The student will be able to:
A. Examine statistical studies, methods of obtaining data, advantages and disadvantages of data collection techniques, and discuss an overview of the data analysis process.
B. Summarize and interpret data graphically and numerically.
C. Calculate measures of central tendency and variation.
D. Use correlation, regression and modeling to examine and interpret bivariate data.
E. Use and manipulate linear functions and expressions.
F. Use and manipulate exponential functions and expressions.
G. Apply concepts of probability and sample space.
H. Calculate the mean, the variance, and probabilities of discrete and continuous probability distributions.
I. Investigate statistical inference: sample and population distributions and the Central Limit Theorem.
J. Apply techniques of statistical inference to a single proportion: confidence intervals and tests of hypotheses, including levels of statistical significance and p-values and Type I and Type II errors.
K. Select and apply techniques of statistical inference to the difference between two population proportions: confidence intervals and tests of hypotheses and interpretation of results.
L. Select and apply techniques of statistical inference to means: confidence intervals and tests of hypotheses and interpretation of results.
M. Apply techniques of statistical inference to categorical data: Chi-squared tests.
N. Apply techniques of statistical inference to multiple means: ANOVA.
O. Use and interpret technology based statistical analyses.
P. Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines, including business, social sciences, psychology, life science, health science and education.
Q. Interpret mathematical solutions.

A. Examine statistical studies and discuss an overview of the data analysis process.
1. Types of statistical studies
a. Observational
b. Experimental
2. Sampling methodologies and bias
a. Simple random sampling
b. Stratified sampling
c. Systematic sampling
d. Convenience sampling
3. Experimental design
a. Random assignment
b. Lurking variables
c. Confounding variables
4. Data analysis process
a. Formulate question
b. Identify appropriate data
c. Select an appropriate data collection strategy
d. Collect, summarize, display data
e. Draw a conclusion
f. Interpret in context
5. Vocabulary
a. Variables
b. Population
c. Sample
d. Quantitative
e. Categorical
f. Study
g. Experiment
B. Summarize and interpret data graphically and numerically.
1. Graphical displays
a. Bar charts
b. Dot plots
c. Histograms
d. Box plots
2. Comparing distributions
a. Graphically
b. Numerically
C. Calculate measures of central tendency and variation.
1. Measures of central tendency
a. Mean
b. Median
c. Mode
2. Measures of variation
a. Range
b. Variance
c. Standard deviation
3. Measures of relative standing--quartiles
D. Use correlation, regression and modeling to examine and interpret bivariate data.
1. Scatter plots
a. Form
b. Interpretations
c. Residuals
2. Correlation
a. Strength
b. Direction
3. Linear regression
a. Interpretations
b. Extrapolation
c. Interpolation
4. Linear and exponential models
a. Interpret parameters
b. Make predictions
c. Multiple representations
1) Tables
2) Graphs
3) Symbolic form
d. Application problems
e. Comparing models
f. Residual plots
E. Use and manipulate linear functions and expressions.
1. 1-variable linear equations
a. Solve algebraically
2. 1-variable inequalities
a. Graphs
3. Linear functions
a. Slope
b. y-intercept
c. Equation of a line y=mx+b
d. Interpretations
1) Slope
2) y-intercept
F. Use and manipulate exponential functions and expressions.
1. Interpret exponential expressions
2. Apply properties of exponents
3. Interpret exponential equations graphically
G. Apply concepts of probability and sample space.
1. Empirical probability
2. Contingency tables
a. Conditional probability
b. Independence
c. Dependence
3. Probability rules
4. Sample space
H. Calculate the mean, the variance, and probabilities of discrete and continuous probability distributions.
1. Random variables and expected value
2. Discrete distributions
a. Mean
b. Standard deviation
c. Binomial
3. Continuous distributions
a. Equating areas with probabilities
b. Empirical Rule
c. Normal distribution
d. T-distributions
e. Application problems from various disciplines
I. Investigate statistical inference: Sample and population distributions and the Central Limit Theorem.
1. Sampling and sampling distributions
a. Mean
b. Standard deviation
c. Central Limit Theorem
2. Logical reasoning
J. Apply techniques of statistical inference to a single proportion: Confidence intervals and tests of hypotheses, including levels of statistical significance and p-values and Type I and Type II errors.
1. Confidence intervals
a. Point estimate
b. Interval estimate
c. Margin of error
d. Confidence level
e. Interpretation
2. Hypothesis tests
a. Null hypothesis
b. Alternate hypothesis
c. Test statistic
d. P-value
e. Decision rule
f. Interpretation
g. Type I and Type II errors
3. Application problems from various disciplines
K. Select and apply techniques of statistical inference to the difference between two population proportions: Confidence intervals and tests of hypotheses and interpretation of results.
1. Confidence intervals
2. Hypothesis tests
3. Application problems from various disciplines
L. Select and apply techniques of statistical inference to means: Confidence intervals and tests of hypotheses and interpretation of results.
1. One-sample confidence interval
2. Two-sample confidence interval
3. One-sample T-test
4. Two-sample T-test
5. Paired T-test
6. Application problems from various disciplines
M. Apply techniques of statistical inference to categorical data: Chi-squared tests.
1. Chi-squared goodness of fit test
2. Chi-squared test for independence
3. Chi-squared test for homogeneity
N. Apply techniques of statistical inference to multiple means: ANOVA.
1. One-way analysis of variance
2. Pair-wise comparisons
O. Use and interpret technology based statistical analyses.
1. Use appropriate technology to approximate binomial probabilities
2. Use appropriate technology to approximate normal probabilities
3. Use appropriate technology to compute summary statistics
4. Use appropriate technology to approximate critical values
5. Use appropriate technology to approximate p-values
P. Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines, including business, social sciences, psychology, life science, health science and education.
1. Application problems from various disciplines
2. Proper notation
Q. Interpret mathematical solutions.
1. Explain the significance of solutions to application problems

Not applicable.

A. Written homework
B. Quizzes
C. Midterms or module exams
D. Proctored comprehensive final examination
E. Project

A. Lecture
B. Discussion
C. Cooperative learning exercises

Carnegie Foundation. Statway Modules 1-11 Ver. 3.0. XanEdu, 2017. ISBN 9781506645995. Digital Access Code Only: ISBN 9781506684239.
Carnegie Foundation. Carnegie Math Pathways: carnegiemathpathways.org/
 

A. Homework problems covering subject matter from text and related material ranging from 30-60 problems per week. Students will need to employ critical thinking in order to complete assignments.
B. Reading and study of the textbook, related materials and notes.
C. Student projects covering subject matter from textbook and related materials. Projects will require students to discuss mathematical problems, write solutions in accurate mathematical language and notation and interpret mathematical solutions. Projects may require the use of technology.
D. Worksheets: Problems and activities covering the subject matter. Such problems and activities will require students to think critically. Such worksheets may be completed both inside and/or outside of class.