Presentation is loading. Please wait.

Presentation is loading. Please wait.

Statistics review 1 Basic concepts: Variability measures Distributions Hypotheses Types of error Common analyses T-tests One-way ANOVA Two-way ANOVA Randomized.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "Statistics review 1 Basic concepts: Variability measures Distributions Hypotheses Types of error Common analyses T-tests One-way ANOVA Two-way ANOVA Randomized."— Presentation transcript:

1 Statistics review 1 Basic concepts: Variability measures Distributions Hypotheses Types of error Common analyses T-tests One-way ANOVA Two-way ANOVA Randomized block

2 Variance Ecological rule # 1: Everything varies …but how much does it vary?

3 Variance Sum-of-square cake Urchin size 3cm15cm

4 Urchin size 3cm15cm Urchin size 3cm15cm Sum-of-square cake

5 Variance What is the mean and variance of 4, 3, 3, 2 ? What are the units? Mean = 3, Variance = 0.67

6 Variance variants 1. Standard deviation (s, or SD) = Square root (variance) Advantage: units

7 Variance variants 2. Standard error (S.E.) Advantage: indicates precision

8 How to report Tourist boats observed 29.7 (+ 5.3) shark attacks on seals (mean + S.E.) A mean (+ SD) of 29.7 (+ 7.4) shark attacks were seen per month + 1SE or SD - 1SE or SD

9 Distributions Normal Quantitative data Poisson Count (frequency) data

10 Normal distribution 67% of data within 1 SD of mean 95% of data within 2 SD of mean

11 Poisson distribution mean Mostly, nothing happens (lots of zeros)

12 Poisson distribution Frequency data Lots of zero (or minimum value) data Variance increases with the mean

13 1.Correct for correlation between mean and variance by log-transforming y (but log (0) is undefined!!) 2.Use non-parametric statistics (but low power) 3.Use a “generalized linear model” specifying a Poisson distribution What do you do with Poisson data?

14 Null (Ho): no effect of our experimental treatment, “status quo” Alternative (Ha): there is an effect Hypotheses

15 Whose null hypothesis? Conditions very strict for rejecting Ho, whereas accepting Ho is easy (just a matter of not finding grounds to reject it). Preliminary study? A criminal trial? Chance of a disease epidemic?

16 Hypotheses Null (Ho) and alternative (Ha): always mutually exclusive So if Ha is treatment>control…

17 Types of error Type 1 error Type 2 error Reject HoAccept Ho Ho true Ho false

18 Usually ensure only 5% chance of type 1 error (ie. Alpha =0.05) Ability to minimize type 2 error: called power Types of error

Download ppt "Statistics review 1 Basic concepts: Variability measures Distributions Hypotheses Types of error Common analyses T-tests One-way ANOVA Two-way ANOVA Randomized."

Similar presentations

Lecture (11,12) Parameter Estimation of PDF and Fitting a Distribution Function.

Lecture (11,12) Parameter Estimation of PDF and Fitting a Distribution Function.
LSU-HSC School of Public Health Biostatistics 1 Statistical Core Didactic Introduction to Biostatistics Donald E. Mercante, PhD.

LSU-HSC School of Public Health Biostatistics 1 Statistical Core Didactic Introduction to Biostatistics Donald E. Mercante, PhD.
CHAPTER 21 Inferential Statistical Analysis. Understanding probability The idea of probability is central to inferential statistics. It means the chance.

CHAPTER 21 Inferential Statistical Analysis. Understanding probability The idea of probability is central to inferential statistics. It means the chance.
Hypothesis: It is an assumption of population parameter ( mean, proportion, variance) There are two types of hypothesis : 1) Simple hypothesis :A statistical.

Hypothesis: It is an assumption of population parameter ( mean, proportion, variance) There are two types of hypothesis : 1) Simple hypothesis :A statistical.
Inference Sampling distributions Hypothesis testing.

Inference Sampling distributions Hypothesis testing.
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.

6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
Statistical Significance What is Statistical Significance? What is Statistical Significance? How Do We Know Whether a Result is Statistically Significant?

Statistical Significance What is Statistical Significance? What is Statistical Significance? How Do We Know Whether a Result is Statistically Significant?
HYPOTHESIS TESTING Four Steps Statistical Significance Outcomes Sampling Distributions.

HYPOTHESIS TESTING Four Steps Statistical Significance Outcomes Sampling Distributions.
QUANTITATIVE DATA ANALYSIS

QUANTITATIVE DATA ANALYSIS
Business 205. Review Sampling Continuous Random Variables Central Limit Theorem Z-test.

Business 205. Review Sampling Continuous Random Variables Central Limit Theorem Z-test.
Experimental Design, Statistical Analysis CSCI 4800/6800 University of Georgia Spring 2007 Eileen Kraemer.

Experimental Design, Statistical Analysis CSCI 4800/6800 University of Georgia Spring 2007 Eileen Kraemer.
DATA ANALYSIS I MKT525. Plan of analysis What decision must be made? What are research objectives? What do you have to know to reach those objectives?

DATA ANALYSIS I MKT525. Plan of analysis What decision must be made? What are research objectives? What do you have to know to reach those objectives?
Statistical Significance What is Statistical Significance? How Do We Know Whether a Result is Statistically Significant? How Do We Know Whether a Result.

Statistical Significance What is Statistical Significance? How Do We Know Whether a Result is Statistically Significant? How Do We Know Whether a Result.
Chapter 6 Hypotheses texts. Central Limit Theorem Hypotheses and statistics are dependent upon this theorem.

Chapter 6 Hypotheses texts. Central Limit Theorem Hypotheses and statistics are dependent upon this theorem.
Spotting pseudoreplication 1.Inspect spatial (temporal) layout of the experiment 2.Examine degrees of freedom in analysis.

Spotting pseudoreplication 1.Inspect spatial (temporal) layout of the experiment 2.Examine degrees of freedom in analysis.
MARE 250 Dr. Jason Turner Hypothesis Testing III.

MARE 250 Dr. Jason Turner Hypothesis Testing III.
Response – variable of interest; variable you collect - #Fish, %Coral cover, temperature, salinity, etc Factor – variable by which response is divided;

Response – variable of interest; variable you collect - #Fish, %Coral cover, temperature, salinity, etc Factor – variable by which response is divided;
C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Overview of Lecture Independent and Dependent Variables Between and Within Designs.

C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Overview of Lecture Independent and Dependent Variables Between and Within Designs.
PSYC512: Research Methods PSYC512: Research Methods Lecture 9 Brian P. Dyre University of Idaho.

PSYC512: Research Methods PSYC512: Research Methods Lecture 9 Brian P. Dyre University of Idaho.
Independent Sample T-test Often used with experimental designs N subjects are randomly assigned to two groups (Control * Treatment). After treatment, the.

Independent Sample T-test Often used with experimental designs N subjects are randomly assigned to two groups (Control * Treatment). After treatment, the.

Similar presentations

Ads by Google