# C-ID Descriptor Introduction to Statistics

## Descriptor Details

• Introduction to Statistics
• Not Identified
• 110
• Not Identified
• 3.0
• 0
• Uploaded: 10/12/2017 04:44:05 PM PDT

## General 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 a broad range of disciplines.

## Prerequisites

Intermediate Algebra or any CSU accepted* statistics pathway curriculum prerequisite

*At present there are two mechanisms to become accepted:

• the proposed statistics course has been accepted to meet CSU General Education Breadth Area B4
• the pathway has been accepted by the CSU Chancellor's Office process per its October 20, 2015 memo (Statistics Pathways in CSU Quantitative Reasoning)

## Content

1. Summarizing data graphically and numerically;
2. Descriptive statistics:  measurement, measures of central tendency, and variation;
3. Sample spaces and probability;
4. Random variables and expected value;
5. Sampling and sampling distributions;
6. Discrete distributions – Binomial;
7. Continuous distributions – Normal;
8. The Central Limit Theorem;
9. Estimation and confidence intervals;
10. Hypothesis Testing and inference, including t-tests for one and two populations, and Chi-square test;
11. Correlation, regression lines, and analysis of variance (ANOVA);
12. Applications using data from at least four of the following disciplines: business, economics, social science, psychology, political science, administration of justice, life science, physical science, health science, information technology, and education; and
13. Technology based statistical analysis.

## Objectives

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

1. Interpret data displayed in tables and graphically;
2. Apply concepts of sample space and probability;
3. Calculate measures of central tendency and variation for a given data set;
4. Identify the standard methods of obtaining data and identify advantages and disadvantages of each;
5. Calculate the mean and variance of a discrete distribution;
6. Calculate probabilities using normal and t-distributions;
7. Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem;
8. Construct and interpret confidence intervals;
9. Determine and interpret levels of statistical significance including p-values;
10. Interpret the output of a technology-based statistical analysis;
11. Identify the basic concept of hypothesis testing including Type I and II errors;
12. Formulate hypothesis tests involving samples from one and two populations;
13. Select the appropriate technique for testing a hypothesis and interpret the result;
14. Use regression lines and ANOVA for estimation and inference, and interpret the associated statistics; and
15. Use appropriate statistical techniques to analyze and interpret applications based on data from at least four of the following disciplines:  business, economics, social science, psychology, political science, administration of justice, life science, physical science, health science, information technology, and education.

## Evaluation Methods

Tests, examinations, homework or projects where students demonstrate their mastery of the learning objectives and their ability to devise, organize and present complete solutions to problems.

## Textbooks

A college level text supporting the learning objectives of this course.

• No
• Not Identified

• Not Identified

• Typically satisfies general education quantitative reasoning requirement (CSU GE B4).

• Not Identified

• Last vetting was from 9/16/12 to 10/31 approved 12/15/12

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