CN112632470A - Method for establishing college entrance probability based on UMPUT probability test - Google Patents

Method for establishing college entrance probability based on UMPUT probability test Download PDF

Info

Publication number
CN112632470A
CN112632470A CN202011535764.2A CN202011535764A CN112632470A CN 112632470 A CN112632470 A CN 112632470A CN 202011535764 A CN202011535764 A CN 202011535764A CN 112632470 A CN112632470 A CN 112632470A
Authority
CN
China
Prior art keywords
probability
test
equivalent
establishing
umput
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202011535764.2A
Other languages
Chinese (zh)
Inventor
赵龙霄
佘彦
任庆伟
李峥
潘生林
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang Cuiwen Technology Co Ltd
Original Assignee
Zhejiang Cuiwen Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang Cuiwen Technology Co Ltd filed Critical Zhejiang Cuiwen Technology Co Ltd
Priority to CN202011535764.2A priority Critical patent/CN112632470A/en
Publication of CN112632470A publication Critical patent/CN112632470A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Algebra (AREA)
  • Evolutionary Biology (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Complex Calculations (AREA)

Abstract

A method for establishing entrance probability of college entrance based on probability test of UMPUT relates to the technical field of hypothesis test, solves the problem of high entrance risk of the existing method for establishing entrance probability of college entrance, and comprises the following steps: step 1, calculating equivalent scores of the current year, which are equivalent to the equivalent scores of the previous year, by combining historical data to obtain an equivalent score sequence; step 2, establishing an admission probability model of the equivalent score sequence; and 3, carrying out uniform optimal potential unbiased inspection on the admission probability model obtained in the step 2. The method for establishing the entrance examination probability based on the UMPUT probability test can be used for testing whether the entrance probability is reasonable after the entrance probability model is established, so that the risk of entrance is greatly reduced.

Description

Method for establishing college entrance probability based on UMPUT probability test
Technical Field
The invention relates to the technical field of hypothesis testing, in particular to a method for establishing college entrance probability based on UMPUT probability testing.
Background
In the current big data era background, when many examinees fill in volunteers, some examination reporting mechanisms are selected or examination is reported through a network platform. In the face of the admission probabilities of various colleges and universities and major on the network, examinees and parents question whether the admission probabilities provided by the platforms are accurate and can be truly trusted.
At present, many platforms basically directly display the calculation result of the admission probability, and the calculation result is obtained by finding out a distribution function satisfied by the score based on various factors such as the score of the past year, the batch line, the scale of an examinee and the like. At present, few methods for detecting the calculated admission probability exist, the probability is originally disturbed by multiple factors, and certain accuracy is difficult to achieve, so that a method for establishing the high-examination admission probability capable of detecting whether the admission probability is reasonable or not is needed to reduce the risk of admission.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for establishing a probability of college entrance based on a probability test of UMPUT.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a method for establishing college entrance probability based on probability test of UMPUT comprises the following steps:
step 1, calculating equivalent scores of the current year, which are equivalent to the equivalent scores of the previous year, by combining historical data to obtain an equivalent score sequence;
step 2, establishing an admission probability model of the equivalent score sequence;
and 3, carrying out uniform optimal potential unbiased inspection on the admission probability model obtained in the step 2.
The invention has the beneficial effects that:
the entrance examination probability establishing method based on UMPUT probability detection can be used for detecting whether the entrance probability is reasonable or not after the entrance probability model is established, so that the risk of entrance is greatly reduced.
Drawings
Fig. 1 is a flowchart of a method for establishing a college entrance probability based on UMPUT probability test according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
A method for establishing a college entrance probability based on a probability test of UMPUT, as shown in fig. 1, includes the following steps:
step 1, calculating equivalent scores of the current year, which are equivalent to the equivalent scores of the previous year, by combining historical data to obtain an equivalent score sequence;
step 2, establishing an admission probability model of the equivalent score sequence;
and 3, carrying out unbiased inspection on the probability with the consistent optimal potential.
The historical data in the step 1 comprises data such as the lowest admission score, the average admission score, the number of examinees, score ranking, the number of recruits in the current year and the reference year enrollment plan and the like. The reference year may be a year or a few years before the current year, the current year is the year to be measured, the method for seeking the equivalent score is equivalent to the method for discounting the option price, and the difference is only that the discount basis in the invention is data such as the number of examinees and the plan for enrollment. Converting the college entrance examination score of the year to be measured into an equivalent score of the college entrance examination of the corresponding reference year to obtain an equivalent score sequence X ═ (X)1,x2,…xn)。
The step 2 specifically comprises the following steps:
calculating sample mean and standard deviation of equivalent fractional sequences
Figure BDA0002853031850000021
Figure BDA0002853031850000022
Wherein xiIs any one of the sequences X, i ═ 1,2, …, n;
the score is wholly obeyed normal distribution, and white noise is added to depict uncertain factors influencing the admission probability, so that the admission probability is obtained:
Figure BDA0002853031850000031
where ω(s) is white noise, a derivative form of brownian motion, which is commonly described in physics for irregular motion of particles, and is used here to characterize random uncertainty factors that affect probability of enrollment, such as policies of different years; x represents a function of X, which is a sufficient statistic for the variable X.
For step 3, the equivalence score sequence satisfies the normal distribution, and the step 3 specifically includes: and carrying out consistent optimum potential unbiased test on the sample mean values met by the identity score sequence, and carrying out consistent most dominant unbiased test on the standard deviation met by the identity score sequence. The details are given by way of specific examples:
the invention adopts the method of respectively carrying out the most dominant and unbiased test on the sample mean value and the standard deviation satisfied by the equivalent fraction sequence of a certain fraction.
Taking the sample mean test as an example, the hypothesis test problem is H0：μ≥μ0To H1：μ＜μ0. Here mu0The selection is based on the year-round specific admission score, mu, of the college major0The average lowest admission score of the past year admission scores. Suppose sample X ═ X1,x2,…xn) Satisfy the normal distribution N (mu, sigma)2) The college entrance examination is divided into 750 minutes, wherein 0 < mu < 750, sigma2Is greater than 0. Y represents the function of Y, i.e. the sufficient statistic of the variable Y, and is made to be x-mu0Obtaining a sample Y ═ (Y)1,y2,…yn) Sample Y ═ Y1,y2,…yn) The combined density of (a) is:
Figure BDA0002853031850000032
where θ is n μ/σ2，r＝(-2σ2)-1，
Figure BDA0002853031850000033
Figure BDA0002853031850000034
Is the average value of the sequence of Y,
Figure BDA0002853031850000035
yirepresents any one of the sequences Y. The test problem with the mean value μ then becomes the test problem with θ. The problem examined at this time is equivalent to H0: theta is more than or equal to 0 to H1: and theta is less than 0.
It is set that the statistical quantity W satisfies:
Figure BDA0002853031850000036
u represents the sufficient statistic of μ and T represents the sufficient statistic of T, because U is the sufficient statistic of μ, then W is a linear function of U for a fixed T; and when mu is equal to mu0Due to
Figure BDA0002853031850000037
Is σ2Is a full, complete statistic of, and due to the distribution of W and the parameter σ2Independent, so W and T are independent of each other, so the rejection field of UMPUT for this test problem is { y: | ≧ c }, c is a constant greater than 0, and then find the statistic V ═ V (U, T), so that for a given T, V is a monotonically increasing function of U, and V and T are independent of each other when θ ═ 0, i.e., when μ ≧ 0. To this end order
Figure BDA0002853031850000041
So the rejection region is finally obtained as { y: |, V | ≧ t1-α/2(n-1) }, i.e. t test with test level alpha, so that the reasonability of the admission probability can be tested according to the interval, i.e. the statistic { y: | < V | > t1-α/2(n-1) } the probability of admission is reasonable.
The standard deviation is then examined, assuming that the examination problem is：H0:σ1 2≤σ2≤σ2 2To H1:σ2＞σ2 2Or σ2＜σ1 2. Where σ is1And σ2The selection is based on the college professions' past year specific admission score, sigma1To set the lower limit of the standard deviation, σ2Is the upper limit of the standard deviation set. The sample joint density function is the same as in the mean test described above, where r is (-2 σ)2)-1Then the inspection problem becomes H0:(-2σ1 2)-1≤r≤(-2σ2 2)-1To H1:r＞(-2σ2 2)-1Or r < -2 sigma1 2)-1。
It is set that the statistic Z satisfies:
Figure BDA0002853031850000042
to be fixed
Figure BDA0002853031850000043
Z is a linearly increasing function of T. In a similar way, at σ2＝σ1 2Or σ2＝σ2 2When, Z-sigma1 2·χ2(n-1) or Z-sigma2 2·χ2(n-1)。
The rejection field of the test problem is therefore { y: Z < σ }1 2·χ2(n-1) or Z > sigma2 2·χ2(n-1) }, here using χ2And (5) a test method.
In the present embodiment: if and only if both V and Z are not in the rejection region, the mean and variance are within reasonable ranges, at which point the probability of enrollment is reasonable.
According to the invention, white noise is integrated to characterize uncertain factors influencing the admission probability when the admission probability model is established, and finally, a reasonable probability interval is found by utilizing the consistent most advantageous unbiased test, so that the aim of testing the admission probability is achieved, and the risk of reporting a check is reduced to a great extent. The invention provides a method for establishing the probability of entrance examination of college entrance examination, which can check whether the probability of entrance examination is reasonable or not, and greatly reduces the risk of entrance examination.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (6)

1. A method for establishing college entrance probability based on probability test of UMPUT is characterized by comprising the following steps:
step 1, calculating equivalent scores of the current year, which are equivalent to the equivalent scores of the previous year, by combining historical data to obtain an equivalent score sequence;
step 2, establishing an admission probability model of the equivalent score sequence;
and 3, carrying out uniform optimal potential unbiased inspection on the admission probability model obtained in the step 2.
2. The method as claimed in claim 1, wherein the equivalent score sequence obtained in step 1 is X ═ X (X) for the probability test based on UMPUT1,x2,…xn) The step 2 specifically comprises:
computing sample means of equivalent fractional sequences
Figure FDA0002853031840000011
And standard deviation of
Figure FDA0002853031840000012
Wherein xiIs any one of the sequences X, i ═ 1,2, …, n;
admission probability of
Figure FDA0002853031840000013
Where ω(s) is white noise.
3. The method for establishing a probability of college entrance based on UMPUT probability test as claimed in claim 1, wherein the equivalent score sequence satisfies a normal distribution, and the step 3 specifically comprises: and carrying out consistent optimum potential unbiased test on the sample mean values met by the identity score sequence, and carrying out consistent most dominant unbiased test on the standard deviation met by the identity score sequence.
4. The method of claim 3, wherein the enrollment probability model is only reasonable if and when the sample mean is not in the reject domain of the consensus most dominant unbiased test and the standard deviation is not in the reject domain of the consensus most dominant unbiased test.
5. The method for establishing probability of college entrance based on UMPUT probabilistic testing as recited in claim 3, wherein the performing a consensus optimality unbiased test on the sample means satisfied by the equivalence score sequence specifically comprises:
X＝(x1,x2,…xn) Satisfy the normal distribution N (mu, sigma)2) (ii) a Hypothesis testing problem is H0：μ≥μ0To H1：μ＜μ0，μ0An average minimum admission score for past year admission scores; for X ═ X1,x2,…xn) Do y ═ x-mu0Obtaining a sample Y ═ (Y)1,y2,…yn) Y is a function of Y, Y is a sufficient statistic of the variable Y, and the sample Y is (Y ═ Y1,y2,…yn) The combined density of (a) is:
Figure FDA0002853031840000021
wherein θ is n μ/σ2，r＝(-2σ2)-1，
Figure FDA0002853031840000026
Figure FDA0002853031840000027
Is the average value of the sequence of Y,
Figure FDA0002853031840000022
yirepresents any one of the sequences Y;
it is set that the statistical quantity W satisfies:
Figure FDA0002853031840000023
wherein the content of the first and second substances,
Figure FDA0002853031840000024
is the average value of the X sequence, U represents the sufficient statistic of mu, and T represents the sufficient statistic of T; obtaining the rejection region as { y: | < W | > or |, c is a constant greater than 0;
it is set that the statistic V satisfies: v is V (U, T), which is a monotonically increasing function of U for a given T, and is independent of T when θ is 0, such that
Figure FDA0002853031840000025
The rejection region is obtained as { y: | < V | > t1-α/2(n-1)}。
6. The method for establishing a probability of college entrance based on UMPUT probability test as claimed in claim 5, wherein the performing the consensus most dominant unbiased test on the standard deviation satisfied by the equivalent score sequence specifically comprises:
assume that the test problems are: h0:σ1 2≤σ2≤σ2 2To H1:σ2＞σ2 2Or σ2＜σ1 2，σ1To set the lower limit of the standard deviation, σ2Is the upper limit of the set standard deviation; let r be (-2 σ)2)-1Then the inspection problem becomes H0:(-2σ1 2)-1≤r≤(-2σ2 2)-1To H1:r＞(-2σ2 2)-1Or r < -2 sigma1 2)-1；
It is set that the statistic Z satisfies:
Figure FDA0002853031840000031
to be fixed
Figure FDA0002853031840000032
Z is a linearly increasing function of T; the rejection region is obtained as { y: Z < sigma1 2·χ2(n-1) or Z > sigma2 2·χ2(n-1)}。
CN202011535764.2A 2020-12-23 2020-12-23 Method for establishing college entrance probability based on UMPUT probability test Pending CN112632470A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011535764.2A CN112632470A (en) 2020-12-23 2020-12-23 Method for establishing college entrance probability based on UMPUT probability test

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011535764.2A CN112632470A (en) 2020-12-23 2020-12-23 Method for establishing college entrance probability based on UMPUT probability test

Publications (1)

Publication Number Publication Date
CN112632470A true CN112632470A (en) 2021-04-09

Family

ID=75321565

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011535764.2A Pending CN112632470A (en) 2020-12-23 2020-12-23 Method for establishing college entrance probability based on UMPUT probability test

Country Status (1)

Country Link
CN (1) CN112632470A (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103823933A (en) * 2014-02-26 2014-05-28 大连理工大学 Method for processing metal cutting simulation data
CN108734131A (en) * 2018-05-22 2018-11-02 杭州电子科技大学 A kind of traffic sign symmetry detection methods in image
CN108874749A (en) * 2018-06-22 2018-11-23 西南科技大学 A kind of method for building up of college entrance will admission probabilistic model
CN111861033A (en) * 2020-07-30 2020-10-30 浙江萃文科技有限公司 College entrance probability prediction method with white noise

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103823933A (en) * 2014-02-26 2014-05-28 大连理工大学 Method for processing metal cutting simulation data
CN108734131A (en) * 2018-05-22 2018-11-02 杭州电子科技大学 A kind of traffic sign symmetry detection methods in image
CN108874749A (en) * 2018-06-22 2018-11-23 西南科技大学 A kind of method for building up of college entrance will admission probabilistic model
CN111861033A (en) * 2020-07-30 2020-10-30 浙江萃文科技有限公司 College entrance probability prediction method with white noise

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
茆诗松等著: "《高等数理统计》", 高等教育出版社等, pages: 202 - 212 *

Similar Documents

Publication Publication Date Title
Bernardi Validating research results when Cronbach's alpha is below. 70: A methodological procedure
Sackett et al. On seeking moderator variables in the meta-analysis of correlational data: A Monte Carlo investigation of statistical power and resistance to Type I error.
Gracia et al. Prevalence of intimate partner violence against women in Sweden and Spain: A psychometric study of the ‘Nordic paradox’
Campbell et al. Assessing colour-dependent occupation statistics inferred from galaxy group catalogues
CN105930976B (en) Node voltage sag severity comprehensive evaluation method based on weighted ideal point method
CN109816031B (en) Transformer state evaluation clustering analysis method based on data imbalance measurement
CN112347652B (en) Heavy rain high risk division method based on linear moment frequency analysis of hydrological region
CN110348516B (en) Data processing method, data processing device, storage medium and electronic equipment
Alvarez‐Esteban et al. Time series clustering using the total variation distance with applications in oceanography
US20060241904A1 (en) Determination of standard deviation
CN112632470A (en) Method for establishing college entrance probability based on UMPUT probability test
CN117593115A (en) Feature value determining method, device, equipment and medium of credit risk assessment model
Saputro et al. The modeling of human development index (HDI) in Papua—Indonesia using geographically weighted ridge regression (GWRR)
CN110045250B (en) Method and system for judging insulation state of power cable
CN113673609B (en) Questionnaire data analysis method based on linear hidden variables
CN107798149B (en) Aircraft maintainability assessment method
RU2148274C1 (en) Method for identification of person using properties of signature
Leng et al. Endpoint estimation for observations with normal measurement errors
CN108874749B (en) Method for establishing college entrance examination volunteer admission probability model
Knoblett The applicability of Bayesian statistics in auditing
CN114582507A (en) Method for analyzing environmental risk factors of alveolar echinococcosis based on geographic detector
CN115062073A (en) College entrance examination probability verification method based on single-parameter bilateral hypothesis test
Petkovic et al. Verifying the accuracy of machine vision algorithms and systems
Olanrewaju et al. Kullback-Leibler Divergence of Mixture Autoregressive Random Processes via Extreme-Value-Distributions (EVDs) Noise with Application of the Processes to Climate Change
CN116187299B (en) Scientific and technological project text data verification and evaluation method, system and medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination