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Fundamentals of Probablity and Statistical Analysis
Source University of Derby
Versions Available Windows
Summary
This package introduces students to statistics in health care including the following topics: Summary statistics, probability, sampling distributions, confidence intervals and hypothesis testing
Contents
Research Interests Variability And Probability Hand In Hand
The Standard Normal Distribution
Sampling Distributions
The Application Of Sampling Distributions
Estimation
Application Of Confidence Intervals
Hypothesis Testing
Summary of Sections
Research Interests:
Through the introduction of a fictitious research problem, different ways of manipulating and describing population data are explored and the concept of probability is introduced.
Variability And Probability Hand In Hand :
The variability of data is considered in greater depth and the link between probability and data variability is discussed. This lesson covers techniques used to standardise data variability to enable different data sets to be compared. The concept of the normal distribution is introduced.
The Standard Normal Distribution:
Students get the opportunity to use the standard normal distribution to make probability statements about population data. Developing a certain familiarity with z values is good preparation for Lesson 4 in which probability statements are refined to take account of sampling.
Sampling Distributions:
The process of sampling data is taken a stage further in defining what is meant by a 'sampling distribution'. The computer simulates a sampling process using our original data set from Lesson 1. This and other learning methods are employed to demonstrate some important mathematical features of sampling distributions
The Application Of Sampling Distributions :
In this lesson students have the opportunity to practise the concepts learned in lesson 4 by doing exercises relating to different sampling distributions. Answers are provided with graphical explanations to help students to develop confidence.
Estimation :
The preparation in lessons 1 to 5 paves the way to calculating confidence intervals. These are simple to do and are an effective way of performing statistical inference.
Application Of Confidence Intervals :
Principles learned in Lesson 6 are refined further. This is achieved by completing further exercises that illustrate the way inferences are made with different sampling distributions.
Hypothesis Testing :
By the time students have completed this lesson they should fully understand how a hypothesis test works. By breaking down the statistical decision process into separate steps, a potentially complicated process is greatly simplified.
System Requirements

This package runs under Windows 3.1 and Windows 95. It will run on a stand-alone workstation, or across a network. The minimum requirement is an IBM compatible PC 80486 computer with 4Mb of RAM, and a VGA monitor capable of 256 colours in 640 x 480 resolution.