Results. Figure 1. Biovolume of Food vs Thoracic Beats in D. magna One Way Analysis of VarianceThursday, February 02, 2006, 4:26:10 PM Group Name N.

Slides:

Advertisements

Similar presentations

1 Chapter 20: Statistical Tests for Ordinal Data.

Advertisements

Estimating a Population Variance

Non-parametric test Introduction, Wilcoxon rank sum test and Man-Whitney U test Reporter: Shao-Li Han.

Chapter 16 Introduction to Nonparametric Statistics

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Nonparametric Methods Chapter 15.

Design of Experiments and Analysis of Variance

Nonparametric tests and ANOVAs: What you need to know.

Probability & Statistical Inference Lecture 8 MSc in Computing (Data Analytics)

10-1 Introduction 10-2 Inference for a Difference in Means of Two Normal Distributions, Variances Known Figure 10-1 Two independent populations.

Analysis of Variance. Experimental Design u Investigator controls one or more independent variables –Called treatment variables or factors –Contain two.

Differences Between Group Means

Chapter Topics The Completely Randomized Model: One-Factor Analysis of Variance F-Test for Difference in c Means The Tukey-Kramer Procedure ANOVA Assumptions.

1 Pertemuan 11 Analisis Varians Data Nonparametrik Matakuliah: A0392 – Statistik Ekonomi Tahun: 2006.

Analysis of variance (2) Lecture 10. Normality Check Frequency histogram (Skewness & Kurtosis) Probability plot, K-S test Normality Check Frequency histogram.

Chi-Square and F Distributions Chapter 11 Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania.

1 Confidence Intervals for Means. 2 When the sample size n< 30 case1-1. the underlying distribution is normal with known variance case1-2. the underlying.

Running Fisher’s LSD Multiple Comparison Test in Excel

Chapter 15 Nonparametric Statistics

T Test for One Sample. Why use a t test? The sampling distribution of t represents the distribution that would be obtained if a value of t were calculated.

1 Tests with two+ groups We have examined tests of means for a single group, and for a difference if we have a matched sample (as in husbands and wives)

1 Department of Pathophysiology Faculty of Medicine in Pilsen STATISTICS.

7.2 Confidence Intervals When SD is unknown. The value of , when it is not known, must be estimated by using s, the standard deviation of the sample.

Formica planipilis ( Thatch ants)  Habitat.  Queen Ant  Male Ants  Colony life cycle  Workers.

Choosing and using statistics to test ecological hypotheses

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Statistical Inferences Based on Two Samples Chapter 9.

T tests comparing two means t tests comparing two means.

1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 11 Inferences About Population Variances n Inference about a Population Variance n.

One Sample Inf-1 If sample came from a normal distribution, t has a t-distribution with n-1 degrees of freedom. 1)Symmetric about 0. 2)Looks like a standard.

Statistics 11 Correlations Definitions: A correlation is measure of association between two quantitative variables with respect to a single individual.

CHAPTER 14: Nonparametric Methods

CHAPTER 14: Nonparametric Methods to accompany Introduction to Business Statistics seventh edition, by Ronald M. Weiers Presentation by Priscilla Chaffe-Stengel.

Chapter 9: Non-parametric Tests n Parametric vs Non-parametric n Chi-Square –1 way –2 way.

ANOVA. Independent ANOVA Scores vary – why? Total variability can be divided up into 2 parts 1) Between treatments 2) Within treatments.

Lesson 15 - R Chapter 15 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.

Essential Statistics Chapter 161 Review Part III_A_Chi Z-procedure Vs t-procedure.

Chapter 15 – Analysis of Variance Math 22 Introductory Statistics.

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 11-1 Chapter 11 Chi-Square Tests and Nonparametric Tests Statistics for.

Kruskal-Wallis H TestThe Kruskal-Wallis H Test is a nonparametric procedure that can be used to compare more than two populations in a completely randomized.

: An alternative representation of level of significance. - normal distribution applies. - α level of significance (e.g. 5% in two tails) determines the.

Friedman F r TestThe Friedman F r Test is the nonparametric equivalent of the randomized block design with k treatments and b blocks. All k measurements.

CD-ROM Chap 16-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition CD-ROM Chapter 16 Introduction.

NON-PARAMETRIC STATISTICS

The t-test: When you use it When you wish to compare the difference between two groups for a continuous (or discrete) variable Examples Blood pressure.

Comparing k > 2 Groups - Numeric Responses Extension of Methods used to Compare 2 Groups Parallel Groups and Crossover Designs Normal and non-normal data.

Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.

Section 6.4 Inferences for Variances. Chi-square probability densities.

1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 

MATB344 Applied Statistics I. Experimental Designs for Small Samples II. Statistical Tests of Significance III. Small Sample Test Statistics Chapter 10.

Correlation. u Definition u Formula Positive Correlation r =

Copyright © 2010, 2007, 2004 Pearson Education, Inc Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by.

Chi Square Test for Goodness of Fit Determining if our sample fits the way it should be.

Session 9: k Samples (Zar, Chapter 10). (1) General Setup: Group 1Group 2…Group k x 11 x 21 x k1 x 12 x 22 x k2 x 13 x 23 x k3 x 1n 1 x 2n 2 x kn k H.

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. CHAPTER 14: Nonparametric Methods to accompany Introduction to Business Statistics fifth.

Statistical Inferences for Population Variances

Inference concerning two population variances

Math 4030 – 10a Tests for Population Mean(s)

Chapter 11 Hypothesis Testing II

Data Analysis and Interpretation

Inference on Mean, Var Unknown

Statistical Inference for the Mean Confidence Interval

F - Ratio Table Degrees of Freedom for the Factor

Statistical Process Control

Tutorial 9 Suppose that a random sample of size 10 is drawn from a normal distribution with mean 10 and variance 4. Find the following probabilities:

Time Calanoid CR Calanoid SE Cladocera CR Cladocera SE

Experimental Design Data Normal Distribution

Elementary Statistics: Picturing The World

Statistical Inference for the Mean: t-test

Thoracic beats per minute by Daphnia magna exposed to variable concentrations of Microcystis sp. and Nannochloropsis sp. (P = 0.140)

Number of treatments ____________________________________________

Presentation transcript:

Results

Figure 1.

Biovolume of Food vs Thoracic Beats in D. magna One Way Analysis of VarianceThursday, February 02, 2006, 4:26:10 PM Group Name N MissingMeanStd DevSEM Mean TB High Micro Mean TB Low Micro Mean TB High Nano Mean TB Low Nano The differences in the mean values among the treatment groups are not great enough to exclude the possibility that the difference is due to random sampling variability; there is not a statistically significant difference (P = 0.140). Table 1 Though there is no significant difference between the Biovolume level of food and the number of thoracic beats in D. magna, there was a trend observed where the Daphnia performed a greater number of thoracic beats at the lower concentrations of food. Also, there was a trend where the number of thoracic beats was greater when in concentrations of Nanocloropsys.

Figure 2. Post Abdominal Rejection vs Food Concentration for D. magna

One Way Analysis of VarianceTuesday, February 07, 2006, 1:57:21 PM Data source: Nanno & MC Data in zool811 Mic vs TB Normality Test:Failed(P < 0.050) Kruskal-Wallis One Way Analysis of Variance on RanksTuesday, February 07, 2006, 1:57:21 PM Data source: Nanno & MC Data in zool811 Mic vs TB GroupN Missing Median 25% 75% Micro PAR lo Micro PAR hi Nanno PAR lo Nanno PAR hi H = with 3 degrees of freedom. (P = 0.425) The differences in the median values among the treatment groups are not great enough to exclude the possibility that the difference is due to random sampling variability; there is not a statistically significant difference (P = 0.425)

Post Abdominal Rejection at Low and High Food Concentrations There is no significance between the post abdominal reflexes (PAR) at the food concentrations tested, however, one allegedly present trend was noted: there were fewer PAR’s in the low concentrated substrates.

One Way Analysis of VarianceThursday, February 02, 2006, 4:25:23 PM Kruskal-Wallis One Way Analysis of Variance on RanksThursday, February 02, 2006, 4:25:23 PM Data source: Data 1 in ComparisonTBHIGHLOW GroupN Missing Median 25% 75% Mean TB High Micro Mean TB Low Micro Mean TB High Nano Mean TB Low Nano H The differences in the median values among the treatment groups are not great enough to exclude the possibility that the difference is due to random sampling variability; there is not a statistically significant difference (P = 0.139)

t-testThursday, February 02, 2006, 4:23:48 PM Mann-Whitney Rank Sum TestThursday, February 02, 2006, 4:23:48 PM Data source: Data 1 in ComparisonTBHIGHLOW GroupN Missing Median 25% 75% Mean TB Low Micro Mean TB Low Nano T = n(small)= 4 n(big)= 4 P(est.)= P(exact)= The difference in the median values between the two groups is not great enough to exclude the possibility that the difference is due to random sampling variability; there is not a statistically significant difference (P = 0.886)

t-testThursday, February 02, 2006, 4:22:37 PM Group Name N MissingMeanStd DevSEM Mean TB High Micro Mean TB High Nano Difference t = with 6 degrees of freedom. (P = 0.081) 95 percent confidence interval for difference of means: to The difference in the mean values of the two groups is not great enough to reject the possibility that the difference is due to random sampling variability. There is not a statistically significant difference between the input groups (P = 0.081).