MATH& 146 - Introduction to Stats

Credits: 5
Variable Credit Course: No

Lecture Hours: 55
Lab Hours: 0
Worksite/Clinical Hours: 0
Other Hours (LIA/Internships): 0

Course Description: This course is an introduction to probability and statistics using statistical inference as its theme. Topics include sampling techniques, probability and probability distributions, inferential methods including confidence intervals and hypothesis tests, regression and correlation. Designed to serve students of all interests requiring an introductory statistics course, including social science, business, and nursing majors. Statistical technology required.

Prerequisite: A grade of C or higher in MATH 97 or CCB 43; or placement into MATH& 146; or C or higher in MATH 96 or CCB 42 and co-enrollment in MATH 46; or placement into MATH 97 and co-enrollment with MATH 46.
Distribution Requirements:

Natural Sciences Distribution Requirement
Quantitative

General Education Requirements:

Fulfills Quantify General Education Requirement

Meets FQE Requirement: No
Integrative Experience Requirement: No

Student Learning Outcomes

Distinguish between categorical and quantitative data.
Use correct vocabulary to describe surveys, experiments, and observational studies.
Construct appropriate graphical displays of categorical and quantitative data.
Compute summary statistics for quantitative data.
Perform computations using the normal distribution.
Perform computations using probability distributions including the binomial, normal, and t-distribution. May also include the uniform, Poisson, and chi-square distributions.
Perform calculations using the Central Limit Theorem.
Construct confidence intervals from one- and two-variable data and use them to make inferences about parameters.
Make inferences about population parameters using hypothesis tests, from one- and two-variable data.
Construct and interpret a linear regression model on bivariate data.
Determine if linear correlation is statistically significant using the linear regression test.

Course Contents

Introduction to Statistics. Data. Design of Experiments.
Summarizing and Graphing Data. Frequency Distributions. Histograms. Statistical Graphs.
Describing Data. Measures of Center. Measures of Dispersion. Measures of Relative Standing.
Probability. Definitions and interpretation of basic probabilities. Addition Rule and Multiplication Rule. Bayes’ Theorem (optional). Counting (optional).
Discrete Probabilities. Basics of Probability Distributions. Binomial Distribution. Poisson Distribution (optional).
Normal Probability Distributions. Standard Normal Distribution. Applications of Normal Distributions. Sampling Distributions. Central Limit Theorem. Assessing Normality.
Estimates, Confidence Intervals, and Sample Sizes. Estimating Population Proportion. Estimating Population Mean. Estimating Population Variance and Standard Deviation (optional). Intervals about Two Proportions. Intervals about Two Means. Intervals about Two Standard Deviations (optional).
Hypothesis Testing. Fundamentals of Hypothesis Testing. Testing a Claim about a Proportion. Testing a Claim About a Mean. Testing a Claim about Standard Deviation or Variance (optional). Testing a Claim about Two proportions. Testing a Claim about Two Means (dependent and independent samples).
Linear Correlation and Regression. Linear correlation. Linear regression. Prediction Intervals (optional).

Instructional Units: 5