This work has a main focus on the six
|
|
|
- Tabitha James
- 9 years ago
- Views:
Transcription
1 Research Article Herpetological Bulletin (2013) 123: 1-7 Separating brown and water frogs to group & species on snout features CHARLES A. SNELL 27 Clock House Road, Beckenham, Kent BR3 4JS ABSTRACT - Identification guidance in the herpetological literature for distinguishing between the northwest European brown frog species and brown frog and water frog groups (Rana and Pelophylax respectively) often describes the degree of snout pointedness as a diagnostic feature. This method is evaluated here and is found to have no value. On the other hand, a novel method, using the markings on the snout, has value in separating the north-west European brown frogs and in separating the two groups. Consideration is given to the possibility that earlier descriptions of species having more pointed heads than others is due to variation in the narrowness of the angles of head markings causing an illusion of greater head pointedness. INTRODUCTION This work has a main focus on the six indigenous northwest European frog species, comprising three brown frogs, the Common Frog Rana temporaria, the Moor Frog Rana arvalis and the Agile Frog Rana dalmatina, and three water frogs, comprising two species, the Pool Frog Pelophylax lessonae (formerly Rana lessonae), and the Marsh Frog Pelophylax ridibundus (formerly Rana ridibunda) and their hybrid, the Edible Frog Pelophylax kl. esculentus (formerly Rana kl. esculenta). Water frogs are often described as having a more pointed snout than brown frogs (e.g. Wycherley, 2003; Natural England, 2008; Arkive-Images of Life on Earth, 2011). In Fig. 1 (from Arnold & Ovenden, 2002) the water frog is depicted with a more pointed snout than the brown frog and Inns (2009) describes the Common Frog as having a blunter snout than water frogs. Additionally, within the brown frogs, Common Frogs are often described as having a less pointed snout than the Moor Frog (e.g. Haltenorth, 1979; Chihar & Cepika, 1979; Hofer, 1985; Laňka & Vít, 1989; Nöllert & Nöllert, 1992) or usually having a less pointed snout (Fog et al., 1997), Arnold and Ovenden (2002). The ubiquity and longevity of this distinction is highlighted by the fact that in Figure 1. Depiction of the water frog snout (left) compared to brown frog (right). From Arnold & Ovenden, Scandinavia the Moor Frog is called the Pointed Nosed Frog (spissnutefrosk (Norwegian), spidssnudet frø (Danish)) whereas the Common Frog is known as the Blunt-nosed Frog (buttsnutefrosk (Nor.), butsnudet frø (Dan)). The usefulness of using these characteristics for distinguishing between species or groups is evaluated here. It was noticed in the course of the work on snout form, eye stripes and eye shape, that the dark facial stripes that pass across each nostril and join to the anterior edge of each eye, had quite varying angles; the utility of these are also investigated British Herpetological Society
2 C. Snell METHODS AND MATERIALS Samples available for comparison Species and sample numbers of north-west European frogs used for the investigation were as follows: water frogs, 27 (of which Pool Frogs, 15, Edible Frogs 5, Marsh Frogs 7), Common Frogs 33, Moor Frogs 19 and Agile Frogs 14. Measuring snout pointedness To quantify snout pointedness, electronically cut-out photographs of the heads (photographed vertically from above) of a range of water and brown frog individuals were made (Fig. 2); no conscious bias was made in the selection. These were transformed into black silhouettes (water frogs) or white silhouettes (brown frogs) and then overlain in pairs (example shown in Fig. 2B) to check for consistent variation in snout form (approximately the upper one third of the Figure 2. Electronic cut-out photographs of the heads of a range of water and brown frogs. image) between the brown and water frogs. A less time-consuming method was adopted partway through the investigation, where a line was traced - using graphical software (MS PhotoDraw) - around the head region starting and terminating at the points where the arms met the body (Fig. 2C). These tracings overlain in permutated pairs were then individually ordered in terms of snout pointedness. In both methods the lower two thirds of the outlines were aligned and sized (conserving proportions) to match each other as closely as possible; a method which also removed differences brought about by the size of the individual. Facial markings and angles The possibility of separating the groups - or even species - by differences in facial markings, proportions or angles was investigated by creating a series of computer generated tracings taken from enlarged photographs. These traced the eyes as well as the dark stripes which, starting near the snout tip, pass through the nostrils and stop at the anterior part of the eye. Fig. 3 gives an example of these and the position of tracing is shown at B, where an outline of the head is included for clarity. The eyes are the sub-semicircular shapes in the lower part of each traced line. The angles between the two stripes - estimated by best fit (see grey dotted lines in R.a., Fig. 3) - were recorded for these and the other specimens in the samples: the turns in the facial markings anterior to the nostrils (e.g. in R.a and R.d) and the more pronounced turns closer to the eyes (e.g. see arrows in R.a, R.a. and R.d. specimens, Fig. 3) were excluded from these best fit considerations. Clearly, the greater the proportion of width compared to length, the greater the amount of eye visible from above and the more upwardly focused the frog eyes are (q.v. Snell, 2011). The right eye of Pr shows the approximate placing of eye measurements (L = length, W = width) later used to estimate the amount of eye visible from above. The dotted lines in the R.d. and P.l. specimens were aligned to the dorsal edge of the eye and illustrate a difference between water and brown frogs in these alignments; they were not used for the angle measurements. 2 Herpetological Bulletin 123 (2013)
3 Separating brown and water frogs silhouettes or traced outlines showed substantive differences between water and brown frogs. Snout pointedness brown frog results Of the brown frogs (N = 66), the three most pointed head outlines (4.5%) belonged to Moor Frogs; the five least pointed (7.58%) were Common Frogs. The remainder (87.9%) had no substantive differences. Figure 3. Computer generated tracings and estimated angles (dotted grey lines). Statistics Measurements of facial stripe angles and eye proportions were subject to ANOVA tests; these were followed by 2-sample t-tests to pinpoint where variation lay. The t-test results were subject to sequential Bonferroni testing (Holm, 1979); this placed more stringency on the rejection of the null hypothesis. RESULTS Snout pointedness - brown frogs and water frogs Fig. 4 shows a representative sample of the possible combinations produced from the black silhouettes (water frogs) overlain with the white silhouettes (brown frogs). In total 27 water frogs and 66 brown frogs were used. The result, as with the sample in Fig. 4, showed little difference in pointedness. An observer in the field would be ill-advised to base identification on snout pointedness. The top one third (the snout area) of silhouette pair B (Fig. 4) shows that this Marsh Frog has a slightly more pointed snout than the Common Frog. None of the other Intraspecific differences Intraspecific differences were also negligible when photographs which seemed to show large variation in pointedness (e.g. Pr1 and Pr2 in Fig. 2) were superimposed (Fig. 4 (I)). However, there were occasional measurable intraspecific differences in the distance between the anterior eye edge and the tip of the snout (e.g. P.r.1 was 1.4 times longer than P.r.2). A similarly large variation was found in the Common Frogs. This variation did not seem to have any substantive effect as the head region outlines were clearly more related to the underlying lateral head bone structure rather than relative eye position. Differences in eye area and facial marking angles Average eye width / eye length ratios results (Table 1B) show the brown frogs were more Figure 4. representative sample of the possible combinations produced from the black silhouettes (water frogs) overlain with the white silhouettes (brown frogs) Herpetological Bulletin 123 (2013) 3
4 C. Snell Figure 5. A scatter plot depicting eye ratios versus facial stripe angles in brown and water frogs. laterally focused compared to the frogs; the averaged ratios were 0.33 and 0.48, respectively. Table 1B and Fig. 3 show that, viewed from above, this is particularly the case in the Moor and Common Frogs (averaging 0.3 and 0.27 respectively) which show very noticeably smaller areas of the eye than the water frogs (average 0.48). The Agile Frog average eye ratios (0.4) on the other hand were much closer to those of the water frogs, a fact emphasised in a scatter plot (eye ratios v. facial stripe angles, Fig. 5) where ca. 43% of the Agile Frog plots abut or inter-penetrate the water frog plots. Both Moor Frogs and Agile Frogs showed an indentation in the facial lines (arrowed, Fig. 3), which was absent from water frogs and weak in the Common Frog (positions arrowed). A line drawn and aligned to the dorsal eye edge (e.g. the dotted lines in R.d. and P.l. in Fig. 3) also emphasised the indentations and straighter route taken (from the anterior of the eye to the nostril) by the snout markings in water frogs and, to a lesser degree, the Common Frog compared to the other two species. The angles and form of the lines across the snout (Fig. 3) varied enough between the groups and species to be a potential diagnostic feature. Statistics Sample statistics, ANOVA and two sample t-tests results are given in Table 1. Table 1A indicates that the Common Frog had an average angle between the markings of (n = 33, SD 6.8), the Moor Frog angles averaged 42.6 (n = 19, SD 4.67), the Agile Frog average was (n = 14, SD 5.93) and the water frogs averaged 49.3 (n = 27, SD 6.7). ANOVA results (Table 1A) gave high significance values for differences between the species/groups (p < 0.001). ANOVA tests do not indicate where this variability lies (i.e. between which species). To remedy this, a series of two sample t-tests were carried out; the results are shown in Table 1A. This table shows that all sample comparisons yielded highly significant differences in eye stripe angle parameters between the species except in the comparison between R. dalmatina and R. arvalis (p = 0.35) and this remained the only case of Ho acceptance after applying Holm s sequential Bonferroni post-hoc testing. The ratio of eye width divided by eye length (EW/EL) obtained from the eye outline tracings described earlier (Fig. 3), used one eye for each specimen. The resulting ratios (Table 1B) were: 4 Herpetological Bulletin 123 (2013)
5 Separating brown and water frogs A Facial stripe angle: Sample Statistics Groups RD RT RA WF N Average (SD, 5.93) (SD, 6.83) 42.6 (SD, 4.67) 49.3 (SD, 6.7) Anova df (between groups) df (within groups) f p < Two-Sample t-tests: Facial stripe angles Pair Species df t Significance 1 Water frogs v. R. temp p < R. arvalis v. R. temp p < Water frogs v. R. arv p < R. dal. v. water frogs p < R. dal. v. R. temp p < R. dal. v. R. arvalis p = 0.35 B Eye Length / Eye Width: Sample Statistics Groups RD RT RA WF N Average 0.4 (SD, 0.055) 0.27 (SD, 0.05) 0.3 (SD, 0.043) 0.48 (SD, 0.056) Anova df (between groups) df (within groups) f p p < Sample t-tests: Eye short axis length / long axis length Pair Species df t Significance 1 Water frogs v. R. temp p < R. Arvalis v. R. temp p = Water frogs v. R. arv p < R. Dal. v. water frogs p < R. Dal. v. R. temp p < R. Dal. v. R. Arvalis p < Table 1A. Facial stripe: sample statistics, ANOVA and t-test results. Table 1B. Eye ratio (short/long axis): sample statistics, ANOVA and t-test results. Note the highly significant p values in all tests except pair 6 in 1A and pair 2 in 1B (boxed). all brown frogs 0.32, all water frogs 0.48, Common Frog 0.27, Moor Frog 0.3, and Agile Frog 0.4. This equates to the water frog samples here having 1.5 times as much eye area visible from above (WF/BF = 0.48/0.32) compared to brown frog samples. An ANOVA test (Table 1B) indicated very significant differences between the populations (p = < 0.001). Corresponding t-tests (Table 1B) indicated that all groupings had highly significant differences except in the case of R. arvalis and R. temporaria where the Ho was accepted; this remained the only case after the application of the sequential Bonferroni test. Snout angle and eye ratio results are plotted in Fig. 5. Herpetological Bulletin 123 (2013) 5
6 C. Snell DISCUSSION Snout pointedness showed no value in group separation: advice that water frogs have a more pointed snout than brown frogs was inaccurate. Based on the samples available here, separation of Common and Moor Frogs using snout pointedness was also unsafe. There was a small tendency for the sharpest snouts to belong to Moor Frogs and the bluntest to Common Frogs, but these differences would be very hard to notice in the field. The majority of head outlines turned out to be indistinguishable between the species. It has been the author s experience that younger frogs (particularly in Common and water frogs) can have relatively longer snouts (if defined as the distance from the anterior eye edge to snout tip) compared to adults, which tend with age to increasing apparent bluntness. This phenomenon has also been reported for Common Frog (Arnold & Ovenden, 2002). Therefore, the Marsh Frog labelled P.r.2 in Fig. 2 could simply be older than the individual labelled P.r.1. If one species had a longer lifespan than another, that species may, on average, appear to have blunter head profiles. Any underlying trend for changes with age weakens the usefulness of this characteristic. The bluntening effect seen in P.r.1 and P.r.2 (Fig. 2) is predominantly associated with changes in the relative distance from the nostril to the anterior edge of the eye. Applying a method not previously described, the facial stripes had patterns and angles which did discriminate between the two groups and between some brown frog species. The V formed by the two snout markings was more pointed in water frogs compared to Common Frogs. While evidence of differences in snout pointedness was not found, the Moor Frog facial markings converged at significantly smaller angles than those of the Common Frog and, possibly tellingly, the Moor Frog is also described as having a more pointed snout than the Common Frog (Matz & Weber, 1983; Laňka & Vít,1989; Fog et al., 1997, Arnold & Ovenden, 2002). References to a more pointed snout could be an illusion based on the greater pointedness of the facial markings in water or Moor Frogs compared to the Common Frog, rather than the physical structure of the snout itself. Compared to Moor and Common Frogs, water frog eyes (viewed from above) showed relatively more area. Water and Common Frog snout stripes show greater straightness than either the Moor or Agile Frogs (Fig. 3). Hence facial detail varied widely between species and the groups. These samples suggested that Agile Frogs also have more upwardly looking eyes than the other two brown frog species. The author has experienced, both in the wild and in outdoor enclosures, that water frogs are more likely to leap up and intercept flying insects than the Common or Moor Frogs. Having more upwardly focused eyes would benefit this mode of feeding. This suggests future research. Where the Agile Frog shares habitat with other brown frog species, it might similarly benefit from its long legs and more upwardly focussed eyes to feed in a similar way; i.e. is there a degree of habitat and trophic partitioning where the ranges overlap? The results have suggested that differential eye ratios and facial stripe angles offer value in the identification of north-west European frog groups and species. The methods given here might also aid identification from images; it is the author s experience that agencies supplying images for publication frequently misname species. Published guidance on discrimination between these species suggest using metatarsal tubercle (MT) form (not usually visible in photographs) (e.g. Arnold & Ovenden, 2002), or temporal mask form to separate Moor and Common Frogs (Fog et al., 1997) and leg length to separate Agile from other (shorter legged) brown frog species and to help separate water frog forms (e.g. Nöllert & Nöllert, 1992; Fog et al., 1997; Arnold & Ovenden, 2002). These parameters, as with some parameters given here, overlap to some degree and some methods described in the literature (e.g. the absence of a temporal mask in water frogs) are inaccurate (Snell, 2011). Where ambiguity exists, it is desirable to have recourse to extra distinguishing methods which might provide more unequivocal separations. Detail on frogs heads, while not easily useable in the field (but likewise leg length and MT), are potentially more useful than flawed guidance suggesting the use of snout pointedness or the presence of a temporal mask. 6 Herpetological Bulletin 123 (2013)
7 Separating brown and water frogs REFERENCES Arkive.org; Arnold, E. N. & Ovenden, N. (2002). A Field Guide to the Reptiles and Amphibians of Britain and Europe. London: Collins. Chihar, J. & Cepika, A. (1979). A Colour Guide to Familiar Amphibians and Reptiles. London: Octopus Books. Fog, K., Smedes, A. & de Lasson, D. (1997). Nordens Padder og Krybdyr. Gottlieb Ernst Clausen Gad, Copenhagen. Haltenorth, T. (1979). British and European Mammals, Amphibians and Reptiles. Chatto and Windus, London. Hofer, R. (1985). Amphibien-Reptilien, Kompass Books, Gräfe & Unzer GmbH, München. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (2): Inns, H. (2009). Britain s Reptiles and Amphibians. UK: Wildguides Ltd. Laňka, V. & Vít, Z. (1989). Amphibians and Reptiles. Artia, Prague: Octopus Books. Matz, G. and Weber, D. (1983). Guide des Amphibiens et Reptiles d Europe. Paris: Delachaux & Niestlé. Natural England, document WML-G10 (2008). Pool Frog: European Protected Species. wmlg10_tcm pdf Nöllert, A. & Nöllert, C. (1992). Die Amphibien Europas. Stuttgart: Kosmos. Snell, C. (2011). Evaluation of methods to separate brown and water frogs. Herpetological Bulletin 118: Wycherley, J. (2003). Water frogs in Britain. British Wildlife 14 (2): Herpetological Bulletin 123 (2013) 7
Section 13, Part 1 ANOVA. Analysis Of Variance
Section 13, Part 1 ANOVA Analysis Of Variance Course Overview So far in this course we ve covered: Descriptive statistics Summary statistics Tables and Graphs Probability Probability Rules Probability
Chapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
Reflection and Refraction
Equipment Reflection and Refraction Acrylic block set, plane-concave-convex universal mirror, cork board, cork board stand, pins, flashlight, protractor, ruler, mirror worksheet, rectangular block worksheet,
Session 7 Bivariate Data and Analysis
Session 7 Bivariate Data and Analysis Key Terms for This Session Previously Introduced mean standard deviation New in This Session association bivariate analysis contingency table co-variation least squares
Analysis of Variance ANOVA
Analysis of Variance ANOVA Overview We ve used the t -test to compare the means from two independent groups. Now we ve come to the final topic of the course: how to compare means from more than two populations.
Self-Portrait Steps Images taken from Andrew Loomis Drawing Head & Hands (there are many sites to download this out of print book for free)
Self-Portrait Steps Images taken from Andrew Loomis Drawing Head & Hands (there are many sites to download this out of print book for free) First of all- put the idea of it doesn t look like me! out of
SAFELIGHT FILTERS AND DARKROOM LAMPS
TECHINCAL INFORMATION SAFELIGHT FILTERS AND DARKROOM LAMPS A RANGE OF HIGH QUALITY FILTERS AND LAMPS FOR DARKROOM ILLUMINATION DESCRIPTION ILFORD manufacture a range of high quality Safelight Colour Use
DATA INTERPRETATION AND STATISTICS
PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE
Comparing Means in Two Populations
Comparing Means in Two Populations Overview The previous section discussed hypothesis testing when sampling from a single population (either a single mean or two means from the same population). Now we
A s h o r t g u i d e t o s ta n d A r d i s e d t e s t s
A short guide to standardised tests Copyright 2013 GL Assessment Limited Published by GL Assessment Limited 389 Chiswick High Road, 9th Floor East, London W4 4AL www.gl-assessment.co.uk GL Assessment is
Unit 9 Describing Relationships in Scatter Plots and Line Graphs
Unit 9 Describing Relationships in Scatter Plots and Line Graphs Objectives: To construct and interpret a scatter plot or line graph for two quantitative variables To recognize linear relationships, non-linear
Statistiek II. John Nerbonne. October 1, 2010. Dept of Information Science [email protected]
Dept of Information Science [email protected] October 1, 2010 Course outline 1 One-way ANOVA. 2 Factorial ANOVA. 3 Repeated measures ANOVA. 4 Correlation and regression. 5 Multiple regression. 6 Logistic
2. Simple Linear Regression
Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according
ON A NEW SPECIES OF DENISONIA (REPTILIA, SERPENTES) FROM NEW GUINEA
ON A NEW SPECIES OF DENISONIA (REPTILIA, SERPENTES) FROM NEW GUINEA by L. D. BRONGERSMA and M. S. KNAAP-VAN MEEUWEN Until now the Elapid genus Denisonia had not been recorded from New Guinea, and this
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish Statistics Statistics are quantitative methods of describing, analysing, and drawing inferences (conclusions)
Experiment 3 Lenses and Images
Experiment 3 Lenses and Images Who shall teach thee, unless it be thine own eyes? Euripides (480?-406? BC) OBJECTIVES To examine the nature and location of images formed by es. THEORY Lenses are frequently
Part 2: Analysis of Relationship Between Two Variables
Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable
DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.
DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,
Geometric Optics Converging Lenses and Mirrors Physics Lab IV
Objective Geometric Optics Converging Lenses and Mirrors Physics Lab IV In this set of lab exercises, the basic properties geometric optics concerning converging lenses and mirrors will be explored. The
Course Objective This course is designed to give you a basic understanding of how to run regressions in SPSS.
SPSS Regressions Social Science Research Lab American University, Washington, D.C. Web. www.american.edu/provost/ctrl/pclabs.cfm Tel. x3862 Email. [email protected] Course Objective This course is designed
Inference for two Population Means
Inference for two Population Means Bret Hanlon and Bret Larget Department of Statistics University of Wisconsin Madison October 27 November 1, 2011 Two Population Means 1 / 65 Case Study Case Study Example
The Wilcoxon Rank-Sum Test
1 The Wilcoxon Rank-Sum Test The Wilcoxon rank-sum test is a nonparametric alternative to the twosample t-test which is based solely on the order in which the observations from the two samples fall. We
International Specification for Ski Orienteering Maps ISSkiOM
International Specification for Ski Orienteering Maps ISSkiOM Rev 12 February 2015 corrected mistake (PMS 345-> PMS 354), page 3 2014 Approved by IOF Ski Orienteering Commission, October 2014 Approved
2. Incidence, prevalence and duration of breastfeeding
2. Incidence, prevalence and duration of breastfeeding Key Findings Mothers in the UK are breastfeeding their babies for longer with one in three mothers still breastfeeding at six months in 2010 compared
A Method of Population Estimation: Mark & Recapture
Biology 103 A Method of Population Estimation: Mark & Recapture Objectives: 1. Learn one method used by wildlife biologists to estimate population size of wild animals. 2. Learn how sampling size effects
Content Sheet 7-1: Overview of Quality Control for Quantitative Tests
Content Sheet 7-1: Overview of Quality Control for Quantitative Tests Role in quality management system Quality Control (QC) is a component of process control, and is a major element of the quality management
Solving Simultaneous Equations and Matrices
Solving Simultaneous Equations and Matrices The following represents a systematic investigation for the steps used to solve two simultaneous linear equations in two unknowns. The motivation for considering
Teacher Management. Estimated Time for Completion 2 class periods
AP Biology, Aquatic Science, Environmental Systems Morphometric Analysis of Eurycea in Austin, TX Teacher Page Essential Questions: Enduring Understandings Nature of Science and Data Vocabulary Morphometric
Week 4: Standard Error and Confidence Intervals
Health Sciences M.Sc. Programme Applied Biostatistics Week 4: Standard Error and Confidence Intervals Sampling Most research data come from subjects we think of as samples drawn from a larger population.
CALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents
Descriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
Multivariate Analysis of Variance. The general purpose of multivariate analysis of variance (MANOVA) is to determine
2 - Manova 4.3.05 25 Multivariate Analysis of Variance What Multivariate Analysis of Variance is The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels
SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
Confidence Intervals for One Standard Deviation Using Standard Deviation
Chapter 640 Confidence Intervals for One Standard Deviation Using Standard Deviation Introduction This routine calculates the sample size necessary to achieve a specified interval width or distance from
Map reading made easy
Map reading made easy What is a map? A map is simply a plan of the ground on paper. The plan is usually drawn as the land would be seen from directly above. A map will normally have the following features:
Module 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
Study Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
UNDERSTANDING THE TWO-WAY ANOVA
UNDERSTANDING THE e have seen how the one-way ANOVA can be used to compare two or more sample means in studies involving a single independent variable. This can be extended to two independent variables
One-Way ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate
1 One-Way ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,
Free Inductive/Logical Test Questions
Free Inductive/Logical Test Questions (With questions and answers) JobTestPrep invites you to a free practice session that represents only some of the materials offered in our online practice packs. Have
Comparing Nested Models
Comparing Nested Models ST 430/514 Two models are nested if one model contains all the terms of the other, and at least one additional term. The larger model is the complete (or full) model, and the smaller
Gage Studies for Continuous Data
1 Gage Studies for Continuous Data Objectives Determine the adequacy of measurement systems. Calculate statistics to assess the linearity and bias of a measurement system. 1-1 Contents Contents Examples
THE BEATIFUL FACE. Beauty. Esthetics. Beauty ESTHETICS LOCAL FASHIONS. Notion of beauty. Looking good. Orthodontics 2005
THE BEATIFUL FACE Orthodontics 2005 Malcolm E. Meistrell,, Jr., D.D.S. Clinical Professor of Dentistry Division of Orthodontics School of Dental and Oral Surgery Columbia University Beauty Beauty Esthetics
Analysing Questionnaires using Minitab (for SPSS queries contact -) [email protected]
Analysing Questionnaires using Minitab (for SPSS queries contact -) [email protected] Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:
Binocular Vision and The Perception of Depth
Binocular Vision and The Perception of Depth Visual Perception How one visually interprets a scene 4 forms of perception to be studied: Depth Color Temporal Motion Depth Perception How does one determine
EXPERIMENT #1: MICROSCOPY
EXPERIMENT #1: MICROSCOPY Brightfield Compound Light Microscope The light microscope is an important tool in the study of microorganisms. The compound light microscope uses visible light to directly illuminate
Sample Size and Power in Clinical Trials
Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance
Electrical Resonance
Electrical Resonance (R-L-C series circuit) APPARATUS 1. R-L-C Circuit board 2. Signal generator 3. Oscilloscope Tektronix TDS1002 with two sets of leads (see Introduction to the Oscilloscope ) INTRODUCTION
Optimum proportions for the design of suspension bridge
Journal of Civil Engineering (IEB), 34 (1) (26) 1-14 Optimum proportions for the design of suspension bridge Tanvir Manzur and Alamgir Habib Department of Civil Engineering Bangladesh University of Engineering
Freehand Sketching. Sections
3 Freehand Sketching Sections 3.1 Why Freehand Sketches? 3.2 Freehand Sketching Fundamentals 3.3 Basic Freehand Sketching 3.4 Advanced Freehand Sketching Key Terms Objectives Explain why freehand sketching
Three daily lessons. Year 5
Unit 6 Perimeter, co-ordinates Three daily lessons Year 4 Autumn term Unit Objectives Year 4 Measure and calculate the perimeter of rectangles and other Page 96 simple shapes using standard units. Suggest
Independent samples t-test. Dr. Tom Pierce Radford University
Independent samples t-test Dr. Tom Pierce Radford University The logic behind drawing causal conclusions from experiments The sampling distribution of the difference between means The standard error of
Introduction to the TI-Nspire CX
Introduction to the TI-Nspire CX Activity Overview: In this activity, you will become familiar with the layout of the TI-Nspire CX. Step 1: Locate the Touchpad. The Touchpad is used to navigate the cursor
Describing Populations Statistically: The Mean, Variance, and Standard Deviation
Describing Populations Statistically: The Mean, Variance, and Standard Deviation BIOLOGICAL VARIATION One aspect of biology that holds true for almost all species is that not every individual is exactly
Section 1.3 P 1 = 1 2. = 1 4 2 8. P n = 1 P 3 = Continuing in this fashion, it should seem reasonable that, for any n = 1, 2, 3,..., = 1 2 4.
Difference Equations to Differential Equations Section. The Sum of a Sequence This section considers the problem of adding together the terms of a sequence. Of course, this is a problem only if more than
Woodcock Ringing Guide Owen Williams
Woodcock Ringing Guide Owen Williams All bird ringing in the UK is controlled by the British Trust for Ornithology. Ringing is only allowed by those granted a permit by the BTO or who are undergoing training
Two-sample inference: Continuous data
Two-sample inference: Continuous data Patrick Breheny April 5 Patrick Breheny STA 580: Biostatistics I 1/32 Introduction Our next two lectures will deal with two-sample inference for continuous data As
How To Check For Differences In The One Way Anova
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way
Statistics courses often teach the two-sample t-test, linear regression, and analysis of variance
2 Making Connections: The Two-Sample t-test, Regression, and ANOVA In theory, there s no difference between theory and practice. In practice, there is. Yogi Berra 1 Statistics courses often teach the two-sample
2013 MBA Jump Start Program. Statistics Module Part 3
2013 MBA Jump Start Program Module 1: Statistics Thomas Gilbert Part 3 Statistics Module Part 3 Hypothesis Testing (Inference) Regressions 2 1 Making an Investment Decision A researcher in your firm just
MULTIPLE REGRESSION WITH CATEGORICAL DATA
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 86 MULTIPLE REGRESSION WITH CATEGORICAL DATA I. AGENDA: A. Multiple regression with categorical variables. Coding schemes. Interpreting
Experiment 3: Magnetic Fields of a Bar Magnet and Helmholtz Coil
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Physics 8.02 Spring 2006 Experiment 3: Magnetic Fields of a Bar Magnet and Helmholtz Coil OBJECTIVES 1. To learn how to visualize magnetic field lines
Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
Adaptive information source selection during hypothesis testing
Adaptive information source selection during hypothesis testing Andrew T. Hendrickson ([email protected]) Amy F. Perfors ([email protected]) Daniel J. Navarro ([email protected])
World Oceans Day at ZSL Whipsnade Zoo
World Oceans Day at ZSL Whipsnade Zoo Teachers notes KS 1 & KS 2 This booklet will help you to focus your self guided trail on ocean animals, looking at the adaptations of the species and focusing in on
Lecture notes for Choice Under Uncertainty
Lecture notes for Choice Under Uncertainty 1. Introduction In this lecture we examine the theory of decision-making under uncertainty and its application to the demand for insurance. The undergraduate
Case Study in Data Analysis Does a drug prevent cardiomegaly in heart failure?
Case Study in Data Analysis Does a drug prevent cardiomegaly in heart failure? Harvey Motulsky [email protected] This is the first case in what I expect will be a series of case studies. While I mention
Copyright 2011 Casa Software Ltd. www.casaxps.com. Centre of Mass
Centre of Mass A central theme in mathematical modelling is that of reducing complex problems to simpler, and hopefully, equivalent problems for which mathematical analysis is possible. The concept of
Introduction to Quantitative Methods
Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................
Linear Models in STATA and ANOVA
Session 4 Linear Models in STATA and ANOVA Page Strengths of Linear Relationships 4-2 A Note on Non-Linear Relationships 4-4 Multiple Linear Regression 4-5 Removal of Variables 4-8 Independent Samples
CHAPTER THREE COMMON DESCRIPTIVE STATISTICS COMMON DESCRIPTIVE STATISTICS / 13
COMMON DESCRIPTIVE STATISTICS / 13 CHAPTER THREE COMMON DESCRIPTIVE STATISTICS The analysis of data begins with descriptive statistics such as the mean, median, mode, range, standard deviation, variance,
Inductive Reasoning Free Sample Test 1
Inductive Reasoning Free Sample Test 1 Solutions Booklet Difficulty Rating: Difficult Instructions This inductive reasoning test comprises 22 questions. You will have 25 minutes in which to correctly answer
Refractive Index Measurement Principle
Refractive Index Measurement Principle Refractive index measurement principle Introduction Detection of liquid concentrations by optical means was already known in antiquity. The law of refraction was
Fairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
Introduction: Growth analysis and crop dry matter accumulation
PBIO*3110 Crop Physiology Lecture #2 Fall Semester 2008 Lecture Notes for Tuesday 9 September How is plant productivity measured? Introduction: Growth analysis and crop dry matter accumulation Learning
Correlation and Simple Linear Regression
Correlation and Simple Linear Regression We are often interested in studying the relationship among variables to determine whether they are associated with one another. When we think that changes in a
Creating Charts in Microsoft Excel A supplement to Chapter 5 of Quantitative Approaches in Business Studies
Creating Charts in Microsoft Excel A supplement to Chapter 5 of Quantitative Approaches in Business Studies Components of a Chart 1 Chart types 2 Data tables 4 The Chart Wizard 5 Column Charts 7 Line charts
IDENTIFICATION OF ADULT CORIXIDS 25
IDENTIFICATION OF ADULT CORIXIDS 25 THE IDENTIFICATION OF BRITISH ADULT SPECIMENS OF SIGARA LATERALIS (LEACH), SIGARA CONCINNA (FIEBER), CALLICORIXA PRAEUSTA (FIEBER) AND CALLICORIXA WOLLASTONI (DOUGLAS
Economics explains discrimination in the labour market
Economics explains discrimination in the labour market 5 Neoclassical models of discrimination 5.1 Introduction Our earlier discussion suggested that to understand labour market discrimination we need
WEB APPENDIX. Calculating Beta Coefficients. b Beta Rise Run Y 7.1 1 8.92 X 10.0 0.0 16.0 10.0 1.6
WEB APPENDIX 8A Calculating Beta Coefficients The CAPM is an ex ante model, which means that all of the variables represent before-thefact, expected values. In particular, the beta coefficient used in
Motion & The Global Positioning System (GPS)
Grade Level: K - 8 Subject: Motion Prep Time: < 10 minutes Duration: 30 minutes Objective: To learn how to analyze GPS data in order to track an object and derive its velocity from positions and times.
Mathematics (Project Maths)
Pre-Leaving Certificate Examination Mathematics (Project Maths) Paper 2 Higher Level February 2010 2½ hours 300 marks Running total Examination number Centre stamp For examiner Question Mark 1 2 3 4 5
Describing, Exploring, and Comparing Data
24 Chapter 2. Describing, Exploring, and Comparing Data Chapter 2. Describing, Exploring, and Comparing Data There are many tools used in Statistics to visualize, summarize, and describe data. This chapter
The correlation coefficient
The correlation coefficient Clinical Biostatistics The correlation coefficient Martin Bland Correlation coefficients are used to measure the of the relationship or association between two quantitative
Experience in Great Crested Newt Mitigation: Guidance for Ecologists and Developers
Experience in Great Crested Newt Mitigation: Guidance for Ecologists and Developers 1. Aims of this note To help ecological consultants to assess their skills and experience levels before applying for
Tutorial for proteome data analysis using the Perseus software platform
Tutorial for proteome data analysis using the Perseus software platform Laboratory of Mass Spectrometry, LNBio, CNPEM Tutorial version 1.0, January 2014. Note: This tutorial was written based on the information
The Use of Above Ground Vehicle Detectors
Traffic Advisory Leaflet 16/99 December 1999 The Use of Above Ground Vehicle Detectors Introduction Responsive traffic signal systems are a key component in managing the safe and efficient progress of
Non-random/non-probability sampling designs in quantitative research
206 RESEARCH MET HODOLOGY Non-random/non-probability sampling designs in quantitative research N on-probability sampling designs do not follow the theory of probability in the choice of elements from the
Biology 300 Homework assignment #1 Solutions. Assignment:
Biology 300 Homework assignment #1 Solutions Assignment: Chapter 1, Problems 6, 15 Chapter 2, Problems 6, 8, 9, 12 Chapter 3, Problems 4, 6, 15 Chapter 4, Problem 16 Answers in bold. Chapter 1 6. Identify
DRIVER ATTRIBUTES AND REAR-END CRASH INVOLVEMENT PROPENSITY
U.S. Department of Transportation National Highway Traffic Safety Administration DOT HS 809 540 March 2003 Technical Report DRIVER ATTRIBUTES AND REAR-END CRASH INVOLVEMENT PROPENSITY Published By: National
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
Doppler. Doppler. Doppler shift. Doppler Frequency. Doppler shift. Doppler shift. Chapter 19
Doppler Doppler Chapter 19 A moving train with a trumpet player holding the same tone for a very long time travels from your left to your right. The tone changes relative the motion of you (receiver) and
Variables Control Charts
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. Variables
Chapter 10. Key Ideas Correlation, Correlation Coefficient (r),
Chapter 0 Key Ideas Correlation, Correlation Coefficient (r), Section 0-: Overview We have already explored the basics of describing single variable data sets. However, when two quantitative variables
Doing Multiple Regression with SPSS. In this case, we are interested in the Analyze options so we choose that menu. If gives us a number of choices:
Doing Multiple Regression with SPSS Multiple Regression for Data Already in Data Editor Next we want to specify a multiple regression analysis for these data. The menu bar for SPSS offers several options:
Introduction. Hypothesis Testing. Hypothesis Testing. Significance Testing
Introduction Hypothesis Testing Mark Lunt Arthritis Research UK Centre for Ecellence in Epidemiology University of Manchester 13/10/2015 We saw last week that we can never know the population parameters
Descriptive Statistics
Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web
Magnetic Fields and Their Effects
Name Date Time to Complete h m Partner Course/ Section / Grade Magnetic Fields and Their Effects This experiment is intended to give you some hands-on experience with the effects of, and in some cases
