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Applied Statistics Course: MAT162 at Central Arizona College, Study notes of Statistics

Central Arizona CollegeStatistics

Information about central arizona college's mat162 applied statistics course. The course catalog description, credit hours, effective term, teaching methods, learning outcomes, and standards. The course covers statistical methods, graphical and numerical summaries, hypothesis testing, and confidence intervals, among other topics.

Typology: Study notes

Pre 2010

Uploaded on 08/16/2009

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Central Arizona College
8470 N. Overfield Road
Coolidge, AZ 85228
Phone: (520) 494-5206 Fax: (520) 494-5212
MAT162 Applied Statistics
Catalo
g
Description:
Graphical and quantitative description of data; binomial,
normal and t distributions; one and two sample hypothesis
tests and confidence intervals; simple linear regression
and correlation.
Credit Hours: 3
Effective Term: Fall 2008
Terms Offered: All Terms
Teaching Methods: Lecture
Modalities: Face to Face Hybrid ITV Online
Prerequisite(s): MAT101 or MAT121; RDG094
Corequisite(s): None
Times for Credit: 1
Grading Option: A/F
Credit Breakdown: 3 Lectures
Cross Listed: No
Learnin
g
Outcomes:
1. Explain simple statistical methods commonly used in
reporting polling data and scientific research studies using
correct statistical notation and appropriate language.
2. Construct informative graphical and numerical
summaries of data appropriate for the type of data and the
context in which the data was collected.
3. Interpret the meaning of graphical and numerical
summaries of data in written terms appropriate to the
context in which the data was collected.
4. Recognize and properly carry out parameter estimation
and hypothesis testing procedures with and without the
use of technology.
5. Discuss the formalism of parameter estimation and
hypothesis testing and how it relates to, supports and
advances the scientific method.
6. Apply parameter estimation and hypothesis testing to
pf3

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8470 N. Overfield Road Coolidge, AZ 85228 Phone: (520) 494-5206 Fax: (520) 494-

MAT162 Applied Statistics

Catalog Description:

Graphical and quantitative description of data; binomial, normal and t distributions; one and two sample hypothesis tests and confidence intervals; simple linear regression and correlation. Credit Hours: 3 Effective Term: Fall 2008 Terms Offered: All Terms

Teaching Methods: Lecture

Modalities: Face to Face^ Hybrid^ ITV^ Online Prerequisite(s): MAT101 or MAT121; RDG Corequisite(s): None Times for Credit: 1 Grading Option: A/F

Credit Breakdown: 3 Lectures

Cross Listed: No Learning Outcomes:

  1. Explain simple statistical methods commonly used in reporting polling data and scientific research studies using correct statistical notation and appropriate language.
  2. Construct informative graphical and numerical summaries of data appropriate for the type of data and the context in which the data was collected.
  3. Interpret the meaning of graphical and numerical summaries of data in written terms appropriate to the context in which the data was collected.
  4. Recognize and properly carry out parameter estimation and hypothesis testing procedures with and without the use of technology.
  5. Discuss the formalism of parameter estimation and hypothesis testing and how it relates to, supports and advances the scientific method.
  6. Apply parameter estimation and hypothesis testing to

MAT162 – Applied Statistics Page 2 of 3 solve problems utilizing the appropriate statistical methods.

  1. Recognize the limitations of statistical methods and discuss the appropriateness of use within a context.
  2. Empirically, theoretically and subjectively obtain the probability of an event.
  3. Utilize the normal distribution to calculate probability of event.
  4. Utilize the properties of the t-distribution, t-statistic and degrees of freedom to construct confidence intervals. Standards: 1. Explain and utilize basic vocabulary of the course including sampling, parameters, sample statistics, distributions, confidence intervals and hypothesis testing.
  5. Accurately create and interpret graphical displays.
  6. Compute and interpret regression lines within a given standard error.
  7. Accurately obtain the probability of an event occurring under multiple circumstances.
  8. Compute and analyze the results of confidence interval estimates and hypothesis tests for the mean of a small sample and a large sample.
  9. Compute and analyze the results of confidence interval estimates and hypothesis tests for a proportion (large sample).
  10. Compute and analyze the results of confidence interval estimates and hypothesis tests for a difference of means (independent).
  11. Compute and analyze the results of confidence interval estimates and hypothesis tests for a difference of means (paired data).
  12. Compute and analyze the results of confidence interval estimates and hypothesis tests for a difference of proportions (large samples, independent and paired).