Feb 15, 2011
Understanding Statistics
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Understanding Statistics
STATISTICAL is a set of methods are discussed: (1) how to collect data that can provide optimal information, (2) how to summarize, process and present data, (3) how to conduct an analysis of the data set, so it arises from an analysis strategy -specific strategies, (4) how to draw conclusions and suggest that decisions should be taken, on the basis of the existing strategy, and (5) how to determine the risk of errors that might occur if we make a decision on the basis of the strategy.
STATISTICS, defined as a collection of facts that shaped figures arranged in the form of a list or table that illustrates a problem. Name statistics depend on the problems described by the statistic that, for example, population statistics, sales statistics, economic statistics and statistics education.
Statistical Classification
DESCRIPTIVE STATISTICAL (descriptive statistics), which discusses ways of collecting data, simplification of the figures obtained by observation (summarize and present), as well as measuring the concentration and distribution of data to obtain information that is more interesting, useful and easily understood. With descriptive statistics, collection of data obtained will be presented with a concise and neat and can provide the core information from existing data sets. The information can be obtained by descriptive statistics, among others, concentration for distributing data, and the tendency of a group of data. Included in the size of the convergence of data such as average, median, and mode. The size of the spread of such range, average deviation, variance, and standard deviation. In addition, the descriptive statistics is also included in the size of the location, such as quartiles, deciles and percentiles
STATISTICAL inference (inferential statistics), which discussed about how to analyze data and draw conclusions (related to parameter estimation and hypothesis testing). Method of statistical inference associated with the analysis of partial data to forecast or conclusion about the overall data. This method is often called inductive statistics because the conclusion drawn based on information from only partial data (samples). Statistical inference is divided into two groups, namely STATISTICAL STATISTICAL PARAMETRIC and nonparametric.
STATISTICAL PARAMETRIC (parametric statistics), the statistical inference that consider the value of one or more parameters of the population. Parametric statistics are usually associated with quantitative data (minimum scale measuring intervals). In addition, procedures for using parametric statistical analysis of data required to be normal distribution. Examples of parametric statistical analysis was the t test, analysis of Variety (ANOVA), Pearson correlation test and regression test (F test).
STATISTICAL nonparametric (nonparametric statistics) are part of statistical inference that does not pay attention to the value of one or more parameters of the population. Nonparametric statistical methods used to analyze data that can not be assumed normal distribution. Data needed more of a nominal or ordinal measurement scale (qualitative data). Examples of nonparametric statistical analysis: Chi Square Test for Freedom Two Variable Category, Spearman correlation coefficient, Wilcoxon rank sign test, Mann-Whitney test, Kruskal-Wallis, and Friedman Test.
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