CCOG for ALM 243B Spring 2024
 Course Number:
 ALM 243B
 Course Title:
 STAT 243 Lab  1 Credit
 Credit Hours:
 1
 Lecture Hours:
 0
 Lecture/Lab Hours:
 0
 Lab Hours:
 30
Course Description
Addendum to Course Description
This class is not intended to be a study hall for students to work on STAT assignments. The time needs to be spent working on material designated by your ALM instructor. If a student is coenrolled in a STAT class, then this may include targeted materials which are intended to support the concepts being taught in that STAT class.
Intended Outcomes for the course
Upon completion of the course students should be able to:

Perform appropriate statistical and/or mathematical computations for a variety of situations either by hand or using an approved technology.

Apply statistical problem solving strategies in limited contexts.

Address statistical problems with increased confidence.

Demonstrate progression through learning objectives established between the student and instructor.
Course Activities and Design
Instructors may employ the use of worksheets, textbooks, online software, minilectures, and/or group work.
Outcome Assessment Strategies
Assessment shall include at least two of the following measures:
1. Active participation/effort
2. Personal program/portfolios
3. Individual student conference
4. Assignments
5. Pre/post evaluations
6. Tests/Quizzes
Course Content (Themes, Concepts, Issues and Skills)
Items from the course content may be chosen as appropriate for each student and some students may even work on content from other ALM courses as deemed appropriate by the instructor.
Statistics I (STAT 243) – (Taken from the STAT 243Z CCOG)
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
Optional topics may include:
 Experimental design
 Linear Regression and the coefficient of determination (\(r^2\))
 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
ALM ADDENDUM:
The mission of the MTH/STAT ALM is to promote student success in MTH/STAT courses by tailoring the coursework to meet individual student needs.
Specifically, the ALM course:

supports students concurrently enrolled in MTH/STAT courses;

prepares students to take a MTH/STAT course the following term;

allows students to work through the content of a MTH/STAT course over multiple terms;