# IUPUI School of Engineering and Technology

## Computer Information & Graphics Technology

### Quantitative Analysis II

#### CIT 22000 / 3 Cr.

A continued investigation into the problem solving tools and techniques that focus on both hardware systems and quantitative data analysis. The course is designed for CIT majors in their second full year of study.

##### Outcomes

Course Outcomes ﻿(What are these?)

• Apply critical thinking and analytical skills (CIT b)
• Express situations in mathematical terms (CIT a)
• Express situations in descriptive statistical terms (CIT a)
• Analyze situations from a descriptive statistical view (CIT a)

CIT Student Outcomes ﻿(What are these?)

(a) An ability to apply knowledge of computing and mathematics appropriate to the program’s student outcomes and to the discipline.

(b) An ability to analyze a problem, and identify and define the computing requirements appropriate to its solution.

##### Topics
• Use of Statistics
• Grouping and Displaying Data to Convey Meaning
• Measures of Central Tendency and Dispersion in Frequency Distributions
• Probability: Introductory Ideas
• Probability: Distributions
• Sampling and Statistical Distributions
##### Principles of Undergraduate Learning (PULs)

1b. Identify and propose solutions for problems using quantitative tools and reasoning.

1c. Make effective use of information resources and technology.

5. Understanding Society and Culture

##### What You Will Learn

Use of Statistics

• Examine who really uses statistics
• Examine how statistics is used
• Provide a very short history of the use of statistics
• Introduction and basic use of statistical software

Grouping and Displaying Data to Convey Meaning

• Show the difference between samples and populations
• Convert raw data to useful information
• Construct and use frequency distributions
• Graph frequency distributions
• Use frequency distributions to make decisions

Measures of Central Tendency and Dispersion in Frequency Distributions

• Use summary statistics to describe collections of data
• Use the mean, median, and mode to describe how data "bunch up"
• Use the range, variance, and standard deviation to describe how data "spread out"
• Examine computer-based exploratory data analysis to see other useful ways to summarize data

Probability: Introductory Ideas

• Examine the use of probability theory in decision making
• Explain the different ways probabilities arise
• Develop rules for calculating different kinds of probabilities

Probability Distributions

• Introduce the probability distributions most commonly used in decision making
• Use the concept of expected value to make decisions
• Show which probability distribution to use and how to find its values
• Understand the limitations of each of the probability distributions used

Sampling and Statistical Distributions

• Take a sample from an entire population and use it to describe the population
• Random Sampling Techniques
• Approximating binomial distributions with normal distributions