CCOG for STAT 243 Winter 2024
 Course Number:
 STAT 243
 Course Title:
 Elementary Statistics I (MTH/STAT243=STAT243Z)
 Credit Hours:
 4
 Lecture Hours:
 30
 Lecture/Lab Hours:
 0
 Lab Hours:
 30
Course Description
Addendum to Course Description
This is the first term of a twoterm sequence that is intended to provide an introduction to statistics in a databased setting.
Intended Outcomes for the course
Upon completion of this course students should be able to:
1. Critically read, interpret, report, and communicate the results of a statistical study along with evaluating assumptions, potential for bias, scope, and limitations of statistical inference.
2. Produce and interpret summaries of numerical and categorical data as well as appropriate graphical and/or tabular representations.
3. Use the distribution of sample statistics to quantify uncertainty and apply the basic concepts of probability into statistical arguments.
4. Identify, conduct, and interpret appropriate parametric hypothesis tests.
5. Assess relationships in quantitative bivariate data.
Quantitative Reasoning
Students completing an associate degree at Portland Community College will be able to analyze questions or problems that impact the community and/or environment using quantitative information.
General education philosophy statement
Mathematics and Statistics courses help students gain tools to analyze and solve problems using numerical and abstract reasoning. Students will develop their abilities to reason quantitatively by working with numbers, operations, and relations and to reason qualitatively by analyzing patterns and making generalizations.
Course Activities and Design
 Teach Statistical Thinking – Students should think of statistics as a problem solving and decision making process instead of a collection of formulas and methods.
 Focus on Conceptual Understanding – Students should primarily apply concepts rather than rely on computations. Focus on depth of content, not breadth of topics.
 Integrate Real data with context and purpose – Use data sets and or studies that are real and are relevant to student’s interests.
 Foster Active Learning – Use group work that allows for discussion and predictions rather than step by step procedures. Have students do basic physical simulations before computer driven simulations.
 Use Technology  Use technology and computer software to analyze and investigate larger data sets.
Outcome Assessment Strategies
Assessments worth at least 40% of the course grade will include both:
 An individual, proctored, closed book examination
 Either a second, individual, proctored, closed book examination OR a project culminating in a final product (ex. oral report, written report, video presentation, poster, slideshow, etc.)
Additionally, at least two of the following numbered measures:
 Exams and/or quizzes (group or individual)
 Projects
 Worksheets/graded homework
 Online homework
 Group or individual activities
 Lab reports
 Portfolios
Optional additional assessment strategies may include, but are not limited to
 Individual student conferences
 Discussions
 Participation
Course Content (Themes, Concepts, Issues and Skills)

Identify and describe common statistical terminology: descriptive statistics, inferential statistics, population, sample, variable, observational units, statistic, parameter, quantitative (numerical) data, qualitative (categorical) data, observational study, experiment
 Consider the quality and appropriateness of data collection methods

Identify and describe common sampling methods: voluntary response, convenience sampling, simple random sampling, stratified sampling, systematic sampling, cluster sampling, multistage sampling

Explore representativeness and the potential for bias

 Analyze qualitative (categorical) data from one and two variables

Compute statistics: proportion

Construct and interpret: (relative) frequency table, two way tables, bar graphs

Use two way tables to introduce probability, including joint, marginal, and conditional probabilities

Use conditional probability to check for independence

 Analyze quantitative (numerical) data from a single variable

Compute statistics using technology: mean, median, standard deviation, 5number summary, IQR

Construct and interpret: (relative) frequency table, dotplot, histogram, modified boxplot

Describe the shape of a distribution and identify outliers (if any)

Determine relative standing using zscores

Interpret the results of statistical analysis in context

Use illustrations and summaries to compare and contrast distributions

 Explore relationships between two quantitative (numerical) variables

Identify and describe: explanatory variable, response variable, correlation, residual

Construct a scatterplot using technology and assess the linear/non linear relationship between variables

Use technology to determine the correlation coefficient and interpret its meaning in context

Use technology to determine the line of best fit (least squares regression line) and use it to make predictions

Discuss cautions and limitations: lurking and confounding variables, correlation versus causation, extrapolation

 Explore the properties of normal distributions

Identify and describe: normal distribution, standard normal distribution, parameters

Use technology to perform calculations from a normal distribution

Use technology to determine the critical value from a standard normal distribution

 Explore and analyze sampling distributions

Identify and describe: parameter, statistic, random variable, sampling variability, binomial, Central Limit Theorem

Perform simulations to investigate sampling distributions (counts, proportions, means) and perform probability calculations

Determine when the Central Limit Theorem applies to a distribution

Use technology to perform probability calculations for sample means and sample proportions based on the Central Limit Theorem

Describe how sample size, shape of the population, population mean and standard deviation impact the distribution of sample means

 Create and interpret confidence intervals

Identify and describe: level of confidence, margin of error, standard error, critical values, student’s t distribution

Understand the construction and meaning of confidence intervals

Determine point and interval estimates of the population mean and population proportion using theoretical and/or simulation based methods

Interpret confidence intervals in context using correct units of measurement

Describe the relationship between sample size, level of confidence, and margin of error in the construction of confidence intervals

Given a specified confidence level, determine the minimum sample size required to attain a specified margin of error.

 Conduct, and interpret hypothesis tests

Identify and describe: null and alternative hypotheses, significance level, pvalue, statistical significance, test statistic

Understand the logic of hypothesis testing

Identify the appropriate test based on variable type

Use theoretical and/or simulation based methods to conduct one and two tailed tests of a single mean

Use theoretical and/or simulation based methods to conduct one and two tailed tests of a single proportion

Interpret test conclusions in context

Describe the potential for error in the decision making process

Distinguish the difference between statistical and practical significance

Investigate the relationship between hypothesis tests and confidence intervals

If time permits, the instructor may supplement the core course content with one or more of the following optional topics:
 Experimental design
 Linear Regression and the coefficient of determination (r2)
 Theoretical probability topics and models (Venn Diagrams, Trees, etc.)
 Discrete random variables and probability distributions
 Expected value and standard deviation of discrete random variables
 Bootstrapping
 PlusFour Method (for Confidence Intervals for a Proportion)
 Simulation based methods to conduct tests of two proportions
 Simulation based methods to conduct tests of two means