AP Statistics - Radnor School District

```Radnor High School
Course Syllabus
AP Statistics
0470
I. Course Description
AP Statistics introduces students to the major concepts and tools for collecting, analyzing and drawing conclusions
from data and exposes them to four broad concepts: exploring data for patterns and departures from patterns,
sampling and experimentation in planning and conducting a study, anticipating patterns while exploring random
phenomena using probability and simulation, and statistical inference - estimating population parameters and testing
hypotheses. Students who successfully complete this course and the College Board AP Statistics examination may
receive advanced credit for one semester of introductory college statistics. A graphing calculator is required.
II. Materials &amp; Equipment
 Introduction to the Practice of Statistics, Third Edition, Moore and McCabe – W.H. Freeman and Co.
 TI-83, TI-83 Plus or TI-84 Plus graphing calculator
 Minitab statistical software (available in two computer labs)
 Video series: Statistics – Decisions Through Data (25 fifteen minutes videos covering specific topics
within the course)
 Supplemental video series Against All Odds: Inside Statistics, The Annenberg/CPB Collection, housed in
the RHS library
III. Course Goals &amp; Objectives
 Prepare students to take the AP Statistics examination.
 Introduce students to technological methods of data analysis.
 Develop the students’ ability to collect, summarize, analyze and communicate their ideas through projects
and data investigations.
 Conclude a solution process with a summary of results and evaluate the degree to which the results
obtained represent an acceptable response to the initial problem and why the reasoning is valid.
IV. Course Topics (Summary Outline)
A. UNIVARIATE DATA
Describing Distributions with Graphs
 Graphs for categorical variables – bar charts,
pie charts, Pareto graphs
 Examining distributions – stemplots,
histograms, timeplots
 Beyond the basics – decomposing time
series
Describing Distributions with Numbers
 Measures of center – the mean and the
median
 Measures of spread – standard deviation; the
quartiles, interquartile range, five number
summary and boxplots
 Choosing the correct measure of center and
Revised: March 10, 2016
The Normal Distribution
 The 68-95-99.7 rule (The Empirical Rule)
 The standard normal distribution; the area
under the curve using Z scores
 Normal quantile plots; using normality to
check for outliers
B. BIVARIATE DATA
Scatterplots; Correlation
variables to scatterplots
 Correlation coefficient, r; properties of the
correlation coefficient
Least Squares Regression
 Fitting a line to data; least square regression;
interpreting the regression line; prediction
 Correlation and regression; r2
Residuals
 Residuals; regression diagnostics; lurking
variables; outliers and influential
observations
G. ONE AND TWO SAMPLE TESTING;
SMALL AND LARGE SAMPLES
The Student T Distribution
 The t distributions; degrees of freedom;
sample standard deviation; robustness of the
t distribution
 One sample t confidence interval
 One sample t test; matched pair
Comparison of Two Means
 Two sample z confidence interval and test
statistics for large samples
 Two sample t confidence intervals and test
statistics for small samples
Exponential Growth and Logarithmic
Transformations
 The logarithm transformation; the use of
residuals; prediction in the exponential
growth model
H. INFERENCE FOR PROPORTIONS
Inference for One and Two Proportions
 Confidence Intervals and significance tests
for one and two proportion Z tests
information
 Choosing a sample size
Sampling Distribution of a Sample Mean
 Mean and standard deviation of X bar
 Sampling distribution of X bar
 The Central Limit Theorem
I. CHI SQUARE
Goodness of Fit
 The chi square distribution; degrees of
freedom
 Significance testing for goodness of fit
F. INTRODUCTION TO INFERENCE
Confidence Intervals
 Confidence interval for a population mean
 How confidence intervals behave
 Choosing the sample size
Tests of Significance, Large Sample
 Hypothesis testing; stating hypotheses, test
statistics, p-values, statistical significance
 Tests for a population mean
 Two sided versus one sided tests and
confidence intervals
 P-values versus fixed alpha values; alpha
error (Type I error)
Homogeneity and Independence
 Significance testing for independence using
matrices; p-value, statistical significance
 The chi-square test and the z test
J. INFERENCE FOR REGRESSION*
Simple Linear Regression*
 Statistical model for linear regression
 Estimating the regression parameters
 Confidence intervals and significance tests
Power and the Beta Error (Type II)
 Power calculation
 Increasing power and decreasing beta error