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Statistics Honors--2015-2016--Tentative Pacing Guide
Elementary Statistics: A Step by Step Approach, 9th ed., Allan Bluman
Lesson Days
1-1
2
1-2
2
1-3
14-1
1-4
14-2
3
3
14-3
2
2-1
2
2-2
2
2-3
3
3-1
3
3-2
3
3-3
3
3-4
2
Lesson Days
4-1
3
4-2
2
4-3
3
4-4
2
4-5
3
5-1
3
5-2
3
5-3
3
5-4
2
6-1
4
6-2
3
6-3
3
6-4
3
Quarter 1
Descriptive and Inferential Statistics
Variables and Types of Data
Data Collection and Sampling Techniques
Common Sampling Techniques
Experimental Design
Surveys and Questionnaire Design
Simulation Techniques and The Monte Carlo Method
Organizing Data
Histograms, Frequency Polygons, and Ogives
Other Types of Graphs
Measures of Central Tendency
Measures of Variation
Measures of Position
Exploratory Data Analysis
Quarter 2
Sample Spaces and Probability
The Addition Rules of Probability
The Multiplication Rules and Conditional Probability
Counting Rules
Probability and Counting Rules
Probability Distributions
Mean, Variance, Standard Deviation, and Expectation
The Binomial Theorem
Other Types of Distributions
Normal Distributions
Applications of Normal Distributions
The Central Limit Theorem
The Normal Approximation to the Binomial Distribution
Lesson Days
7-1
3
7-2
2
7-3
2
7-4
3
8-1
4
8-2
4
8-3
4
8-4
4
8-5
4
8-6
9-1
9-2
9-3
1
3
3
3
Lesson Days
9-4
4
9-5
4
10-1
3
10-2
4
11-1
4
11-2
4
Quarter 3
Confidence Intervals for Mean When is σ Known
Confidence Intervals for Mean When is σ Unknown
Confidence Intervals and Sample Size for Proportions
Confidence Intervals for Variances and Standard
Deviations
Steps in Hypothesis Testing - Traditional Method
z-Test for a Mean
t-Test for a Mean
z-Test for a Proportion
Chi-Squared Test for a Variance or Standard Deviation
Additional Topics Regarding Hypothesis Testing
Testing the Diff. Between 2 Means of Independent Samples: z-Test
Testing the Diff. Between 2 Means of Independent Samples: t-Test
Testing the Difference Between Two Means: Dependent Samples
Quarter 4
Testing the Difference Between Proportions
Testing the Difference Between Two Variances
Scatter Plots and Correlation
Regression
Test for Goodness-of-Fit
Tests Using Contingency Tables
Mathematical Practices for Students

Make sense of problems and
persevere in solving them.

Reason abstractly and
quantitatively.

Construct viable arguments and
critique the reasoning of others.

Model with mathematics.

Use appropriate tools strategically.

Attend to precision.

Look for and make use of
structure.

Look for and express regularity in
repeated reasoning.
Mathematics Teaching Practices

Establish mathematics goals to focus
learning.

Implement tasks that promote reasoning
and problem solving.

Use and connect mathematical
representations.

Facilitate meaningful mathematical
discourse.

Pose purposeful questions.

Build procedural fluency from conceptual
understanding.

Support productive struggle in learning
mathematics.

Elicit and use evidence of student thinking.
Statistics 2015-2016
Tennessee’s State Mathematics Standards
Textbook: Elementary Statistics: A Step by Step Approach, 9th ed, Allan Bluman
Exploring Data
Interpreting Categorical and Quantitative Data (S-ID)
Understand, represent, and use univariate data
1. Understand the term 'variable' and differentiate between the data types: measurement,
categorical, univariate and bivariate. 1-1
2. Understand histograms, parallel box plots, and scatterplots, and use them to display and compare
data. 2-2
3. Summarize distributions of univariate data. 2-1
4. Compute basic statistics and understand the distinction between a statistic and a parameter. 3-1
5. For univariate measurement data, be able to display the distribution, describe its shape;
select and calculate summary statistics. 3-1
6. Recognize how linear transformations of univariate data affect shape, center, and spread. 3-1
7. Analyze the effect of changing units on summary measures. 3-1, 3-4
8. Construct and analyze frequency tables and bar charts. 2-2
9. Describe individual performances in terms of percentiles, z-scores, and t- scores. 3-3
Understand, represent, and use bivariate data
10. Explore categorical data. 10-1
11. Display and discuss bivariate data where at least one variable is categorical. 10-1
12. For bivariate measurement data, be able to display a scatterplot and describe its shape;
use technological tools to determine regression equations and correlation coefficients. 10-1, 10-2
13. Identify trends in bivariate data; find functions that model the data and
that transform the data so that they can be modeled. 10-2
Probability
Conditional Probability and the Rules of Probability (S-CP)
Understand and apply the basic concepts of probability
1. Describe events as subsets of a sample space (the set of outcomes) using characteristics
(or categories) of the outcomes, or as unions, intersections, or complements of
other events (“or,” “and,” “not”). 4-1
2. Use permutations and combinations to compute probabilities of compound events
and solve problems. 4-4
3. Demonstrate an understanding of the Law of Large Numbers (Strong and Weak). 4-1
Use the rule of probability to compute probabilities of compound events in a uniform
probability model
4. Demonstrate an understanding of the addition rule (4-2), the multiplication rule (4-3),
conditional probability (4-3), and independence. (4-3)
5. Apply the general Multiplication Rule in a uniform probability model,
P(A and B) = P(A)P(B|A) = P(B)P(A|B), and interpret the answer in terms of the model. (4-3)
Probability Distributions
Using Probability to Make Decisions (S-MD)
Understand and use discrete probability distributions.
1. Define a random variable for a quantity of interest by assigning a numerical value to
each event in a sample space; graph the corresponding probability distribution using
the same graphical displays as for data distributions. 5-1
2. Calculate the expected value of a random variable; interpret it as the mean of the
probability distribution. 5-2
3. Design a simulation of random behavior and probability distributions. 14-3
4. Analyze discrete random variables and their probability distributions, including
binomial (5-3) and geometric. 5-4
5. Develop a probability distribution for a random variable defined for a sample space in
which theoretical probabilities can be calculated; find the expected value. For example,
find the theoretical probability distribution for the number of correct answers obtained
by guessing on all five questions of a multiple-choice test where each question has four
choices, and find the expected grade under various grading schemes. 4-1
6. Develop a probability distribution for a random variable defined for a sample space in
which probabilities are assigned empirically; find the expected value. For example, find
a current data distribution on the number of TV sets per household in the United States,
and calculate the expected number of sets per household. How many TV sets would you
expect to find in 100 randomly selected households? 4-1
7. Weigh the possible outcomes of a decision by assigning probabilities to payoff values
and finding expected values.
a. Find the expected payoff for a game of chance. For example, find the expected
winnings from a state lottery ticket or a game at a fast-food restaurant. 5-2
b. Evaluate and compare strategies on the basis of expected values. For example,
compare a high-deductible versus a low-deductible automobile insurance policy using
various, but reasonable, chances of having a minor or a major accident. 5-2
8. Use probabilities to make fair decisions (e.g., drawing by lots, using a random number
generator). 5-4
9. Analyze decisions and strategies using probability concepts (e.g., product testing,
medical testing, pulling a hockey goalie at the end of a game). 5-2, 5-3
Understand the normal probability distribution
10. Calculate the mean (expected value) and standard deviation of both a random variable
and a linear transformation of a random variable. 3-1, 5-2, 3-2
11. Use the mean and standard deviation of a data set to fit it to a normal distribution
and to estimate population percentages. Recognize that there are data sets for which
such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate
areas under the normal curve. 3-2
Sampling and Experimentation
Making Inferences and Justifying Conclusions (S-IC)
Know the characteristics of well-designed studies.
1. Understand the differences among various kinds of studies and which types of inferences
can be legitimately drawn from each. 1-4
2. Compare census, sample survey, experiment, and observational study. 1-4
3. Describe the role of randomization in surveys and experiments. 1-3, 14-2
4. Demonstrate an understanding of bias in sampling. 1-3
5. Describe the sampling distribution of a statistic and define the standard error of a statistic. 6-3
6. Demonstrate an understanding of the Central Limit Theorem. 6-3
Design and conduct a statistical experiment to study a problem, then interpret and
communicate the outcomes.
7. Select a method to collect data and plan and conduct surveys and experiments. 1-3, 14-1
8. Compare and use sampling methods, including simple random sampling, stratified
random sampling, and cluster sampling. 1-3, 14.1
9. Test hypotheses using appropriate statistics. 8-1
10. Analyze results and make conclusions from observational studies, experiments, and
surveys. 2-2
11. Evaluate reports based on data. 3-1
Make inferences about population parameters based on a random sample from that population.
12. Develop and evaluate inferences and predictions that are based on data. 8-2
13. Use properties of point estimators, including biased/unbiased, and variability. 7-1
Understand and use confidence intervals.
14. Understand the meaning of confidence level, of confidence intervals, and the properties
of confidence intervals. 7-1
15. Construct and interpret a large sample confidence interval for a proportion and for a
difference between two proportions. 7-3
16. Construct the confidence interval for a mean and for a difference between two means. 7-2,
7-3
Use distributions to make inferences about a data set.
17. Apply the properties of a Chi-square distribution in appropriate situations in order to
make inferences about a data set. 7-4
18. Apply the properties of the normal distribution in appropriate situations in order to make
inferences about a data set. 6-2
19. Interpret the t-distribution and determine the appropriate degrees of freedom. 8-3
STATISTICS HONORS - Tentative Schedule 2015-2016
Elementary Statistics: A Step by Step Approach, 9th ed, Allan Bluman
TEXTBOOK REFERENCE
TN Ready Mathematics Standards
Lesson 1.1
(2 days)
Descriptive and
Inferential Statistics
S-ID.1: Understand the term ‘variable’ and differentiate
between the data types: measurement, categorical, univariate,
and bivariate.
Lesson 1.2
(2 days)
Variables and Types of
Data
S-ID.1: Understand the term ‘variable’ and differentiate
between the data types: measurement, categorical, univariate,
and bivariate.
Lesson 1.3/Lesson 14.1
(3 days)
Data Collection and
Sampling
Techniques/Common
Sampling Techniques
S-IC.3: Describe the role of randomization in surveys and
experiments
S-IC.4: Demonstrate an understanding of bias in sampling
S-IC.7: Select a method to collect data and plan and conduct
surveys and experiments
S-IC.8: Compare and use sampling methods, including simple
random sampling, stratified random sampling, and cluster
sampling
Lesson 1.4 / Lesson 14.2
(3 days)
Experimental Design /
Surveys and
Questionnaire Design
S-IC.1: Understand the differences among various kinds of
studies and which types of inferences can be legitimately
drawn from each
S-IC.2: Compare census, sample survey, experiment, and
observational study
S-IC.3
Lesson 14.3
(1 day)
Simulation Techniques
and the Monte Carlo
Method
S-MD.3: Design a simulation of random behavior and
probability distributions
Lesson 2.1
(2 days)
Organizing Data
S-ID.3: Summarize distributions of univariate data
Lesson 2.2
(3 days)
Histograms, Frequency
Polygons, and Ogives
S-ID.2: Understand histograms, parallel box plots, and
scatterplots, and use them to display and compare data
S-ID.5: For univariate measurement data, be able to display
the distribution, describe its shape; select and calculate
summary statistics
S-ID.8: Construct and analyze frequency tables and bar charts
S-IC.10: Analyze results and make conclusion from
observational studies, experiments, and surveys
Lesson 2.3
(3 days)
Other Types of Graphs
S-ID.2, 5, 8
S-IC.10
Lesson 3.1
(3 days)
Measures of Central
Tendency
S-IC.4: Compute basic statistics and understand the distinction
between a statistic and a parameter
S-ID.6: Recognize how linear transformations of univariate
data affect shape, center, and spread
S-ID.7: Analyze the effect of changing units on summary
measures
S-MD.10: Calculate the mean (expected value) and standard
deviation of both a random variable and a linear
transformation of a random variable
S-IC.11: Evaluate reports based on data
S-ID.5
Lesson 3.2
(3 days)
Measures of Variation
S-MD.11: Use the mean and standard deviation of a data set
to fit it to a normal distribution and to estimate population
percentages. Recognize that there are data sets for which such
a procedure is not appropriate. Use calculators, spreadsheets,
and tables to estimate areas under the normal curve
S-MD.10, S-IC.11
Lesson 3.3
(3 days)
Measures of Position
S-ID.9: Describe individual performances in terms of
percentiles and z-scores
S-IC.11
Lesson 3.4
(2 days)
Exploratory Data
Analysis
S-ID.7: Analyze the effect of changing units on summary
measures
S-ID.2, 3, 4, 5, S-IC.11
Quarter 2
Lesson 4.1
(2-3 days)
Sample Spaces and
Probability
Lesson 4.2
(2-3 days)
The Addition Rules for
Probability
Lesson 4.3
(2-3 days)
The Multiplication Rules
and Conditional
Probability
Lesson 4.4
(1 day)
Counting Rules
S-CP.1: Describe events as subsets of a sample space (the set
of outcomes) using characteristics (or categories) of the
outcomes, or as unions, intersections, or complements of other
evens (“or,” “and,” “not”)
S-CP.3: Demonstrate an understand of the Law of Large
Numbers (strong and weak)
S-MD.5: Develop a probability distribution for a random
variable defined for a sample space in which theoretical
probabilities can be calculated; find the expected value
S-MD.6: Develop a probability distribution for a random
variable defined for a sample space in which probabilities can
be assigned empirically; find the expected value
S-CP.4: Demonstrate an understanding of the addition rule
S-CP.4: Demonstrate an understanding of the multiplication
rule, conditional probability, and independence
S-CP.5: Apply the general Multiplication Rule in a uniform
probability model, P(A and B) = P(A)P(B|A) = P(B)P(A|B), and
interpret the answer in terms of the model
S-CP.2: Use permutations and combinations to compute
probabilities of compound events and solve problems
Lesson 4-5
(3 days)
Probability and Counting
Rules
S-CP.2, S-CP.4
S-MD.1: Define a random variable for a quantity of interest by
Lesson 5.1
assigning a numerical value to each event in a sample space;
graph the corresponding probability distribution using the same
(2-3 days)
graphical displays as for data distributions
Probability Distributions
Lesson 5.2
(2-3 days)
Mean, Variance,
Standard Deviation and
Expectation
Lesson 5.3
(2-3 days)
The Binomial
Distribution
Lesson 5.4
(2 days)
Other Types of
Distributions
S-MD.5, 6, 9
S-MD.2: Calculate the expected value of a random variable;
interpret it as the mean of the probability distribution
S-MD.7a: Weigh the possible outcomes of a decision by
assigning probabilities to payoff values and finding expected
values. Find the expected payoff for a game of chance
S-MD.7b: Evaluate and compare strategies on the basis of
expected values
S-MD.9: Analyze decisions and strategies using probability
concepts
S.MD.10
S-MD.4: Analyze discrete random variables and their
probability distributions (including binomial)
S-MD.9
S-MD.8: Use probabilities to make fair decisions
S-MD.4, 9
Lesson 6.1
(3-4 days)
Normal Distributions
S-IC.18: Apply the properties of the normal distribution
in appropriate situations in order to make inferences
about a data set
S-MD.9
Lesson 6.2
(3-4 days)
Applications of Normal
Distributions
S-IC.18, S-MD.9
Lesson 6.3
(2-3 days)
The Central Limit
Theorem
S-IC.5: Describe the sampling distribution of a statistic
and define the standard error of a statistic
S-IC.6: Demonstrate an understanding of the Central
Limit Theorem
S-MD.9, S-IC.18
Lesson 6.4
(2-3 days)
The Normal
Approximation to the
Binomial Distribution
S-IC.18, S-MD.9
Quarter 3
Lesson 7.1
(2-3 days)
Confidence Intervals for
the Mean When σ is
Known
S-IC.13: Use properties of point estimators, including
biased/unbiased, and variability
S-IC.14: Understand the meaning of confidence level,
of confidence intervals, and the properties of
confidence intervals
Lesson 7.2
(2-3 days)
S-IC.16: Construct a confidence interval for a mean and
Confidence Intervals for
for a difference between two means
the Mean When σ is
Unknown
Lesson 7.3
(2-3 days)
Confidence Intervals and
Sample Size for
Proportions
S-IC.15: Construct and interpret a large sample
confidence interval for a proportion and for a
difference between two proportions
S-IC.16
Lesson 7.4
(2-3 days)
Confidence Intervals for
Variances and Standard
Deviations
S-IC.17: Apply the properties of a Chi-Squared
distribution in appropriate situations in order to make
inferences about a data set
Lesson 8.1
(3-4 days)
Steps in Hypothesis
Testing – Traditional
Method
S-IC.9: Test hypotheses using appropriate statistics
Lesson 8.2
(3-4 days)
z Test for a Mean
S-IC.12: Develop and evaluate inferences and
predictions that are based on data
S-IC.9
Lesson 8.3
(3-4 days)
t Test for a Mean
S-IC.19: Interpret the t-distribution and determine the
appropriate degrees of freedom
S-IC.9, S-IC.12
Lesson 8.4
(3-4 days)
z Test for a Proportion
S-IC.9, S-IC.12
Lesson 8.5
(3-4 days)
Chi-Squared Test for a
Variance or Standard
Deviation
S-IC.9, S-IC.12
Lesson 8.6
(1 day)
Additional Topics
Regarding Hypothesis
Testing
S-IC.9, S-IC.12
Lesson 9.1
(3-4 days)
Testing the Difference
Between Two Means:
Using the z Test
S-IC.9, S-IC.12
Lesson 9.2
(3-4 days)
Testing the Difference
Between Two Means of
Independent Samples:
Using the t Test
S-IC.9, S-IC.12
Lesson 9.3
(3-4 days)
Testing the Difference
Between Two Means:
Dependent Samples
S-IC.9, S-IC.12
Quarter 4
Lesson 9.4
(3-4 days)
Testing the Difference
Between Proportions
S-IC.9, S-IC.12
Lesson 9.5
(3-4 days)
Testing the Difference
Between Two Variances
S-IC.9, S-IC.12
Lesson 10.1
(2-3 days)
Scatter Plots and
Correlation
Lesson 10.2
(3-4 days)
Regression
S-ID.10: Explore categorical data
S-ID.11: Display and discuss bivariate data where at
least one variable is categorical
S-ID.12: For bivariate measurement data, be able to
display a scatterplot, describe its shape, determine the
correlation coefficient
S-ID.13: Identify trends in bivariate data; find functions
that model the data and that transform the data so that
they can be modeled
S-ID.12
Lesson 11.1
(3-4 days)
Test for Goodness-of-Fit
S-IC.17: Apply the properties of a chi-square
distribution in appropriate situations in order to make
inferences about a data set
Lesson 11.2
(3-4 days)
Test Using Contingency
Tables
Beyond the scope of TN Ready Standards