CSE 331 Software Design & Implementation Winter 2021 Homework Overview

CSE 331 Software Design & Implementation Winter 2021 Homework Overview
Slide Note
Embed
Share

In this winter 2021 course, students are working on various assignments including HW9 using technologies like Spark Java and React. The assignments involve creating web GUIs, implementing Java servers, and dealing with JSON data. The course covers topics such as Anonymous Inner Classes and Campus Paths. Students are encouraged to be creative and explore different coding opportunities. The assignments are designed to enhance skills in software design and implementation.

  • CSE 331
  • Software Design
  • Winter 2021
  • Homework
  • Spark Java

Uploaded on Apr 13, 2025 | 0 Views


Download Presentation

Please find below an Image/Link to download the presentation.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.

You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.

E N D

Presentation Transcript


  1. HUDM4122 Probability and Statistical Inference February 2, 2015

  2. Special Session on SPSS Thursday, April 23 4pm-6pm As of when I closed the poll, every student except one could make it to this I am happy to meet individually with students who can t make this session

  3. And people say pie charts arent informative From Jeanine DeFalco

  4. Homework 1 How did it go? How did you like working with the ASSISTments system? Too few problems? Too many? Just right?

  5. What the homework covered Computing the mean, median, mode Symmetric and skewed distributions Variance Standard Deviation

  6. Difficulties with rounding Sorry about that I ll try to be clearer next time

  7. Difficult Problems 3. You are given n=8 measurements: 3, 2, 5, 6, 4, 4, 3, 4. What is the median? We had answers 4, 4.5, 5 Anyone want to explain any of these answers?

  8. Difficult Problems 9. You are given 6 measurements: 5, 4, 4, 6, 8, 6. Calculate the sample variance, s2 We had answers 1.9167, 2.3, 2.5, 3.1, 3.3, 11.5 Anyone want to explain any of these answers?

  9. Questions? Comments?

  10. Beyond these topics, in the last class We looked at how to create and interpret Box Plots And discussed Bimodal Distributions Mean Absolute Deviation Percentiles Z scores

  11. Questions? Comments?

  12. Today Ch. 3 in Mendenhall, Beaver, & Beaver

  13. Today Scatterplots Covariance The Pearson Correlation Coefficient Regression Lines

  14. Univariate Data A single variable is collected Height 5 11 5 11 5 10 5 6

  15. Bivariate Data Two variables are collected (for the same data point) Height Drum-Playing Skill 5 11 1 5 11 2 5 10 4 5 6 8

  16. Multivariate Data 3+ variables are collected Name Height Drum-Playing Skill John Lennon 5 11 1 Paul McCartney 5 11 2 George Harrison 5 10 4 Ringo Starr 5 6 8

  17. Last Class Univariate Data

  18. Today Bivariate Data

  19. Scatterplot Shows the relationship between two variables

  20. Are more expensive brands of peanut butter better? From InterMath intermath.coe.uga.edu

  21. Dependent and Independent Variables Dependent Variable From InterMath intermath.coe.uga.edu Independent Variable

  22. The Independent Variable Influences the Dependent Variable (Maybe) Dependent Variable From InterMath intermath.coe.uga.edu Independent Variable

  23. (You dont always have to be sure) Dependent Variable From InterMath intermath.coe.uga.edu Independent Variable

  24. Data Miners Would Instead Say Predictor and Predicted Variables Predicted Variable From InterMath intermath.coe.uga.edu Predictor Variable

  25. I like this terminology better because it s neutral on causation Predicted Variable From InterMath intermath.coe.uga.edu Predictor Variable

  26. Anyways From InterMath intermath.coe.uga.edu

  27. So which brand of peanut butter should you buy? From InterMath intermath.coe.uga.edu

  28. Which brand of peanut butter should a gourmet buy? From InterMath intermath.coe.uga.edu

  29. Which brand of peanut butter should a gourmet buy? From InterMath intermath.coe.uga.edu

  30. How about a frugal person? From InterMath intermath.coe.uga.edu

  31. How about a frugal person? From InterMath intermath.coe.uga.edu

  32. Who should buy this peanut butter? From InterMath intermath.coe.uga.edu

  33. How about this one? From InterMath intermath.coe.uga.edu

  34. A lot of variability, right? From InterMath intermath.coe.uga.edu

  35. Questions? Comments?

  36. Lets discuss some of the properties of scatterplots

  37. What can you say about the relationship between Price and Quality? Snorgles 300 250 200 Quality 150 100 50 0 0 50 100 150 Price 200 250 300

  38. What can you say about the relationship between Price and Quality? Frungles 250 200 150 Quality 100 50 0 0 50 100 150 Price 200 250 300

  39. What can you say about the relationship between Price and Quality? Trandles 200 180 160 140 120 Quality 100 80 60 40 20 0 0 50 100 150 Price 200 250 300

  40. So in other words Spend your hard earned dollars on expensive snorgles But save your money on frungles and trandles

  41. Questions? Comments?

  42. Quick comment on scatterplots Scatterplots are great

  43. Quick comment on scatterplots Scatterplots are great

  44. Quick comment on scatterplots But they don t scale to really big data sets If your scatterplot just looks like a giant blob or a grid, try a heat map We won t go into detail on heat maps there s a lot to cover today -- but I wanted to put that in your brains

  45. Linear functions All these graphs can be described by linear functions, a.k.a. straight lines

  46. Linear functions All these graphs can be described by linear functions, a.k.a. straight lines Snorgles 300 250 200 Quality 150 100 50 0 0 50 100 150 Price 200 250 300

  47. Figuring out what the best-fitting line is Is the simplest case of linear regression Linear regression is a sophisticated statistical modeling method Focus of HUDM5122 This is just the simplest application of it

  48. Linear Regression: X and Y If the two variables have a linear (straight line) relationship Then we can predict Y s value from X

  49. Finding Y from X If you buy a new snorgle that costs $200, what is its quality likely to be? Snorgles 300 250 200 Quality 150 100 50 0 0 50 100 150 Price 200 250 300

  50. Finding Y from X If you buy a new snorgle that costs $120, what is its quality likely to be? Snorgles 300 250 200 Quality 150 100 50 0 0 50 100 150 Price 200 250 300

More Related Content