Вивчаючи статистику

Statistic—A numerical measure that describes a characteristic of a sample.
Statistics—The branch of mathematics that consists of methods of processing and analyzing data to better support rational decision-making processes.
Sum of squares among groups (SSA)—The sum of the squared differences between the sample mean of each group and the mean of all the values, weighted by the sample size in each group.
Sum of squares total (SST)—Represents the sum of the squared differences between each individual value and the mean of all the values.
Sum of squares within groups (SSW)—Measures the difference between each value and the mean of its own group and sums the squares of these differences over all groups.
Summary table—A two-column table in which the names of the categories are listed in the first column, and the count, amount, or percentage of responses are listed in a second column.
Survey—A process that uses questionnaires or similar means to gather values for the responses from a set of participants.
Symmetry—Distribution in which each half of a distribution is a mirror image of the other half of the distribution.
t distribution—A distribution used to estimate the mean of a population and to test hypotheses about means.
Test statistic—The statistic used to determine whether to reject the null hypothesis.
Third quartile (Q3)—The value such that 75.0% of the observations are smaller and 25.0% are larger.
Time-series plot—A chart in which each point represents a response at a specific time. In a time series plot, the X-axis (the horizontal axis) always represents units of time, and the Y-axis (the vertical axis) always represents units of the numerical responses.
Two-way cross-classification table—A table that presents the count or percentage of joint responses to two categorical variables (a mutually exclusive pairing, or cross-classifying, of categories from each variable). The categories of one variable form the rows of the table, and the categories of the other variable form the columns.
Type I error—Occurs if the null hypothesis H0 is rejected when in fact it is true and should not be rejected. The probability of a type I error occurring is α.
Type II error—Occurs if the null hypothesis H1 is not rejected when in fact it is false and should be rejected. The probability of a type II error occurring is β.