Q1 If two variables are correlated then the observations will have a pattern of

Question

Q1

If two variables are correlated then the observations will have a pattern of covariance

Question 2

For regression, the data need only be normally distributed in the vertical direction to meet assumptions for the statistical analysis

Question 3

For correlation, we will test the null hypothesis that the slope relating the dependent variables as a function of the independent variable is equal to zero

Question 4

If you find that there is a significant correlation between 2 variables, you will report that p<0.05 in your conclusion statement (statistical evidence)

Question 5

For the Mann Whitney U Test and Spearman's Rank correlation, the analysis is on the ranks of the data instead of the actual values in the data set

Question 6

For the 1 and 2 population sign tests, the analysis is on whether the data are positive, negative or zero instead of the actual values in the data set

Question 7

For the nonparametric tests discussed in this class, parameteric tests always have greater statistical power than nonparametric tests

Explanation / Answer

1. FALSE. Covariance is a measurement of how related the variances between two variables. The extend to which any two random variables change together or vary together can be defined as covariance. It is really the association between them

5. TRUE. Analysis is done on observations' positions in relation to another, not on their values.

6. TRUE. Sign tests look at the sign of the observation, not the actual value.

7.TRUE. Parametric tests are always more powerful, however we cannot use them if assumptions are violated.

Hope this helps. God Bless you :-))

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