The value of the parameter is that which maximizes a maximum likelihood function;

Theoretical moments are equal to empirical ones;

Before using the moments method we must assume about the moments of the parameter.

Before using the moments method we must assume the distribution of the variable;

The relation between X and Y can be expressed like this: y(x) = a + bx;

There is no relationship between X and Y.

There is strong positive relationship between x and y;

The variables are independent;

The variables are dependent;

If b = 0, then the prediction of y is always equal to E(Y);

Errors should be distributed by the normal distribution with a mean equal to zero, and variance σ²;

Variable x is called the dependent variable;

The linear regression model does not show that between variables exists linear dependency.

Data, which are measured by interval scale.

Qualitative data;

Data, which are measured by nominal scale;

Quantitative data;

hypothesis about the difference of the mean for independent samples.

hypothesis about coefficient of correlation;

hypothesis about the variance;

test of independence;

(5−10], (10−15], (15−20], (20−25], (25−30)

< 10, [10−15), [15−20), [20−25), [25−30)

(5−10), (10−15), (15−20), (20−25), (25−30)

[5−9], [10−14], [15−19], [20−24], [25−29]

The number of persons in the family

The time needed to go from home to work;

Education

Blood type

Some elements from the population have zero probability of being selected;

The set of population is unknown;

Quota sampling is non-probabilistic sampling;

Each element has a non-zero probability of being selected;

The set of population is known;