'Larger values' presentation slideshows

Sorting

Based on Chapter 10 of Koffmann and Wolfgang. Sorting. Chapter Outline. How to use standard sorting methods in the Java API How to implement these sorting algorithms: Selection sort Bubble sort Insertion sort Shell sort Merge sort Heapsort Quicksort. Chapter Outline (2).

847 views • 65 slides

Chapter 2. Random Variables

Chapter 2. Random Variables. 2.1 Discrete Random Variables 2.2 Continuous Random Variables 2.3 The Expectation of a Random Variable 2.4 The Variance of a Random Variable 2.5 Jointly Distributed Random Variables 2.6 Combinations and Functions of Random Variables. -3. -2. -1. 1. 0. 2. 3.

948 views • 59 slides

Continuous Probability Distributions

Exponential. f ( x ). Uniform. f ( x ). Normal. f ( x ). x. x. x. Continuous Probability Distributions. Uniform Probability Distribution Normal Probability Distribution Exponential Probability Distribution. Continuous Probability Distributions.

919 views • 55 slides

Chapter 3 part B

Chapter 3 part B. Probability Distribution. f ( x ). f ( x ). x. x. Chapter 3, Part B Probability Distributions. Uniform Probability Distribution Normal Probability Distribution Exponential Probability Distribution. Exponential. Uniform. Normal. f ( x ). x.

553 views • 43 slides

8. Association between Categorical Variables

8. Association between Categorical Variables. Suppose both response and explanatory variables are categorical. (Chap. 9 considers both quantitative .)

550 views • 36 slides

8. Association between Categorical Variables

8. Association between Categorical Variables. Suppose both response and explanatory variables are categorical. (Chap. 9 considers both quantitative .)

531 views • 36 slides

How Competition Shapes the Creation and Distribution of Economic Value

605 views • 35 slides

Clustering Methods

Clustering Methods. For the Study of Transcription Regulatory Networks. Mehak Aziz. Recap: What is Clustering?. Clustering is the classification of a set of data or objects into subsets (clusters) so that the data in each subset (ideally) share some common trait. . Applications.

867 views • 34 slides

8. Association between Categorical Variables

8. Association between Categorical Variables. Suppose both response and explanatory variables are categorical. (For comparing means in Chap. 7, response variable is quantitative, explanatory variable is categorical. Chap. 9 considers both quantitative .)

483 views • 33 slides

Teaching Business Mathematics II without formulas

Teaching Business Mathematics II without formulas. Oded Tal School of Business Conestoga College. Outline. Rationale and goal Challenges and solutions Solving compound interest questions Solving for effective and equivalent interest rates Solving annuities Solving perpetuities

511 views • 32 slides

Trade Talks Among BRICS Economies: Mapping out Threats and Opportunities for the Brazilian economy

531 views • 28 slides

Correlating traits with phylogenies

Correlating traits with phylogenies. Using BaTS. Phylogeny and trait values. A phylogeny describes a hypothesis about the evolutionary relationship between individuals sampled from a population Discrete character traits of interest can be mapped onto the phylogeny

386 views • 23 slides

Binary Search Trees

Binary Search Trees . Eileen Kraemer CSCI 2720 The University of Georgia Spring 2007. Overview. Binary trees Binary search trees Find Insert Delete. Hierarchical Data Structures. One-to-many relationship between elements Tree Single parent, multiple children Binary tree

477 views • 23 slides

CS344: Principles of Artificial Intelligence

CS344: Principles of Artificial Intelligence. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 11, 12: Perceptron Training 30 th and 31 st Jan , 2012. The Perceptron Model

334 views • 23 slides

Hidden Markov Models - Training

Hidden Markov Models - Training . Parameter Estimation. How to specify a HMM mode? Design the structure: states, transitions, etc. Assign the parameter values: the transition and emission probabilities a kl and e k (b). Parameter Estimation.

389 views • 16 slides

Logistic Regression Example: Horseshoe Crab Data

182 views • 15 slides

Business Research Methods

Business Research Methods Lecture 6 by Selim Bora Graphs The distribution of a variable tells us what values it takes and how often it takes these values. To see what data say, start with graphs. Pie chart and bar graphs are two illustrative types.

1.1k views • 9 slides

Regression Diagnostics

Regression Diagnostics. Regression Diagnostic asks 3 questions: Are the assumptions of multiple regression complied with? Is the model adequate? Is there anything unusual about any data points?. Checking for Non-violation of Assumptions.

328 views • 9 slides

The Relationship of Composted and Uncomposted Indianola Soil to Chemical Composition of Broccoli Inflorescences

The Relationship of Composted and Uncomposted Indianola Soil to Chemical Composition of Broccoli Inflorescences. By Christopher Licata & B. Adams. Model Randomized split-block design with one replicate Two treatments (composted vs. unamended) 4 reps per treatment.

180 views • 9 slides

The Structure of the Taurus Molecular Cloud Complex

The Structure of the Taurus Molecular Cloud Complex

82 views • 1 slides