significance tests of skew and kurtosis in large samples? A- Yes, because large samples add power to the test. B- No, because they are likely to be non-significant even when skew and kurtosis are significantly different from normal. C- Yes, because large samples produce more accurate results. D- No, because they are likely to be significant even when skew and kurtosis are not too different from normal.

----> 5- A standard score is: A- The variance. B- The standard deviation of a particular score. C- The population mean. D- A z-score

Explanation / Answer

1. Option B is correct.

2. Option C is correct.

3. Option C is correct.

4. Option B is correct.

5. Option D is correct.

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