'Group sum' presentation slideshows

One-Way Analysis of Variance

1. One-Way Analysis of Variance. Comparing means of more than 2 independent samples. Why not multiple t-tests?. 1. # comparisons = k(k-1)/2, where k is the # of groups. In other words, when there are many more than 2 groups, the number of comparisons is large

380 views • 22 slides

S519: Evaluation of Information Systems

S519: Evaluation of Information Systems. Social Statistics Inferential Statistics Chapter 11: ANOVA. Last week. This week. When to use F statstic How to compute and interpret Using FTEST and FDIST functions How to use the ANOVA. Analysis of variance.

343 views • 22 slides

S519: Evaluation of Information Systems

S519: Evaluation of Information Systems. Social Statistics Inferential Statistics Chapter 11: ANOVA. Last week. This week. When to use F statstic How to compute and interpret Using FTEST and FDIST functions How to use the ANOVA. Analysis of variance.

229 views • 22 slides

Expectation Maximization

Expectation Maximization. Dekang Lin Department of Computing Science University of Alberta. Objectives. Expectation Maximization (EM) is perhaps most often used and mostly half understood algorithm for unsupervised learning. It is very intuitive.

582 views • 20 slides

CRIM 483

CRIM 483. Analysis of Variance. Purpose. There are times when you want to compare something across more than two groups For instance, level of education, SES, age groups, etc.

261 views • 16 slides

The Broad-Spectrum of Complete Total Permanent Disability in

145 views • 2 slides