Testing

Question 1

statistical testing

Answer 1

The purpose of it is to make inferences about the population by information parameters by analyzing the sample. Thus, if some phenomenon is present in the sample, is it also present in the population. Statistical testing tells which of the hypotheses is supported.
Statistical tests can be divided into two groups: parametric and non-parametric based
on the distribution they use and the measurement level of the test variable.

Question 2

Hypothesis

Answer 2

is some theory, claim or assertion of a particular parameter of the population.

Question 3

Null hypothesis

Answer 3

is always formed as “no difference” or “no correlation” H0: σ1 = σ2

Question 4

Alternative hypothesis

Answer 4

(H1)
o H1: σ1≠ σ2

Question 5

Parametric tests assume

Answer 5

• Data at the interval or ratio level of measurement
• Normal distribution of the population (the test variable is normally distributed)

Question 6

P-value

Answer 6

is the probability of getting a test statistic equal to or more extreme than the
sample result, given that the null hypothesis is true.
The p-value is often referred to as the observed level of significance
Если Р меньше 0,05 = Н1 correct

Question 7

стр 67

Answer 7

пройти шаги

Question 8

Correlation

Answer 8

relationships between variables
Knowing the value of value X, we can say something additional about Y

Hence, it helps to understand if and how strong the relationship is + predict Y based on X

Question 9

Chi-Square test

Answer 9

Testing if there is a correlation, Used if there are few values and if it is not a continuus data
The reliability of the result when using the Chi test depends on the amount of data. To check it, you need to look at the table of expected values. None of them should be less than 1. No more than 20% of the cells should be less than 5.

Chi-square (χ²) test used to determine if two variables in a crosstabulation are independent or related. The process involves:

Crosstabulation: Creating a table to show the frequency of different combinations of variables (e.g., gender and department choice).

Expected Frequencies: Calculating expected values assuming the variables are independent. This is done by multiplying the row and column probabilities.

Chi-square Calculation: Using the formula:

χ2=∑Ei (Oi −Ei )2

where (O_i) is the observed frequency and (E_i) is the expected frequency.

P-value: Determining the p-value from the Chi-square statistic to assess the significance of the results. A p-value less than 0.05 indicates a significant correlation.

In the example provided, the p-value was 0.013, indicating a significant correlation between gender and department choice.

Question 10

пройти чи тест

Answer 10

стр 70

Question 11

объяснить формулу чи теста

Answer 11

стр 70