统计题（Statistical questions）0. what is the standardization law? What is the significance of standardization? What criteria are used to meet the material requirements and the direct method?The standard method is two or more than two of total numbers were compared in order to eliminate the internal structure of the different effects of the uniform standards, different groups of internal mean method after adjustment were compared.When the number of observation cases in each group is large enough and the number of positive cases is known, the direct method can be used1 the significance of medical reference value range and the method of determining itThe meaning is the fluctuation range of data determined by a certain probability, as the reference index to judge the normal and abnormal in clinicMethods: normal distribution method, steps: 1> normality test, 2> if normal, calculated mean X and standard deviation S, 3> estimation range of reference value x (-) + ua/2S2 how do four table data undergo hypothesis testing?1. establish hypothesis testing and establish inspection levelH0: the sample rate of the two classified variables is the sameH1: the data rate of the two classified variables is differentA=0.052. compute test statisticsA., if n>=40, but with 1<T<5, requires continuity correction of X2 valuesB. If n<40 or T<=1, the exact probability method3. determine the P value and draw inferences3, talk about your understanding of hypothesis testing P valuesThe meaning of the P value is the probability that the total random sampling from H0 is equal to and equal to or equal to or equal to the probability of the test value obtained not less than the existing sample. If: p<=a, then the conclusion rejected H0 accept H1, according to a test level, get "difference is statistically significant" conclusion. On the other hand, p>a, to H0, according to the a test standard, "no significant difference" conclusion, but not "no difference" conclusion, only "according to the test results, still can't believe that there is difference between" conclusion, because H0 is not equal to refuse to accept H0.4, briefly describe the differences and connections between type I errors and type II errors, and understand the practical significance of these two types of errors.The type I error refers to the establishment of the H0 I actually refused to make mistakes, "abandoning the truth" the probability of a, the type II error refers to the error from the "pseudo" received the H0 was not made, the probability of B. When the sample is n, the smaller the A, the bigger the B; on the contrary, the larger the A, the smaller the B; if the application should focus on reducing a, then take the a=0.05; if the focus is reduced by B, take a=0.10 or 0.20, or even higher.5. briefly describe the relation between normal distribution, two term distribution and Poisson distribution.Normal distribution: the estimation of the frequency distribution of the normal distribution of continuous random variablesTwo distribution: there were only two possible outcomes in each trial, and they were antagonistic; each experiment was independent and independent of the other test resultsPoisson distribution: a discrete distribution of a single parameter, representing the average number of times in a unit, time, or spaceWhen the n is larger or the PI is not close to 0 or 1, the two term distribution can be regarded as approximately normal distributionPoisson can be considered as a limit of the two distribution, that is, when Pi is small and n tends to infinity, the two termdistribution is approximately Poisson distribution, and when >=20, we can approximate it by normal distribution6. what are the indicators that describe the trend of concentration? What are the similarities and differences in its scope of application?Mean: normal or approximately normal distributionGeometric mean: geometric series or lognormal distributionMedian: the data is skewed distribution; irregular distribution; uncertain data (opening data) at one or both ends.7 what is hypothesis testing and can be illustrated by examplesHypothesis testing is the use of small probability apagoge thought, make the first two opposite to the general characteristics of statistical assumptions, and then calculate the test statistic in H0 was established under the condition that the probability of getting the value of P, a compared to determine the statistical inference process of H0 is established and the probability of a predetermined value1. establish hypothesis testing and establish inspection levelH0: the sample rate of the two classified variables is the sameH1: the data rate of the two classified variables is differentA=0.052. compute test statistics3. determine the P value and draw inferences8 talk about your assumptions about the conclusions of the testsThe conclusion is based on the principle of hypothesis testing a small probability event may not occur for the actual test, therefore rejected when testing hypotheses may make the type I error, when the tested hypothesis is likely to make the type II error.9. compare the similarities and differences between standard and standard errorsThe standard deviation of individual differences in size of n = S, and the average combination can make a reference rangeStandard error of sampling error size n = - 0 and mean confidence interval can be calculated with the overall mean number10, how can sampling errors be controlled or reduced in sampling studies?A reasonable sampling design increases the sample size11., what is sampling error? Why is sampling error inevitable in sampling studies?Sample and sample statistics by sampling. The difference caused by sample statistics and overall parameters. Because the individual differences exist, the research object is part of the total, so this part of the overall results and the differences between the results of color is inevitable.12. can we say that the smaller the p value of the hypothesis test is, the greater the difference between the two overall indicators? Why?No, because of differences in size and overall index P value of size is not exactly the same size except with the overall difference is related to the size of the.P value, and the sampling error is related to the size of the overall difference, the same, the sampling error of different sizes, the P will not the same size, the sampling error of actual work is mainly reflected in the sample size.13 in the rank sum test, why does the same data appear between the different groups, and the average rank is not necessary in the same set of data?The basic idea of rank test is: if the establishment of the H0, when N1 and N2 were determined after sample content for rank and T N1 samples and the average rank and should not vary greatly; if the disparity is that two of the overall distribution. Without the calculation of the average rank will not affect the rank in the same set of data however, different groups have the same data to calculate the average rank will affect the T valueCharacteristics of 14.t distributionThe 1.t distribution is a unimodal distribution with 0 as the center and the left and right sides symmetrical;2.t distribution curve is a cluster of curves, and its morphological change is related to the degree of freedom v. The degrees of freedom of V is small, the T value is more dispersed, the curve is flat; degree of freedom V increased gradually, t distribution gradually approaching the standard normal distribution. When v= is infinite, the t distribution becomes a standard normal distribution.Characteristics of 15 and Poisson distribution.1.Poisson distribution is a single parameter discrete distribution, whose parameter is mu, which represents the average number of events occurring in unit time or space, also known as intensity parameter.The variance sigma 2 of the 2.Poisson distribution is equal to the mean mu, that is, sigma 2= MuThe 3.Poisson distribution is asymmetric, and is skewed distribution when it is not large. It is rapidly approaching the normal distribution with the increase of mu. Generally speaking, when =20, we can consider approximately normal distribution, and Poisson distribution data can be processed by normal distribution.The cumulative probabilities of the 4.Poisson distribution are two kinds of cumulative left and right. The number of eventsoccurring in a unit, time, or space.The shape of the 5.Poisson distribution depends on the size of the mu. The value is small, distribution is partial, with Mu increases, the more symmetrical distribution, when =20, the distribution is close to normal distribution, when =50, can be considered Poisson distribution normal distribution N (U, U), according to the normal distribution.16, what are the characteristics of the sampling distribution of the sample mean?Among the 100 sample numbers, there were differences in the mean of each sample, but the mean of each sample fluctuated around the population mean.The distribution curve of sample number is middle height, both sides are low, left and right symmetry, and approximately obeys normal distribution.The standard deviation of sample mean is obviously smallerWhat are the characteristics of the 17 and t distributions compared with the U distribution?1., the two are unimodal distribution, t distribution curve to t=0 as the center, the left and right sides symmetryThe peak value of 2.t distribution curve is lower, and the tail curve is higher. It shows that the individual of far t value is relatively more, and the smaller the degree of freedom is,the more obvious the situation is3. as the degree of freedom increases, the t distribution is more and more close to the standard normal distribution. When the v= is infinite, the limit distribution of the t distribution is the standard normal distribution18, why do we assume that the conclusion of the test can not be absolute?The hypothesis test, when P refused to accept alpha Ho, H1 Ho, this is not completely true only existing sample information does not support the Ho; when the O> alpha, to Ho, but not that of Ho fully established. In a word, whether we reject Ho or not, we make mistakes. Therefore, the conclusion of statistics is probabilistic and can not be absolute.19 what is the difference between a completely randomized design and a randomized block design?A completely randomized design with randomized method to control the error variance, that by randomization, variation between subjects distributed in each processing level is random, each level was tested in the treated before is exactly the same, can be attributed to the differences between the experimental results do not affect the same treatment. A completely randomized design hypothesis can balance the differences between subjects by randomization, but in fact the experimental results often include individual differences, and when we can be eliminated, the results tend to be more accurate.The random block design uses the method of group grouping to separate the variance caused by irrelevant variables. The randomized block design should be as homogeneous as possible within the group, making the difference of experimental results better attributed to the influence of different treatments.IxJ 20 factorial design with different randomized design: IxJ is applied to pick up the object of two kinds of processing factors, which belongs to the multi factor experimental design, analysis of effect of different factors on test effect by variance, interaction exists; randomized design was applied to the subjects of a region and processing factors. Group factors are often subjects certain characteristics of the object itself, is to improve the balance between the two groups and the establishment of the non treatment factors, is the group condition, using variance analysis to compare the differences among groups onlyThe type of chi square test and its application conditions are discussedOne. Four chi square data chi square test: two rates or two constituent ratio comparison. 1) completely random design four lattice table, data chi square test 2) paired design, four grid table, data chi square testTwo. Row X list data chi square test: comparison of multiple rates or constituent ratios. 1) comparison of multiple sample rates 2) comparison of two groups or groups of constituent ratios 3) data association testsThree. Multiple comparisons of rates: ask for a difference between the overall rates, using 22 comparisons between ratesFour. Frequency distribution goodness of fit test: the sample content n is large enough, and the theoretical frequency is not less than 5; if the degree of freedom is =1, continuity correction should be doneFive. A linear trend test is used to analyze whether there is a linear trend between multiple percentages and hierarchical variables22, the difference between parameter test and nonparametric test, and what are their advantages and disadvantages?The parameter test is a method that assumes that the random sample comes from a known distribution of population and that two or more population parameters are the same. In practice, many data do not meet the requirements of parameter statistics, then the parameter test can not be used.The nonparametric test does not explicitly limit the parameters of the population distribution in the testing hypothesis, nor does it involve the overall distribution of the samples. There are some: a wide range of use, less restrictive conditions, robustness23. Briefly describe the applicability of nonparametric testsIt can be used not only for the comparison and analysis of hierarchical data, but also for exploratory studies withextreme skewness, small sample population variance, general distribution pattern unknown, and data analysis without precise representation. But if the data is suitable for parameter testing, the non parametric test is used to analyze the loss information24. The difference and relation of line correlation and rank correlation are briefly describedDifferences: different data requirements; different application purposes; different indicators; different calculations; different range of values; different units.Relation: 1 and two have the same theoretical basis, and obtain the parameter estimate according to the least square principle;2. The same coefficient data, the regression coefficient B and the correlation coefficient r, the sign is consistent.3. The regression coefficient B is equivalent to the hypothesis test of correlation coefficient R. 4, the correlation was explained by regression.25. What problems should we pay attention to in the linear regression analysis?LINE four condition26. Briefly describe the differences and relations between linear regression and linear correlationThe analysis can describe the strain Y changes with the change of the independent variable X and linear regression, but didnot describe the close degree of the relationship between the two. Correlation analysis, no independent variables and variables should be divided, it only study the extent and nature of any two variables, or a variable and multiple variables the correlation between the degree, can not use one or more variables to predict, control changes in another variable.27 what are the indicators that describe the trend of concentration? What are the differences in its scope of application?Mean: normal or approximately normal distributionGeometric mean: geometric series or lognormal distributionMedian: skewed distribution of data rooms; irregular distribution; uncertain data at one or both ends (opening data).28 what is hypothesis testing? Examples can be illustrated.Firstly, to test the hypothesis, then random sampling was carried out under this assumption, the two extreme cases and statistics probability calculated, if the probability is small, then reject the hypothesis, if the probability is not a small probability, accept the hypothesis that the process became a hypothesis test.29 please talk about the hypothesis test conclusion.With the assumption that the inspection concluded according tothe principle of small probability time of the actual test can not occur, so when rejecting the hypothesis testing may reverse the type I error, when the tested hypothesis is likely to make the type II error.30 how can we control or reduce the sampling gap in sampling studies?A reasonable sampling design increases the sample size31 what is the sampling error? Why is sampling error inevitable in sampling studies?The difference between the sample statistics and the sample statistics, the sample statistics and the population parameters caused by the sampling. Because individual differences are objective, the object of study is a part of the whole, so it is inevitable that the results of this part are different from the overall results32 can we say that the smaller the p value of the hypothesis test is, the greater the difference between the two overall indicators? Why?No, because the magnitude of the difference between the p value and the overall index is not exactly the same. In addition to the size and overall size differences about the p value, and the sampling error is related to the size of the overall difference, the same, the sampling error of different sizes, the P will be different, the size of the sampling error in the actual work is mainly reflected in the sample size.33 in the rank sum test, why do the same data be given to the average rank sum between the different groups, while the same data in the same group do not have to calculate the average rank?Such rank does not affect the calculation of the rank sum of the two groups, or does not bias the calculation of the rank sum of the two sets.。
