Email answers to me Assignment*Which commandments did Sepkoski (1984) break, do you think his inferences hold (if so, to what extent)? R assignment(s)

Slides:



Advertisements
Similar presentations
Chapter 6 Sampling and Sampling Distributions
Advertisements

Confidence Intervals Chapter 10. Rate your confidence Name my age within 10 years? 0 within 5 years? 0 within 1 year? 0 Shooting a basketball.
Confidence Intervals for Proportions
1 Statistical Inference H Plan: –Discuss statistical methods in simulations –Define concepts and terminology –Traditional approaches: u Hypothesis testing.
QUANTITATIVE DATA ANALYSIS
Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.
Class 5: Thurs., Sep. 23 Example of using regression to make predictions and understand the likely errors in the predictions: salaries of teachers and.
Chapter 7 Sampling and Sampling Distributions
Sampling Distributions
Chap 9-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 9 Estimation: Additional Topics Statistics for Business and Economics.
PSY 1950 Confidence and Power December, Requisite Quote “The picturing of data allows us to be sensitive not only to the multiple hypotheses that.
OMS 201 Review. Range The range of a data set is the difference between the largest and smallest data values. It is the simplest measure of dispersion.
Part III: Inference Topic 6 Sampling and Sampling Distributions
Chapter 11: Inference for Distributions
CHAPTER 8 Estimating with Confidence
Chapter 12 Inferring from the Data. Inferring from Data Estimation and Significance testing.
Principles of the Global Positioning System Lecture 10 Prof. Thomas Herring Room A;
Standard error of estimate & Confidence interval.
Copyright © 2012 Pearson Education. All rights reserved Copyright © 2012 Pearson Education. All rights reserved. Chapter 10 Sampling Distributions.
Meet the Kiwis…. Population of kiwis… Codes… Species Region GS-Great Spotted, NIBr-NorthIsland Brown, Tok-Southern Tokoeka NWN-North West Nelson, CW-Central.
Sampling. Concerns 1)Representativeness of the Sample: Does the sample accurately portray the population from which it is drawn 2)Time and Change: Was.
Foundations of Sociological Inquiry The Logic of Sampling.
CHAPTER 8 Estimating with Confidence
Statistical inference. Distribution of the sample mean Take a random sample of n independent observations from a population. Calculate the mean of these.
Review of Chapters 1- 5 We review some important themes from the first 5 chapters 1.Introduction Statistics- Set of methods for collecting/analyzing data.
Continuous Probability Distributions Continuous random variable –Values from interval of numbers –Absence of gaps Continuous probability distribution –Distribution.
Standard Error and Confidence Intervals Martin Bland Professor of Health Statistics University of York
I.Intro to Statistics II.Various Variables. I.Intro to Statistics A. Definitions -
Biostatistics Class 1 1/25/2000 Introduction Descriptive Statistics.
From the Data at Hand to the World at Large
Chapter 7 The Logic Of Sampling. Observation and Sampling Polls and other forms of social research rest on observations. The task of researchers is.
Questions from reading material What are Houle et al’s 10 “commandments” and why are they important? How would you help McKinney (1997) improve on Figure.
Fundamentals of Data Analysis Lecture 3 Basics of statistics.
1 Chapter 6 Estimates and Sample Sizes 6-1 Estimating a Population Mean: Large Samples / σ Known 6-2 Estimating a Population Mean: Small Samples / σ Unknown.
Stat 112: Notes 2 Today’s class: Section 3.3. –Full description of simple linear regression model. –Checking the assumptions of the simple linear regression.
Some dates check out “outline version 3.0.pdf” Return reviews to reviewees (use track changes and “comments) by 20 Sep – send also to Lee Hsiang Revised.
ANOVA Assumptions 1.Normality (sampling distribution of the mean) 2.Homogeneity of Variance 3.Independence of Observations - reason for random assignment.
Lecture V Probability theory. Lecture questions Classical definition of probability Frequency probability Discrete variable and probability distribution.
Statistics : Statistical Inference Krishna.V.Palem Kenneth and Audrey Kennedy Professor of Computing Department of Computer Science, Rice University 1.
Statistics: Unlocking the Power of Data Lock 5 Exam 2 Review STAT 101 Dr. Kari Lock Morgan 11/13/12 Review of Chapters 5-9.
Sampling distributions rule of thumb…. Some important points about sample distributions… If we obtain a sample that meets the rules of thumb, then…
1 Summarizing Performance Data Confidence Intervals Important Easy to Difficult Warning: some mathematical content.
: An alternative representation of level of significance. - normal distribution applies. - α level of significance (e.g. 5% in two tails) determines the.
Statistical analysis Why?? (besides making your life difficult …)  Scientists must collect data AND analyze it  Does your data support your hypothesis?
ANOVA, Regression and Multiple Regression March
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 8: Estimating with Confidence Section 8.1 Confidence Intervals: The.
BASIC STATISTICAL CONCEPTS Statistical Moments & Probability Density Functions Ocean is not “stationary” “Stationary” - statistical properties remain constant.
Chapter 13 Understanding research results: statistical inference.
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Analysis of (cDNA) Microarray.
Descriptive Statistics Used in Biology. It is rarely practical for scientists to measure every event or individual in a population. Instead, they typically.
Estimating a Population Proportion ADM 2304 – Winter 2012 ©Tony Quon.
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 8: Estimating with Confidence Section 8.1 Confidence Intervals: The.
Fundamentals of Data Analysis Lecture 3 Basics of statistics.
Critical Appraisal Course for Emergency Medicine Trainees Module 2 Statistics.
Data Analysis Module: One Way Analysis of Variance (ANOVA)
Inference: Conclusion with Confidence
Estimation of Gene-Specific Variance
Statistical tests for quantitative variables
Inference for the Difference Between Two Means
POSC 202A: Lecture Lecture: Substantive Significance, Relationship between Variables 1.
Descriptive and inferential statistics. Confidence interval
Chapter 8: Estimating with Confidence
Confidence Intervals Chapter 10 Section 1.
Section 7.7 Introduction to Inference
Statistics.
Statistical Inference
CS639: Data Management for Data Science
Introductory Statistics
STAT 515 Statistical Methods I Chapter 1 Data Types and Data Collection Brian Habing Department of Statistics University of South Carolina Redistribution.
Presentation transcript:

answers to me Assignment*Which commandments did Sepkoski (1984) break, do you think his inferences hold (if so, to what extent)? R assignment(s) Keep working on it

Extinction: When did a taxon become extinct? Statistical Paleobiology Remote lecture 9 Sep 2013 Oslo Helsinki

Marshall 1990 (actually “anglicizing Sadler and Strauss older papers): Assuming random preservation/sampling

Marshall 1990 Assuming random preservation/sampling Stratigraphic range is AWALYS shorter than TRUE duration (barring reworking)

Testing assumptions Is fossilization random? (is sampling stochastically constant?) Are fossilization events independent? (multiple records taken as one) *Continuous sampling R Marshall 1990 Assuming random preservation/sampling

Solow, A. R. (2003). "Estimation of stratigraphic ranges when fossil finds are not randomly distributed." Paleobiology 29(2): (Based on Robson and Whitlock 1964) U = point estimate for extinction time L = point estimate for “speciation” or migration X = vector for data of times of occurrence where X1 is the oldest and Xn is the youngest.

Non-random preservation/sampling Marshall 1994 Paleoiology Median = 4.5

Any gap has a 50% chance of being larger than the median The chance for all gaps to be larger than the median of the underlying distribution = That also means that the probability that the median gap lies within the range of those sampled is = Catch: CI’s have own uncertainties Marshall 1994 Paleoiology Assumes gap duration distribution free

Marshall 1994 Paleoiology Non-random preservation/sampling Confidence levels For N = 6 and for the statement, that a gap has a 50 % chance of being greater or smaller than the median, we have a 0.95 probability that the next gap is as small as the first smallest gap and or as large as the 6 th largest gap.

Cheetham, A. H. (1986). "Tempo of Evolution in a Neogene Bryozoan: Rates of Morphologic Change Within and Across Species Boundaries." Paleobiology 12(2):

Marshall 1994 Paleoiology

Reasons for non randomness Sequence stratigraphic architectures Variation in paleo-environment Variation in quality of outcrop Taphonomic regimes Collecting practices Ocean circulation Biotic interactions (many more reasons for global non-randomness)

Marshall, C. R. (1997). "Confidence intervals on stratigraphic ranges with nonrandom distributions of fossil horizons." Paleobiology 23(2):

Summary of single taxon extinction time estimation covered Assume uniform random sampling (Strauss and Sadler 1986, Marshall 1990) Distribution free gaps (Marshall 1994) Non-random distribution of fossil finds (Solow 2003) When the fossil recovery potential is known (Marshall 1997) If a paper doesn’t talk about assumptions, think about the implicit ones violating assumptions vs not measuring uncertainty at all

References READING: Marshall 2010 in Quantitative Paleobiology short course Strauss, D. and P. M. Sadler (1989). "Classical Confidence-Intervals and Bayesian Probability Estimates for Ends of Local Taxon Ranges." Mathematical Geology 21(4): Marshall, C. R. (1990). "Confidence-intervals on stratigraphic ranges." Paleobiology 16(1): Marshall, C. R. (1994). "Confidence-intervals on stratigraphic ranges - partial relaxation of the assumption of randomly distributed fossil horizons." Paleobiology 20(4): Marshall, C. R. (1997). "Confidence intervals on stratigraphic ranges with nonrandom distributions of fossil horizons." Paleobiology 23(2): Weiss, R. E. and C. R. Marshall (1999). "The uncertainty in the true end point of a fossil's stratigraphic range when stratigraphic sections are sampled discretely." Mathematical Geology 31(4): Solow, A. R. (2003). "Estimation of stratigraphic ranges when fossil finds are not randomly distributed." Paleobiology 29(2): Bradshaw, C. J. A., et al. (2012). "Robust estimates of extinction time in the geological record." Quaternary Science Reviews 33:

Assignment Download sampled occurrence data for a taxon of your interest from the PBDB (can be species within a genus or genera within a family) (at least 7 temporal data points) Write a short description of the taxon Using the data you downloaded, write an R script (annotated) to organize the data and to estimate the range end points using the methods presented in Marshall 1990 and Solow Write a summary of your observations What assumptions must you make and are these assumptions likely to have been violated? What are the consequences of the violations? Should you use the method given that assumptions have been violated or would you rather just report raw or mean values?

Optional Assignments Marshall 1990 is based on continuous fossilization. Simulate both a continuous fossilization process and a discrete fossilization process and explore how much of an issue it is to violate the assumption that fossilization is continuous, in R. Solow 2003 seems like a dream, so simple and elegant. Simulate a few probable fossilization processes and apply Solow 2003 to them to check out how reliable the approach is, in R.