Course Syllabus

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Course Description:

 

The use of probability techniques, hypothesis testing, and predictive techniques to facilitate decision-making. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-square and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings.  Applications using data from disciplines including business, social sciences, psychology, life science, health science, and education.

 

Syllabus:

Syllabus_Math146_Statistics_SP2023.pdf

Student Learning Outcomes:

 

Upon successful completion of the course, students will be able to:

  • Distinguish among different scales of measurement and their implications;
  • Interpret data displayed in tables and graphically;
  • Apply concepts of sample space and probability;
  • Calculate measures of central tendency and variation for a given data set;
  • Identify the standard methods of obtaining data and identify advantages and disadvantages of each;
  • Calculate the mean and variance of a discrete distribution;
  • Calculate probabilities using normal and student’s t-distributions;
  • Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem;
  • Construct and interpret confidence intervals;
  • Determine and interpret levels of statistical significance including p-values;
  • Interpret the output of a technology-based statistical analysis;
  • Identify the basic concept of hypothesis testing including Type I and II errors;
  • Formulate hypothesis tests involving samples from one and two populations;
  • Select the appropriate technique for testing a hypothesis and interpret the result;
  • Use linear regression and ANOVA analysis for estimation and inference, and interpret the associated statistics; and
  • Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including business, social sciences, psychology, life science, health science, and education.

Course Content:

 

  • Summarizing data graphically and numerically;
  • Descriptive statistics:  measures of central tendency, variation, relative position, and levels/scales of measurement;
  • Sample spaces and probability;
  • Random variables and expected value;
  • Sampling and sampling distributions;
  • Discrete distributions – Binomial;
  • Continuous distributions – Normal;
  • The Central Limit Theorem;
  • Estimation and confidence intervals;
  • Hypothesis Testing and inference, including t-tests for one and two populations, and Chi-square test;
  • Correlation and linear regression and analysis of variance (ANOVA);
  • Applications using data from disciplines including business, social sciences, psychology, life science, health science, and education; and
  • Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.

Textbook:

Great news: your textbook for this class is available for free online!
Statistics from OpenStax, ISBN 1-947172-05-0

You have several options to obtain this book:

You can use whichever formats you want. Web view is recommended -- the responsive design works seamlessly on any device.

 

Course Summary:

Date Details Due