Ads
related to: grade 12 analysis and interpretation of statistics module 4 unit test
Search results
Results From The WOW.Com Content Network
[12] [17] The readers use a pre-made rubric to assess the answers and normally grade only one question in a given exam. Each question is graded on a scale from 0 to 4, with a 4 representing the most complete response. Communication and clarity in the answers receive a lot of emphasis in the grading. [12]
The data type is a fundamental concept in statistics and controls what sorts of probability distributions can logically be used to describe the variable, the permissible operations on the variable, the type of regression analysis used to predict the variable, etc.
A typical "Business Statistics" course is intended for business majors, and covers [71] descriptive statistics (collection, description, analysis, and summary of data), probability (typically the binomial and normal distributions), test of hypotheses and confidence intervals, linear regression, and correlation; (follow-on) courses may include ...
A simple unit is one which represents a single condition without any qualification. A composite unit is one which is formed by adding a qualification word or phrase to a simple unit. For example, labour-hours and passenger-kilometer. Unit of analysis and interpretation: units in terms of which statistical data are analyzed and interpreted.
The test statistic was a simple count of the number of successes in selecting the 4 cups. The critical region was the single case of 4 successes of 4 possible based on a conventional probability criterion (< 5%). A pattern of 4 successes corresponds to 1 out of 70 possible combinations (p≈ 1.4%).
Statistics is a field of inquiry that studies the collection, analysis, interpretation, and presentation of data. It is applicable to a wide variety of academic disciplines , from the physical and social sciences to the humanities ; it is also used and misused for making informed decisions in all areas of business and government .
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x).
Statistics subsequently branched out into various directions, including decision theory, Bayesian statistics, exploratory data analysis, robust statistics, and non-parametric statistics. Neyman-Pearson hypothesis testing made significant contributions to decision theory, which is widely employed, particularly in statistical quality control.