BUSN311

My BUSN311 U2 was incorrect, please ettemplate wth the figures

Assignment 2
Title:
Unit2 – intellipath
Type:

 

Deliverable Length:

 

See Assignment Details
Due Date:

 

6/23/2014
Possible Points:

 

120
Description:

 

Unit 2:

Unit 2

There are 3 steps to complete this task.

Step 1: Determine Knowledge (Complete this step by Wednesday)

  • Click on the intellipath icon in the Left Navigation Bar of your Classroom under the Course Work section to begin.
  • A landing page will appear, scroll down and Click on Start intellipath.
  • Choose Determine Knowledge from the Steps Tab on the left-side of your screen.

Step 2: Learning Path

  • Once you complete the Determine Knowledge follow the instructions to begin working on your Learning Path.
  • Your path will only consist of learning nodes that you need to work on and is individualized for you.
  • You will receive feedback as you work through your path.

Step 3: Practice & Revision

As you are working in your Learning Path you may need to spend additional time in specific areas to improve. You can do this by practicing and revising. Review this document. Click Intellipath for intellipath Suggestions to help guide you on how to Practice and Revise to increase your Knowledge State.

How do I make the most out of the intellipath Experience?

Each week you will want to improve your Knowledge State. You can do this by practicing and revising your work in your learning path. You can improve every phase throughout the entire course.

Choose to Practice when you have a good understanding of the material, but still have room for improvement.

Revise a node when you feel like you need to learn more on a topic. Click on the Revise Button to begin a new lesson on a specific node.

Grading Criteria:

 

Grading by intellipath
Measurable Terminal Course Objectives:

 

There are no TCOs for this Learning Event.

 

Feature List:

  • LearningMaterials
Live Session Talking Points:
  • Event
    • Defined
      • Outcome of a probability experiment
    • Sample Space
      • Collection of all events
      • List of all possible outcomes
  • Properties of Probability
    • Individual Probabilities
      • Each individual probability must be between 0 and 1 inclusive
    • Sum of Probabilities
      • The entire sample space of probabilities must always sum to 1
  • Classical Probability
    • Requires equally likely outcomes
      • Each event’s probability is the same
    • Number of ways event can occur/ Number of possible outcomes
    • No probability experiment is performed
    • “TRUE” probability
  • Empirical Probability
    • Probability experiment is performed
    • Frequency of event / number of trials of experiment
    • Estimated value as each probability experiment will produce different results
  • Rules
    • Complement
      • Probability the event did not happen
      • One minus the probability event did happen
    • Addition Rule
      • “OR” probability of multiple events
    • Multiplication Rule
      • “AND” probability of multiple events
  • Conditional Probability
    • Given information ahead of time
      • Information can change the probability results
    • Uses | as given sign
    • Used with dependent events
  • Random Variables
    • Variable: used to represent a value associated with a probability outcome
    • Types
      • Discrete
        • Countable with distinct separation between each
      • Continuous
        • Infinite or with upper bounds that take on a range of values
  • Discrete Probability Distribution
    • Distribution
      • Measurement of probability data set
      • Consist of probability values between zero and one, inclusive
      • Probabilities must add to one
  • Binomial vs. Poisson
    • Both discrete probability distributions
    • Binomial
      • Two possible outcomes
      • Find number of successes in a fixed number of trials
    • Poisson
      • Interval probability
      • Find probability occurred in a particular interval of time
  • Expectation
    • Expected Value
      • Experiment ran over and over
      • In the long run what would be expected
      • Multiply each value of random variable by particular probability and sum
  • Normal Distribution
      • Continuous Random Variable
      • Symmetrical
        • Mean, Median, Mode all equal
      • Standardized data for Normal table lookup
        • Mean = 0
        • Standard Deviation = 1
Unit Resources:

Probability and Distributions

 

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