Sample - A group of subjects selected from a larger group, the population.
Sample Statistic, Statistic - A measurement used to quantify a characteristic of the Sample. Even when the word sample is not used, the term statistic refers to the sample. Example: The sample mean is a measure of central tendency of the sample (see Population Parametric).
Sampling Error, Sampling Variability, Random Error - The estimation of the expected differences between the sample statistic and the population parameter.
Sampling Distribution - It is all possible values of a statistic and their probabilities of occurring for a sample of a particular size.
Scaling - expresses the centered observation in the units of the standard deviation of the observations.
Scatter Diagram, Scattergram, Scatter Plot - The pattern of points due to plotting two variables on a graph.
Significance - The degree to which a researcher’s finding is meaningful or important. (See statistical significance and practical significance.)
Significance Level - there are two types of significance levels, the observed significance level (alpha) and the chosen significance level (p-value). The lower the probability the greater the statistical significance, called alpha level.
Simple Correlation - Correlation between only two variables.
Simple Linear Correlation - Correlation that describes a linear relationship.
Simple Linear Regression - A form of regression analysis, which has only one independent variable.
Slope - The rate at which the line or curve rises or falls when covering a given horizontal distance.
Spearman Correlation Coefficient (rho), Rank-Difference Correlation, rs. - A statistical measure of the amount of monotonic relationship between two variables that are arranged in rank order.
Specification error (Error, Specification) - A mistake made when specifying which model to use in the regression analysis. A common specification error involves including a irrelevant variable and leaving out an important variable.
Standard
Deviation - A statistic that shows the square root of the squared distance
that the data points are from the mean.
for a sample
for a population
Standardized Measure of Scale - Any statistic that allows comparisons between things measured on different scales. Example: percent, standard deviations and z-scores
Standardized Regression Coefficient - Regression Coefficients which have been standardized in order to better make comparisons between the regression coefficients. This is particularly helpful when different independent variables have different units.
Standardized Regression Model - This is the regression model used after centering and scaling of the dependent variable and independent variables.
Standardized residuals - Standardized residuals are of the form (residual) / (square root of the Mean Square Error). Standardized residuals have variance 1. If the standardized residual is larger than 2, then it is usually considered large. (Minitab.)
where
Standard Error, Standard Error of the Regression, Standard Error of the Mean, Standard Error of the Estimate - In regression the standard error of the estimate is the standard deviation of the observed y-values about the predicted y-values. In general, the standard error is a measure of sampling error. Standard error refers to error in estimates resulting from random fluctuations in samples. The standard error is the standard deviation of the sampling distribution of a statistic. Typically the smaller the standard error, the better the sample statistic estimates of the population parameter. As N goes up, so does standard error.
Statistical Significance - Statistical significance does not necessarily mean that the result is clinically or practically important. For example, a clinical trial might result is a statistically significant finding (at the 5% level) that shows the difference in the average cholesterol rating for people taking drug A is lower than that of those taking drug B. However, drug A may only lower the cholesterol by 2 units more than drug B which is probably not a difference that is clinically important to the people taking the drug. Note: Large sample sizes can lead to results that are statistically significant that would otherwise be considered inconsequential.
Stepwise Regression - A method of regression analysis where independent variables are added and removed in order to find the best model. Stepwise regression combines the methods of backward elimination and forward selection. (See also Hierarchical Regression Analysis.)
Strength of Association, Strength of Effect Index - The degree of relationship between two (or more) variables. One example is R-squared, which measures the proportion of variability in a dependent variable explained by the independent variable(s).
Studentized Residuals: The studentized residual has the form of error/standard deviation of the error. Studentized residuals have constant variance when the model is appropraite.
Transformations - This is a method of changing all the values of a variable by using some mathematical operation.
Trend Line- This is a line representing a movement in one direction of the values of a variable over a period of time.
Unbiased Estimator - A sample statistic that is free from systemic bias.
Variance Inflation Factor (VIF) - A statistics used to measuring the possible collinearity of the explanatory variables.
Weighted Least Squares - A method of regression used to take into account the non constant variance. The variables are multiples by a particular number (weights). It is typical to choose weights that are the inverse of the pure error variance in the response. (Minitab, page 2-7.) This choice gives large variances relatively small weights and visa versa.
Y-intercept - The point where a regressin line intersects the y axis.
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