A question on nonparametric tests in educational statistics 150

Use the Man Whitney U test, mix the two sets of data, and then sort them, and <> sort them.

It should be that the results of the experiment are not different (I am not a student of statistics, and it is possible to make a mistake (*.)

<>r **：x=c(19,32,21,34,19,25,25,31,31,27,22,26,26,29)

y=c(25,30,28,34,23,25,27,35,30,29,29,33,35,37,24,34,32)

n=length(x)

m=length(y)

w=sum(rank(c(x,y))[1:length(x)])exp_w=n*(n+m+1)/2

sd w=sqrt(n*m*(n+m+1) 12) n*m*(n+m+1) 12 16=should be used here.

z_w=(w-exp_w)/sd_w

The z-value is , the ratio is small, so if the significance level is, the null hypothesis should be rejected and considered different.

p=pnorm(z_w)*2

print(p)

1. The confidence interval of 95% of the average weight of the food is (,, to determine the 95% confidence interval, it is necessary to find the confidence interval z value of the sum. By querying the normal step-by-step **, find the corresponding z-value as .

2. If the weight of the specified food is less than 100 grams, it is unqualified, and the confidence interval of 95% of the pass rate of the batch of food is determined to be (,.

3. The batch of parts meets the requirements of the standard.

In the statistical test, it is important to consider whether the non-parametric statistical method is used ( ).

a.The decision should be made based on the purpose of the study and the characteristics of the data.

b.The choice can be made after several statistics have been calculated and preliminary conclusions have been drawn.

c.It depends on which statistical conclusion conforms to the professional theory.

d.It depends on which value is smaller.

e.Since non-parametric statistics do not have strict requirements for data, they can be used directly in any case.

Answer analysis aThe decision should be made based on the purpose of the study and the characteristics of the data.

Statistical testing (the process of rejecting or not rejecting a null hypothesis of one or more population distributions based on the results of sampling) is generally referred to as hypothesis testing.

Hypothesis testing, also known as statistical hypothesis testing, is a statistical inference method used to determine whether the differences between samples and samples and populations are caused by sampling errors or essential differences.

Significance testing is one of the most commonly used methods of hypothesis testing, and it is also the most basic form of statistical inference, which is based on the basic principle of making some kind of assumption about the characteristics of the population, and then making inferences about whether the hypothesis should be rejected or accepted through statistical reasoning from sampling studies. Commonly used hypothesis testing methods include z-test, t-test, chi-square test, f-test, etc.

The basic idea of hypothesis testing is the principle of "small probability events", and its statistical inference method is a counterproof method with a certain probabilistic nature. The idea of low probability is that a small probability event will not occur in a single trial. The idea of the counter-evidence method is to first put forward a test hypothesis, and then use appropriate statistical methods to determine whether the hypothesis is true by using the principle of small probability.

That is, in order to test whether a hypothesis h0 is correct, the hypothesis h0 is first assumed to be correct, and then a decision to accept or reject hypothesis h0 is made based on the sample. If the sample observations cause a "small probability event" to occur, hypothesis h0 should be rejected, otherwise hypothesis h0 should be accepted.

The so-called "small probability event" in hypothesis testing is not an absolute contradiction in logic, but is based on the principle widely adopted in practice, that is, small probability events almost never occur in an experiment;

Obviously, the smaller the probability of a "small probability event", the more convincing it is to reject the null hypothesis h0, and this probability value is often remembered as (0< <1), which is called the significance level of the test.

The significance level of the test is not necessarily the same for different problems, and it is generally accepted that the probability of an event occurring is less than05 or so on, i.e. "small probability event".

Whether to choose a non-parametric statistical method in the Lingwang statistical test, ()aDecisions should be made based on the purpose of the study and the characteristics of the data.

b.The choice can be made after calculating several statistics and deriving a preliminary conclusion.

c.It depends on which statistical conclusion conforms to the professional theory.

d.It depends on which p-value is smaller.

Correct Answer: a

Answer]: 2

The test has a wide range of uses in non-parametric statistics, because: (1) there are many nonparametric hypothesis testing problems that can be transformed into the problem of testing the close degree between the number of observations and the expected number of times, and the test statistics here do not depend on the distribution form of the population, but are used as a method to test whether the population distribution is the probability distribution of a particular object; (2) The data collected by this method can be the spacing measurement data or the naming measurement data. χ2

The test is mainly used for goodness-of-fit testing and independence testing.

When applying the 2 statistic for testing, it should be noted that: (1) the theoretical frequency fe of each group shall not be less than n; (2) The frequency n should be large, at least greater than 50; (3) If the theoretical frequency of a group is less than 5, several adjacent groups can be merged until the theoretical frequency is greater than 5, and then check 2

k-r-1) should be the k-value of the actual number of groups after merging.

Answer]: The basic idea of hypothesis testing is to first put forward a certain hypothesis about the overall Zen state parameters or distribution of the Antui, and then use the sample information and the distribution characteristics of the relevant statistics to test this hypothesis, and make a conclusion about whether to reject the original hypothesis.

Answer] :(1) Two types of errors in hypothesis testing: Two types of errors in statistical testing, namely errors and errors.

Error refers to the "abandonment of truth" error made by rejecting the null hypothesis when the null hypothesis (HO) is true, also known as a type I error. The p-error refers to the "falsification" error made by not rejecting the null hypothesis when the null hypothesis is not true, also known as the type error. (2) The relationship between the two types of errors: and is discussed in the case of assuming that the null hypothesis is true and the null hypothesis is false, respectively, so + is not necessarily equal to 1.

All other things being equal, and it is not possible to decrease or increase at the same time. The most straightforward way to minimize the size of the sample is to increase the sample size. i- Statistically known as statistical testing.

The answer to question 14 is: ,.

The answer to question 15 is: The hemoglobin of the students in this school is significantly lower than the normal value. Hemoglobin is an important indicator to evaluate whether a patient is anemic.

Anemia refers to the loss and reduction of blood from various causes, making hemoglobin lower than the reference value, which is an important aspect of observing human health.