PHA 6935 Applied Statistics for Data Analysis
Applied Statistics for Data Analysis provides students with the basic knowledge of how scientific evidence is classified and how statistical procedures are utilized to analyze data. This encompasses common descriptive measures such as the mean, standard deviation, and uncertainty of measurements as well as comparative statistics that aim to compare two or more population means or perform simple linear regression. These basic statistical operations are important for everyday tasks conducted in an analytical laboratory. No matter which field the student will be working in, a basic understanding of the scientific method and the statistics used to analyze data is essential to job performance.
Please review our recommended course order.
|Module 1||Introduction to Scientific Evidence and Statistics|
|Module 2||Measures of central tendency and the normal distribution|
|Module 4||Discrete random variables and probability distributions|
|Module 5||Estimation of mean and standard deviation and the normal distribution|
|Module 6||Hypothesis testing for one or two population means, Student t-test|
|Module 7||Hypothesis testing for small sample sizes and multinomial experiments, Fisher’s exact test|
|Module 8||Analysis of Variance and multiple comparison tests|
|Module 9||Simple linear regression|
Author: Prem S. Mann
Publisher: Wiley; 9th edition (28/03/2017)
Any of the earlier editions (6th, 7th or 8th) will also be acceptable. Some editions are accompanied by a Student Solutions Manual, which is useful for practicing the exercises in the book, but it is not necessary. The text book should be used to support the theoretical understanding and provision of examples beyond those provided in the modules of this course. In addition, some editions have an accompanying Student Study Guide, but purchase is not necessary.
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