Corresponding Techniques for Task Type T 1 Μ , T 1 P , T 1  2

We call:

- Test method

2 is the test that K has a Chi square distribution

2 .

The problem of testing statistical hypotheses related to the parameters of random variables is divided into 3 cases according to the pair of hypotheses H 0 , H 1 , in which we call:

- “Hypothesis testing

- “Hypothesis testing

left​​

H 0 :   0 with the alternative hypothesis

H 0 :   0 with the alternative hypothesis

H 0 :   0 with the alternative hypothesis

H 1 :   0 ” is the test

H 1 :   0 ” is the test

H 1 :   0 ” is the test

1 

Symbol ZL is a technique for testing a mean using the Z-test.

left , that is how to solve the problem of testing the hypothesis about the average of a random variable

normal distribution in the case of unknown variance

2 , fake test

theory

H 0 :   0

with alternative hypothesis

H 1 :   0 ”, select test statistic

K  ( X   0 )

n

S

has a standard normal distribution N (0 ; 1). We have ZL

1 

is a technique to solve

decide the task type T 1μ .

b) About examples and exercises

5.14. In the exercises section of the GT V textbook , there are 9 exercises on this topic, including exercises 1 to 9. Similarly, for statistical hypothesis testing of conformity, the GT V textbook provides 6 examples, from example 5.15 to example 5.20, and there are 6 exercises on this topic, including exercises 10 to 15. In the examples, the techniques for solving various types of tasks are illustrated in detail. Consider example 5.2 in the GT V textbook below:

“Example 5.2. A medical report states that the average hemoglobin level for Vietnamese people is 138 (g/l). Measuring the hemoglobin of 25 factory workers exposed to chemicals, the sample mean is 123.7 (g/l), the sample standard deviation is 13.2 (g/l). From this survey result, is there a basis to say that the average hemoglobin of factory workers is lower than the average of Vietnamese people, with a significance level of 0.05. Suppose that the hemoglobin of factory workers is a random variable that satisfies the normal distribution.

Solution: Let X be the amount of hemoglobin of chemical factory workers, X  N (  ; 2 ) . Problem of testing hypothesis about the mean of a normally distributed random variable in case the variance is unknown.

Hypothesis testing

H 0 :  138 (g/l) with alternative hypothesis

H 1 :  138 (g/l).

Test statistics

K . If hypothesis H

right then

( X  138) n

S0

K  ( X  

)n

0.05

S

has a Student distribution with degrees of freedom df = n – 1 = 24.

With = 0.05, region of rejection of the hypothesis

W  (  ;  t 24

)  (  ;  1, 711) .

From the survey sample data n = 25, we have

K 0  5, 417 .

(123, 7  138) 25

13, 2

K 0  W There should be a basis to reject the hypothesis H 0 and accept H 1 .

From this it can be assumed that the average hemoglobin of chemical factory workers is lower than the national average” [GT V , pp.117-118].

1 

1 

Example 5.2 illustrates the  technique.tL solves a task type T 1μ , where  tL

is a left-tailed t-test, used to test whether the mean of a random variable satisfies a normal distribution, the population variance is unknown, the test statistic K is chosen to have a Student distribution.

On the technique of solving different types of tasks T

; T ; T ; T

; T pair ; T ;​​

1 μ 1 p

1 2

2 μ 2 

2 p 2 2

T Anova ; T phuhop ; T doclap , we summarize in Table 3.3, Table 3.4 and Table 3.5.

2 2

Table 3.3. Corresponding techniques for task types T 1 μ , T 1 p , T 1 2

Task type

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Table 3.4. Corresponding techniques for task type T

, T pair , T , T , T

2 μ 2 2 p 2 2

Anova

Task type

Table 3.5. Corresponding techniques for task types T phuhop , T doclap

 2  2

Task type

The technological factors that explain the respective techniques for solving these 10 types of tasks include:

- Rules of inference of statistical hypothesis testing.

- Theories to support each step of the inference of the tests

indicated in each technique.

- Theory of validity when using tests: for parametric tests, the conditions to be checked are whether the random variable must satisfy the normal distribution, the variance is known or unknown, the variances are homogeneous or not,

large or small sample size; for tests

2 , the condition to be checked is sample size,

Experimental frequency, expected frequency large or small.

statistical hypothesis testing T

, T , T

, T , T pair , T , T , T

, T phuhop , T doclap ,

1 μ 1 p

1 2

2 μ 2 

2 p 2 2

Anova 2 2

For the type of mission that requires SLTKYH at a higher level than T kdgt , T kdgt then GT V has not been

Gkl UNcyh

mentioned, although it has included practical medical problems that require the use of SPSS statistics to process data.

3.3.2.4. Topic “Regression and Correlation Analysis” in GT V textbook

6.1. Regression analysis

6.2. Regression model fit

6.3. Correlation analysis

6.4. Regression and correlation analysis in SPSS

a) In theory

When several random variables are studied simultaneously, the question arises whether these variables are related or independent. In the case of a relationship, it is necessary to determine the degree of the relationship and, if possible, to represent the relationship using mathematical expressions. In medical research, people are often interested in assessing the relationship between quantitative variables, for example the relationship between blood pressure and age, weight and height, drug concentration and heart rate.

Regression and correlation analysis presents methods for analyzing that relationship according to three issues:

- Determine whether or not there is a relationship between two variables by testing the hypothesis of statistical independence.

- Assess the degree of association between two variables based on the concept of correlation coefficient.

- Describe this relationship in a mathematical form called a regression function [GT V , p.153].

With the task type

1

T

HqTq

establish a sample regression function, section 6.5.1 estimates the parameters

T

service

2

HqTq

, GT V refers to the theory presented in section 6.2 of GT V , however

There are no illustrative examples. The GT V textbook has presented techniques for solving the problems.

T 3 and T 4 task types are implicitly presented in the theory and illustration section.

HqTq HqTq

through examples, however, not complete. Section 6.3 Correlation analysis, GT V textbook presents the definition of Pearson correlation coefficient, but does not state the standard conditions of two random variables X , Y , and does not present the correlation testing procedure based on sample data.

b) About examples and exercises

HqTq

techniques for solving task type T 1 . Examples 6.3, 6.4 and 6.5a relate to techniques

solve the task type

3

T

HqTq

. Related to task type solving techniques

HqTq

Conclusion. GT V textbook gives example 6.6 to illustrate the technique of solving this type of problem.

Exercises related to task types T 1 , T 3 , T 4 similar to example 6.5.

HqTq HqTq HqTq

all 4 types of tasks T 1 , T 2 , T 3 , T 4 , however only mentioned the case

HqTq HqTq HqTq HqTq

examining the relationship between two variables, not to mention the case of examining the relationship between multiple variables. The requirements for solving these types of tasks are also limited to performing calculations, applying familiar formulas and algorithms, not taking into account SLTKYH at a higher level. Although practical medical problems have been included in which the use of SPSS statistics is required to process data, the task of applying regression and correlation analysis methods in practical problem solving in medical research has not been mentioned, making well-founded predictions, these predictions are used in diagnosing, preventing diseases or evaluating the effectiveness of treatment methods.