This course is equivalent to a one-semester, introductory, non-calculus-based, college course in statistics. Statistics blends the rigor, calculations and deductive thinking of mathematics with the real-world examples and problems of the social sciences, the decision-making needs of business and medicine and the laboratory method and experimental procedures of the sciences. What students learn in this course will help them prosper in the 21st century.

## Required Text

- “Stats: Modeling the World” (can be found at Pearson Textbook) [e-text portal? If so, provide hyperlink]

## Required Supplies

- Lined paper/ notebook (8.5-by-11).
- Three-ring binder highly recommended.
- Graphing calculator TI-84 (provided for those who do not own one)
- Writing utensil and highlighter

## Course Format and Procedures

One to two quizzes will be given each week, ranging in duration from 20 to 40 minutes. Homework is assigned daily and constitutes 5% of quarter grade. After each chapter (see course outline) is completed, there will be a “homework assessment,” which cumulatively will constitute the 5% of the quarter grade.

## Grading Procedures

- Quizzes = 95%.
- Mid-term exam: 10%

## Student and Parent Resources

- Instructor email (buyces@bpcsd.org), Google Classroom and Remind 101 app.

## Course Outline

### Part 1: Exploratory Data

**Chapter 1:** Stats Starts: Define statistics; examples of statistics activity; class survey

**Chapter 2:** Data: Identify the who, what, why, where and how of data classification of categorical versus quantitative variables.

**Chapter 3:** Displaying and Describing Categorical Data: Recognize categorical variables; display with a frequency table, bar chart, pie chart or contingency table; compare conditional and marginal percentages.

**Chapter 4:** Displaying Quantitative Data: Recognize quantitative variables; display with stem and leaf plots, dot plots, histograms or time plots; describe distributions (gaps or clusters, shape, outliers, center, spread).

**Chapter 5:** Describing Distributions Numerically: Select and calculate suitable measure of center and spread for quantitative variables; learn basic properties of mean, median and standard deviation; calculate mean, median, 5-number summary, standard deviation and interquartile range (IQR).

**Chapter 6:** The Standard Deviation and the Normal Model: Calculate and interpret z-score; recognize appropriateness of a “normal model”; estimate probability through the use of normal models and the “empirical rule use” of z-table or appropriate technology to obtain percentages and probabilities.

**Chapter 7:** Scatter plots, Association and Correlation: Define explanatory and responsive variables; create scatter plots of data; compute correlation between variables; describe direction, form and strength of linear associations; interpret statistical software produced by correlation tables.

**Chapter 8:** Linear Regression: Identify regression equations, correlation, slope and intercept values from statistical software output and from summary statistics; predict a value of y of a given x from a regression equation; compute residuals; use residual plots to determine appropriateness of computing a regression.

**Chapter 9:** Regression Wisdom Identify high leverage points and influential points; explain the impact of leverage and influential points on the regression; identify lurking variables; address the danger of extrapolating beyond the range of x-values used in regression.

**Chapter 10:** Re-expression of Data: Model non-linear data activity. Explore exponential M+M’s

### Part 2: Experimental Design

**Chapter 11:** Understanding Randomness: Recognize random outcomes in real-world situations; design and perform simulations by generating random numbers on calculator or by using a random number table; draw conclusions from results of simulations.

**Chapter 12:** Sample Surveys: Distinguish between samples versus populations; understand the pros and cons of various sampling methods (e.g., simple random sample, systematic sample, stratified sampling, cluster sampling, multistage sample and convenience sample); understand the various biases and how they impact survey results and conclusions (e.g., voluntary response , non-responsive, response, under-coverage).

**Chapter 13:** Experiments and Observational Studies: Identify retrospective vs. perspective studies and the usefulness of each; identify the four basic principles of experimental design (control, randomization, replication and blocking); define experimental units, factors and treatments; design a completely randomized experiment – to test the effect of one or two factors; discuss ethics of experiments.

Midterm Review and Midterm

### Part 3: Probability

**Chapter 14:** From Randomness to Probability: Learn law of large numbers; define basic terms of probability (e.g., trial, outcome, event and complement); apply basic add/multi rules for probability; identify disjoint and independent events.

**Chapter 15:** Probability Rules: Learn general addition and multiplication rules for non-disjoint and non-independent events; understand conditional probability; calculate probabilities through the use of Venn diagrams, two-way tables and tree diagrams.

**Chapter 16:** Random Variables: Find the probability model for a discrete random variable; calculate the expected variable and variance of a random variable; determine mean and standard deviation after +/x by a constant; determine mean and standard deviation when +/- two independent random variables.

**Chapter 17:** Probability Models: Identify Bernoulli trials; find expected value and probabilities of a geometric model; find mean and standard deviation of a Binomial model; estimate Binomial probability using a normal model.

### Part 4: Inference

**Chapter 18:** Sampling Distribution Models: Use a sampling distribution model to make statements about mean or distribution proportion; interpret the central limit theorem.

**Chapter 19:** Confidence Intervals for Proportions: Construct a one-proportion z interval both mathematically and using statistical software; understand the impact of sample size and confidence level on the margin of error.

**Chapter 20:** Testing Hypotheses about Proportions: State the null and alternative hypotheses for a one-proportion z test; understand differences between one-sided and two-sided alternative hypotheses; perform a one-proportion z test both mathematically and using statistical software; interpret the results of a one-proportion z test.

**Chapter 21:** More About Tests: Understand the relationship between hypothesis tests and confidence intervals; choose an appropriate significance level (alpha level) with justification; identify Type 1 and Type 2 errors; define the power of a test.

**Chapter 22:** Comparing Two Proportions: Find a confidence interval for the difference between two proportions; perform a two-proportion z test both mathematically and using statistical software; interpret the meaning of the calculated p-value.

**Chapter 23:** Inferences about Means: Compute and interpret one-mean t interval using statistics software or from summary statistics; perform and interpret a one-mean t test using summary stats or statistical software; explain the difference between using a z-table and a t-table, define degrees of freedom.

**Chapter 24:** Comparing Means: Perform and interpret a two-sample t test using software or a calculator; calculate and interpret a two-sample t interval using software or a calculator.

**Chapter 25:** Paired Samples and Blocks: Recognize whether a design comparing two groups is paired; calculate and interpret a paired confidence interval; perform and interpret a paired t-test.

**Chapter 26:** Comparing Counts: Display and interpret counts in a two-way table; use the chi-squared tables to perform and interpret chi-square tests; compute and interpret a chi-square test using software or a calculator.

**Chapter 27:** Inferences for Regression: Test the standard hypothesis that the true regression slope is zero; state the null and alternative hypotheses; locate relevant numbers in a standard computer regression output; find a confidence interval for the slope of a regression based on the values reported in a standard regression output table