IUPUI School of Engineering and Technology

IUPUI School of Engineering and Technology

Quantitative Analysis III

CIT 32000 / 3 Cr.

A continuation of statistical inference introduced in Quantitative Analysis II with emphasis on confidence intervals, hypothesis testing, analysis of variance, forecasting, including linear regression and correlation, and quality control as they apply to information technology.

  • SPSS
  • Excel

Course Outcomes (What are these?)

  • Enhance student quantitative reasoning skills through a study of inferential statistics (CIT a)
  • Improve students ability to express situations in mathematical terms and to design analytical experiments (CIT i)
  • Develop student awareness of how and when to make quantitative assumptions and simplification (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.

(i) An ability to use current techniques, skills, and tools necessary for computing practice.

  • Hypothesis Testing-One Sample Tests
  • Hypothesis Testing-Two Sample Tests
  • Chi-Square and Analysis of Variance
  • Simple Regression Analysis and Correlation
  • Time Series and Forecasting
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.

3. Integration and Application of Knowledge

4. Intellectual Depth, Breadth, and Adaptiveness

What You Will Learn

Testing Hypotheses: One-Sample Tests

  • Learn how to use samples to decide whether a population possesses a particular characteristic
  • Determine how unlikely it is that an observed sample could have come from a hypothesized population
  • Understand the two types of errors possible when testing hypotheses
  • Learn when to use one-tailed tests
  • Learn when to use two-tailed tests
  • Learn the five-step process of testing hypotheses
  • Understand how and when to use the normal distributions for testing hypotheses about population means and proportions

Testing Hypotheses: Two-Sample Tests

  • Learn how to use samples from two populations to test hypotheses about how the populations are related
  • Learn how hypothesis tests for differences between population means take different forms, depending on whether the samples are large or small
  • Distinguish between independent and dependent samples when comparing two means
  • Learn how to reduce a hypothesis test for the difference of means from dependent samples to a test about a single mean
  • Learn how to test hypotheses that compare the proportions of two populations having some attribute of interest
  • Understand how prob values can be used in testing hypotheses
  • Get a feel for the kinds of outputs computer statistical packages produce for testing hypotheses

Chi-Square and Analysis of Variance

  • Recognize situations requiring the comparison of more than two means or proportions
  • Introduce the chi-square and F distributions and learn how to use them in statistical inferences
  • Use the chi-square distribution to see whether two classifications of the same data are independent of each other
  • Use a chi-square test to check whether a particular collection of data is well described by a specified distribution
  • Use the chi-square distribution for confidence intervals and testing hypotheses about a single population variance
  • Compare more than two population means using analysis of variance
  • Use the F distribution to test hypotheses about two population variances

Simple Regression and Correlation

  • Learn how many business decisions depend on knowing the specific relationship between two or more variables
  • Use scatter diagrams to visualize the relationship between two variables
  • Use regression analysis to estimate the relationship between two variables
  • Use the least-squares estimating equation to predict future values of the dependent variable
  • Learn how correlation analysis describes the degree to which two variables are linearly related to each other
  • Understand the coefficient of determination as a measure of the strength of the relationship between two variables
  • Learn limitations of regression and correlation analyses and caveats about their use

Time Series and Forecasting

  • Learn why forecasting changes that take place over time are an important part of decision making
  • Understand the four components of a time series
  • Use regression-based techniques to estimate and forecast the trend in a time series
  • Learn how to measure the cyclical component of a time series
  • Compute seasonal indices and use them to deseasonalize a time series
  • Recognize irregular variation in a time series
  • Deal simultaneously with all four components of a time series
  • Use time-series analysis for forecasting