Course Overview

This syllabus outlines the structure, policies, and content for MAT143: Introduction to Statistics at North Shore Community College. The course provides a comprehensive introduction to both descriptive and inferential statistics, emphasizing theoretical understanding, practical application, and the use of statistical software where appropriate.

Course Structure and Policies

• Course Delivery: Face-to-face instruction; attendance and participation are required.

• Calculator Policy: Only basic arithmetic functions allowed; no use of statistical calculator functions during exams.

• Software: StatCrunch may be used for selected homework problems as directed by the instructor.

• Academic Integrity: Use of AI or unauthorized technology is strictly prohibited.

• Assessment: Homework (online and written), quizzes, midterm, and final exam. All assessments require demonstration of calculation steps and reasoning.

Major Topics and Learning Objectives

The course covers the following main topics, aligned with standard introductory statistics curricula:

Descriptive Statistics: Measures and Graphs

• Types of Data: Understanding qualitative vs. quantitative data, discrete vs. continuous variables.

• Sampling Methods: Simple random, stratified, cluster, and systematic sampling; identifying potential biases.

• Graphical Representations: Bar plots, histograms, time series, scatter plots, pie charts, dot plots, and box plots.

• Measures of Center: Mean, median, mode.

• Measures of Variation: Range, variance, standard deviation, interquartile range.

• Relative Standing: Percentiles, quartiles, z-scores.

• Five Number Summary: Minimum, Q1, median, Q3, maximum.

• Software Application: Using StatCrunch for data analysis and visualization.

Probability and Discrete Probability Distributions

• Basic Probability: Properties of probability, sample spaces, events.

• Probability Calculation Methods: Relative frequency and classical approaches.

• Addition and Multiplication Rules: For mutually exclusive and independent events.

• Random Variables: Discrete vs. continuous.

• Binomial Distribution: Calculating probabilities using the binomial formula.

• Poisson Distribution: Calculating probabilities for rare events.

• Software Application: Using StatCrunch for probability calculations.

Normal Probability Distributions

• Standard Normal Distribution: Calculating probabilities and percentiles using z-scores.

• Non-Standard Normal Distribution: Transforming to standard normal and finding probabilities.

• Central Limit Theorem: Understanding sampling distributions of the sample mean.

• Software Application: Using StatCrunch for normal probability calculations.

Estimating Population Parameters (Confidence Intervals)

• Population Proportion: Calculating point estimates, margin of error, and constructing confidence intervals.

• Population Mean: Constructing confidence intervals using z or t distributions as appropriate.

• Population Variance/Standard Deviation: Using the chi-square distribution for interval estimation.

• Sample Size Determination: Calculating required sample sizes for desired precision.

• Software Application: Using StatCrunch for confidence interval construction.

Hypothesis Testing

• Formulating Hypotheses: Null and alternative hypotheses for population parameters.

• Test Statistics: Calculating and interpreting z, t, and chi-square statistics.

• P-values: Understanding and interpreting significance levels.

• Type I and II Errors: Definitions and implications.

• Testing Claims: About population proportions, means, and variances.

• Software Application: Using StatCrunch for hypothesis testing.

Course Schedule (Summary Table)

Key Formulas and Concepts (Selected)

• Mean:

• Variance:

• Standard Deviation:

• Binomial Probability:

• Standard Normal (z-score):

• Confidence Interval for Mean (known ):

• Confidence Interval for Proportion:

• Test Statistic (z):

• Chi-Square Statistic:

Grading Criteria

Grading Scale

Support and Resources

• College Tutoring Services

• Blackboard and MyLab-Stat Helpdesks

• StatCrunch tutorials and resources

• Accessibility Services for students with disabilities

Additional info:

• This syllabus aligns closely with the standard topics for an Introductory Statistics course, including all foundational concepts from data collection and description to probability, distributions, estimation, and hypothesis testing.

• Topics such as correlation, regression, chi-square tests, and ANOVA may be covered as time permits, consistent with the course outline.