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Frequently Asked Questions
  • Home
  • Contact Us
  • Online Course
    • 3D Printing Applications
    • Accounting: Principles of Financial Accounting
    • Amazon DynamoDB: Building NoSQL Database-Driven Applications
    • Analyzing Big Data with SQL Quiz Answer
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    • Big Data, Artificial Intelligence, and Ethics
    • Blockchain and Cryptocurrency Explained
    • Build Your First Android App Quiz (Project-Centered Course)
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    • Cost and Economics in Pricing Strategy
    • Customer Value in Pricing Strategy
  • All Assignments
    • Week 5 Assignments of Data Warehouse Concepts Design and Data Integration
    • Peer graded Assignment Personas Problem Scenarios and Propositions and User Stories
    • Peer-graded Assignment: Positioning Statement & Focal Questions
    • Peer-graded Assignment: Creating and Testing a Demand/Value Hypothesis
    • Supply Chain Logistics Week 4 Peer-Graded Assignment Image Link:
    • Week – 4 Peer-graded Assignment: Build a Logistics Network
    • Supply Chain Logistics Week 2 Peer-Graded Assignment Image
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    • The 48 Best Free Online Spanish Courses To Learn
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Fitting Statistical Models to Data with Python

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Now, lets say that I observe two more people and I see that they also have IQs of 110. So we have three people with IQs of 110. How does the variance of my estimate change from my prior? We can do this in the code by setting:
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If I observe now five people: the first three have an IQ of 110, and the last two have an IQ of 125, which of the following are true?
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What is the posterior mean after observing three people with an IQ of 110 in a row?
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For this problem, we are going to be using the above code to recreate some of the mathematics behind the Introduction to Bayesian Statistics lecture. The math has already been worked out for you, so you will only have to manipulate code, but if you are curious of the math behind the update for the mean of a distribution, you can look here:The math for this problem is located under the continuous distributions section where our model parameter is mu and we have a known variance sigma^2
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Let’s say that we observe a person with an IQ of 125, as we did in the lecture. Which way should the posterior distribution, after our Bayesian update, shift?
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Which of the following is NOT true regarding reasons why we fit marginal models?
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Multilevel models are expressed in terms of ________.
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What is true about multiple linear regression and marginal linear models when dependence is present in data?
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Which of the following features has the highest correlation between two observations in the same cluster?
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What is clustered data?
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