PPT-Normality, P-values,

Into to Comparing 2 Groups Analysis of Biological DataBiometrics Dr Ryan McEwan Department of Biology University of Dayton ryanmcewanudaytonedu First Principle As a scientist investigator or data handler it is your personal responsibility to make sure that the analysis you are doing is appropriate. Explaining the Normal Distribution. Preliminaries: How to describe data. Discussion Question: How do we describe data in Statistics?. In this course, the way we describe data is by using the C.U.S.S. Method. We look at these characteristics:. U.S. Coal Mine Production – 2011. Source: www.eia.gov. Data Description. Coal Mine Production and Labor Effort for all Mines Producing Over 100,000 short tons of Coal in 2011. Units: Mine (n = 691). Key concepts. Testing conditions of applications in complex study design. Residuals. Tests of normality. Residuals plots. Residuals vs. fitted. QQ plots. Cook’s distance. Conditions of applications. Validating and Preparing your data. Lyytinen & Gaskin . Data Screening. Data screening . (also known for us as “data screaming”) ensures . your data is . “clean” . and ready to go before you conduct . “Essentially. , all models are wrong, but some are . useful”. George E.P. Box. Your model . has to be. wrong…. … but that’s o.k.. if it’s illuminating!. Linear Model. Assumptions. Absence of. Analysis of Biological Data. Ryan McEwan and Julia Chapman. Department of Biology. University of Dayton. ryan.mcewan@udayton.edu. First Principle:. As a scientist, investigator or data handler, it is your personal responsibility to make sure that the analysis you are doing is appropriate.. Use of nacelle lidar data . to explore impact of . non-linear averaging. chris.slinger@zephirlidar.com. +44 1531 650 757. 02 September 2014. Background.. . From the April 2014 PCWG meeting:. “Existing correction methods (. Normal Probability Plots: Look for the observations to fall reasonably close to the green line. Strong deviations from the line indicate non-normality. Expected Cummulative Prob Expected Cummulative Examine the concepts of normality . and abnormality. What is normal . behaviour. ?. In pairs or small groups discuss examples of . behaviour. that is normal, and . behaviour. that is abnormal.. Examine the concepts of normality . F-test in ANOVA is the so-called . omnibus test. . It tests the means globally. It says nothing about which particular means are different.. post hoc tests. , . multiple comparison tests. .. Tukey . Honestly Significant . Are My Data Normal?. @UWE_JT9. @. dave_lush. Scientific . Practice. Do My Data . Fit?. Statistics relies on having expectations about how experimental data are distributed. if we know how we expect data to behave, we can draw inferences if we observe its behaviour change. Data Analysis & Computers II. Slide . 1. Assumption of normality. Assumption of normality. Transformations. Assumption of normality script. Practice problems. SW388R7. Data Analysis & Computers II. My Email. awolters@scopus.vic.edu.au. . DISCLAIMER:. I do not represent . vcaa. . This is just my interpretation of the new study design requirements.. Activities to Demonstrate. Any . time I do an activity, big or small, I will. Presented by. Kelly Benson. Mouiad Al-Wahah. 1. Definitions we agreed upon... Def 1. . Diagnosis is the problem of trying to find what is wrong with some system based on knowledge about the design/structure of the system, possible malfunctions that can occur in the system and observations made of the behavior of the system..