Question

Question 2

Distinguish between the following:

a. Parametric test and non-parametric test

b. Linearity and normality

c. Interpreting static variable and interpreting dynamic variable

d. P-value and α-value

e. Outliers and influential data points.

Answer 1

a)

1. Parametric test is a statistical test in which the researcher has an idea about the form of the distribution followed by the random variable under study and some claim about the unknown population parameter is to be tested.
2. Non-parametric test is one in which the researcher has no idea about the form of the distribution of the population. In this case, the test statistic is general based on positional statistic like median.

b)

1. Linearty means that two variables a related linearly, i.e., one variable can be expressed as a linear polynomial of the other.
2. Normality means that the random variable follows a normal distribution.

c)

1. A static variable is one in which the variable takes a constant value.
2. A dynamic variabe is one who values changes from one observation to another.

d)

1. p-value: It is the probability that the test statistic takes a value more extreme than the one observed from the current data, under the assumption that the null hypothesis is true. It is based on data.
2. Alpha-value: It is the probability of rejecting the null hypothesis given that the null hypothesis is true, i.e., it is the probability of committing type one error. It is not based on data but is pre-fixed to a small value.

e)

1. Outliers: Outliers are the data points for which the observation is far from most of the other data points.
2. An influential data point is one in which the regressor variable has a value similar to other data points but the response variable has a very different value compared to the other data points.

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