STATISTICAL PROCESSING OF RESULTS OF TESTING OF STUDENTS

Provides statistical analysis of the results of testing of students involved in the experiment conducted by the Department of Higher Mathematics at the Samara State University of Railways, since the resulting test data (rates of assimilation of educational information), which are taken as random variables are a set of numbers, which is difficult to detect any pattern of change (variation). Built interval variation series calculated the most important numerical characteristics of random variable - the sample mean (arithmetic mean value of the flag of sample), the sample variance (the arithmetic average of the squared deviations of the observed values of the trait from their average values) and sample standard deviation was given the opportunity to build a histogram of relative frequencies - a stepped shape consisting of rectangles whose bases are partial intervals, and the height equal to the density of the relative frequency. The area of the histogram is equal to the sum of all the relative frequencies, ie, unit. Combining adjacent mid-upper side of the rectangle of the histogram line segments, we received a broken line, called line empirical density. By type of line empiric density statistic put forward the hypothesis of normal distribution of the random variable. To test this hypothesis using one of the criteria for approval - a specially selected random variable, exact or approximate distribution, is known. Pearson , which consists in comparing empirical and theoretical frequencies falls into the "region of acceptance of the hypothesis", is therefore considered a random variable subject to the normal distribution law. This makes it possible to determine its unknown parameters to estimate the unknown expectation (average rate of assimilation of educational information) found using a sample according to the sample average. Assessment of the probability that a random value within the range of learning, characterized by a deficiency in the assimilation of educational material, leads to the conclusion that about 30% of students will require more self-educational activity to achieve a satisfactory invariant form of self-competence. Selection of the normal distribution curve also allows you to build a scale of success of training, that is practiced standard estimates.

of self-competence, self-educational activity, the sample mean, expectation, sample variance, histogram of relative frequency distribution function, the hypothesis, the confidence interval, the scale of the success of training, the scale of percentiles, Z-scale point scale