A. Descriptive
B. Distributions
C. Comparative
III. Statistics
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Guidelines and tips
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community
Ask the community for help and clear up your study doubts
University Rankings
Discover the best universities in your country according to Docsity users
Free resources
Our save-the-student-ebooks!
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
Statistics: Descriptive, Distributions, and Comparative Analysis with Focus on Central Ten, Study notes of Ecology and Environment
An overview of descriptive statistics, distributions, and comparative statistics in the context of statistical analysis. Topics include central tendency (mean and standard deviation), dispersion, normal and log normal distributions, and tests such as t test, z test, and chi-square test. The document also covers concepts like degrees of freedom, probability, and statistical hypotheses.
Typology: Study notes
Pre 2010
1 / 17
This page cannot be seen from the preview
Don't miss anything!
Related documents
Partial preview of the text
Download Statistics: Descriptive, Distributions, and Comparative Analysis with Focus on Central Ten and more Study notes Ecology and Environment in PDF only on Docsity!
A. Descriptive B. Distributions C. Comparative
III. Statistics
- Central tendency- mean or average, m = population mean & = sample mean
- Dispersion- standard deviation, s = population SD & s = sample SD
- m + s is estimated by + s
A. Descriptive Statistics
a. Bell curve
2. Normal Distribution
a. Log curve
3. Log Normal Distribution
- Questions
- Tests
- Variance
- Decision Theory
C. Comparative Statistics
a. Do the observed experimental results agree with the expected theoretical results? b. Are the experimental results significantly different than the control results? c. Is the experimental sample significantly different than the control sample?
1. Questions
d. c^2 test Example
1.) Flip a coin 10x, 5:5, 6:4, 7:3, 8:... 2.)
3.) degrees of freedom = # of classes – 1 df = 2 - 1 = 1, so 0.148 < c^2 calc < 0.455, & 0.50 > p > 0. 4.) If p < 0.05, then sig diff exists
Result Exp Obs E - O (E-O)^2 (E-O)^2 /d Head 5 4 1 1 1/5 = 0. Tail 5 6 -1 1 1/5 = 0. c^2 calc = (^) 0.
1.) 50 to 70% of time when a coin is flipped 10x, 6 to 4 split or greater can occur by chance alone 2.) 50 to 70% likelihood deviation results from chance 3.) 30 to 50% chance deviation is real & not random 4.) Do not use %, 6 & 4 vs 60 & 40
d. c^2 test Interpretation
a. Statistical hypotheses b. Type I & II errors c. Sample size
4. Decision Theory
1.) Null hypothesis- m 1 = m 2 , used to
estimate m
2.) Alternate hypothesis-
nondirectional, m 1 m 2 ; directional,
m 1 < m 2 or m 1 > m 2
a. Statistical Hypotheses
1.) N = population size & n = sample size 2.) Central Limit Theorem- as n approaches N, sample will more likely represent population 3.) At n = 25, the sampling distribution of becomes normal & meaningful predictions are possible 4.) Using n = 30 allows for mistakes, deaths,... & still have sample >