Download SCHOOL DISTRICT 283 Statistics - East Pennsboro Area School

EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Terminology
Subjects: Math
Days: 3
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
It is necessary to have a foundation based on essential
terminology.
Unit Essential Question(s):
How do we effectively use terminology to describe our world using
statistics?
Concept:
Concept:
Concept:
Population vs. Sample
2.5.11.B, 2.5.11.C
Parameter vs. Statistic
2.5.11.B, 2.5.11.C
Qualitative vs. Quantitative
2.5.11.B, 2.5.11.C
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Why would we need to
distinguish between a
population and a sample?
Why is it important to
distinguish between a
parameter and a statistic?
Why would you need to
distinguish between qualitative
and quantitative data?
Vocabulary:
Vocabulary:
Vocabulary:
Population
Sample
Parameter
Statistic
Qualitative data
Quantitative data
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Levels of Measurement
2.5.11.B, 2.5.11.C
Data Collection Methods
2.5.11A, 2.5.11.B, 2.5.11.D
Sampling Techniques
2.5.11A, 2.5.11C, 2.5.11.E
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Why do we need to classify
data with respect to the four
levels of measurement:
nominal, ordinal, interval, and
ratio?
How can data be collected for
reliability and validity?

How can we create a sample
using random sampling,
stratified sampling, cluster
sampling, systematic sampling,
and convenience sampling?
When is it appropriate to use
each sampling technique?
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Vocabulary:
Vocabulary:
Vocabulary:
Nominal
Ordinal
Interval
Ration
Experiment
Simulation Census
Sampling
Random
Stratified
Cluster
Systematic
Convenience
Chapter:
Chapter:
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Mathematical Modeling
Subjects: Math
Days: 6
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Data can be displayed graphically to organize and interpret
information effectively.
Unit Essential Question(s):
How can data be organized into a meaningful form?
Concept:
Concept:
Concept:
Frequency Distributions
2.2.11.A, 2.2.11.C, 2.2.11.F
Frequency Graph Construction
2.2.11.A, 2.2.11.C, 2.2.11.F,
2.3.11.C, 2.6.11.B
Stem-and-Leaf Plots vs. Dot lot
2.2.11.A, 2.2.11.C, 2.2.11.F,
2.6.11.B
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:




When is it appropriate to use
frequency distribution?
How does a frequency
distribution summarize data?

How can a conclusion be
formulated from information
presented in a graph?
How can statistics (data)
mislead?


When is it appropriate to
construct a stem-and-leaf plot?
When is it appropriate to
construct a dot plot?
How could you
compare/contrast information
displayed in a stem-and-leaf
plot and a dot plot?
Vocabulary:
Vocabulary:
Vocabulary:
Frequency Distribution
Frequency Midpoint
Relative Frequency
Cumulative Frequency
Frequency Histogram
Frequency Polygon
Relative Frequency Histogram
Ogive
Stem-and-Leaf Plot
Dot Plot
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Pie Charts vs. Pareto Charts
2.2.11.A, 2.2.11.C, 2.2.11.F,
2.3.11.C, 2.6.11.B
Time Series
2.2.11.B, 2.2.11.F, 2.6.11.B,
2.6.11.G, 2.8.11.B
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:





When is it appropriate to
construct a pie chart?
When is it appropriate to
construct a Pareto chart?
How could you
compare/contrast information
displayed in a pie chart and a
Pareto chart?
When is it appropriate to
construct a time series chart?
Vocabulary:
Vocabulary:
Pie Chart
Pareto Chart
Time Series Chart
Chapter:
Chapter:
Vocabulary:
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Descriptive Statistics
Subjects: Math
Days: 10
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Data can be described in order to see trends, averages, and
variations.
Unit Essential Question(s):
Why is it important to calculate measures of central tendencies,
variation, and position for a set of data?
Concept:
Concept:
Concept:
Measures of Central Tendencies
2.1.11.A, 2.6.11.A, 2.6.11.B,
2.8.11.A
Measures of Variation
2.1.11.A, 2.6.11.A, 2.6.11.B,
2.8.11.A
Five Number Summary
2.1.11.A, 2.6.11.B, 2.8.11.A
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:




How do you justify a measure
if central tendency?
How do the measures of
central tendency affect the
shape of a distribution?


How do you calculate the
range, variance, and standard
deviation of a population and a
sample?
How can we use the Empirical
Rule and Chebyshev’s
Theorem to interpret standard
deviation?
How is approximating the
sample standard deviation for
grouped data different than
non-grouped data?



How do you fine the first,
second, and third quantities of
a data set?
How would you determine if a
data point is an outlier?
When is it appropriate to use a
box-and-whisker plot?
What do percentiles measure?
Vocabulary:
Vocabulary:
Vocabulary:
Mean
Median
Mode
Weighted Mean
Skewed
Uniform
Symmetric
Bimodal
Range
Variance
Standard Deviation
Grouped Data
Sum of Squares
Interquartile Range
Quartile
Minimum
Maximum
Box-and-Whisker
Outlier
5-Number Summary
Percentile
Chapter:
Chapter:
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Probability
Subjects: Math
Days: 10
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Methods we use to generalize results from a sample to a
population are based on probability.
Unit Essential Question(s):
How can we quantify our predictions occurring outcomes?
Concept:
Concept:
Concept:
Types and Properties of
Probabilities
2.7.11.E, 2.7.11.A
Multiplication Rule
2.7.11.A, 2.7.11.B, 2.7.11.E
Addition Rule
2.7.11.A, 2.7.11.B, 2.7.11.E
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:




How do you identify a sample
space of a probability
experiment?
How do you need to
distinguish between simple
and compound events?

Why would you need to
distinguish between
independent and dependent
events?
When is it appropriate to use
the multiplication rule?

Why would it be important to
determine if two events are
mutually exclusive?
When is it appropriate to use
the addition rule?
Vocabulary:
Vocabulary:
Vocabulary:
Sample Space
Simple Event
Compound Event
Complement
Experimental Probability
Theoretical Probability
Independent Event
Dependent Event
Conditional Probability
Mutually Exclusive
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Counting Techniques
2.7.11.A, 2.7.11.B, 2.7.11.E
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Vocabulary:
Vocabulary:
Chapter:
Chapter:


When is it appropriate to use
the fundamental counting
principle?
How do you differentiate using
permutations or
combinations?
How would we apply counting
techniques to compute
probabilities?
Vocabulary:
Combination
Permutations
Factorial
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Discrete Probability Distributions
Subjects: Math
Days: 15
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Discrete probability distributions are common in business,
science, engineering, and many other fields.
Unit Essential Question(s):
How can discrete probability distributions be applied in real-life
situations?
Concept:
Concept:
Concept:
Properties of Discrete Probability
Distributions
2.4.11.B
Modeling and Analyzing Discrete
Probability Distributions
2.1.11.A, 2.4.11.E, 2.6.11.B,
2.7.11.C, 2.7.11.D
Binomial Distributions
2.1.11.A, 2.4.11.E, 2.6.11.B,
2.7.11.C, 2.7.11.D
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:




How do you distinguish
between discrete and
continuous random variables?
What are the characteristics
of a probability distribution?

How do you construct a
discrete probability distribution?
How do you calculate the
expected value of a probability
distribution?


How would you determine if a
probability experiment is a
binomial experiment?
How do you compute binomial
probabilities?
How do you calculate the
mean, variance, and standard
deviation of a binomial
probability distribution?
Vocabulary:
Vocabulary:
Vocabulary:
Random variable
Discrete random variable
Continuous random variable
Discrete probability distribution
Expected value
Binomial experiment
Binomial
Experiment
Chapter:
Chapter:
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Concept:
Concept:
Concept:
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Vocabulary:
Vocabulary:
Chapter:
Chapter:
Geometric and Poisson
Distributions
2.1.11.A, 2.4.11.E, 2.6.11.B,
2.7.11.C, 2.7.11.D

How do you use the
geometric distribution to
compute probabilities?
How do you use the Poisson
distribution to compute
probabilities?
Vocabulary:
Geometric distribution
Poisson distribution
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Normal Probability Distributions
Subjects: Math
Days: 15
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
The normal distribution is the most important probability
distribution of statistics.
Unit Essential Question(s):
How can the normal distribution be applied in real-life situations?
Concept:
Concept:
Concept:
Analyzing Normal Probability
Distributions
2.2.11.A, 2.6.11.I
Z-Scores
2.2.11.A, 2.6.11.I
Sampling Distributions
2.2.11.A, 2.6.11.H, 2.6.11.I
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:





How would you interpret
graphs of normal probability
distributions?
How would you summarize
your findings of the area
under a normal curve?
How would you calculate the
probabilities for normally
distributed variable?

How do you calculate and
interpret standard z-scores?
How do you formulate a value
of a variable when its standard
score is given?

How do you apply the Central
Limit Theorem?
When applying the Central
Limit Theorem, how do you
calculate the probability of a
sample mean?
Vocabulary:
Vocabulary:
Vocabulary:
Continuous random variable
Normal curve
Normal distribution
Standard (z) score
Standard normal distribution
Sampling distribution
Standard error of mean
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Approximating Binomial
2.2.11.A, 2.6.11.I
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Vocabulary:
Vocabulary:
Chapter:
Chapter:

How would you decide when
the normal distribution can
approximate the binomial
distribution?
How do you calculate the
correction for continuity?
Vocabulary:
Correction for continuity
Correction
Continuity
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Confidence Intervals
Subjects: Math
Days: 15
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
When it is impossible to calculate an exact parameter, a more
meaningful estimate can be found by specifying an interval and
creating a statement of how confident you are that your interval
contains the population parameter.
Unit Essential Question(s):
What techniques can be used to make accurate estimates of
population parameters?
Concept:
Concept:
Concept:
Confidence Intervals for
Population Means
2.2.11.A, 2.2.11.D, 2.2.11.F,
2.6.11.A, 2.8.11.D, 2.11.11.A,
2.11.11.B
t-Distributions
2.2.11.A, 2.2.11.D, 2.2.11.F,
2.6.11.A, 2.8.11.D, 2.11.11.A,
2.11.11.B
Confidence Intervals for Population
Proportion
2.2.11.A, 2.2.11.D, 2.2.11.F,
2.6.11.A, 2.8.11.D, 2.11.11.A,
2.11.11.B
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:





How do you calculate a point
estimate and maximum error
of estimate?
What conclusions can you
make from the construction of
a confidence interval for a
population mean?
How would you determine the
required minimum sample
size when estimating the
population mean?


What information can you
interpret from a t-distribution?
How is the t-distribution applied
to real-life applications?
How do you construct
confidence intervals when the
sample size is less than thirty
and the same standard
deviation is unknown?


How do you calculate a point
estimate for the population
proportion?
How would you create a
confidence interval for a
population proportion?
When estimating a population
proportion, what information is
required to find the minimum
sample size?
Vocabulary:
Vocabulary:
Vocabulary:
Point estimate
Level of confidence
Critical value
Maximum error of estimate
Confidence interval
Degrees of freedom
Proportion
Chapter:
Chapter:
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Concept:
Concept:
Concept:
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Vocabulary:
Vocabulary:
Chapter:
Chapter:
Confidence Intervals for
Population Differences

How can you estimate the
difference between two
populations?
How can you estimate the
difference between two
proportions?
Vocabulary:
Pooled variances
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Hypothesis Testing
Subjects: Math
Days: 15
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Hypothesis testing gives a scientific procedure for assessing the
validity of a claim about a population?
Unit Essential Question(s):
How can samples be used to test the validity of a claim?
Concept:
Concept:
Concept:
Characteristics of Hypothesis
Testing
2.2.11.D, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
z-Test (One and Two Sample)
2.2.11.D, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
t-Test (One and Two Samples)
2.2.11.D, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:






How would you summarize
the process for completing a
hypothesis test?
How would you distinguish
between a null hypothesis and
an alternative hypothesis?
How would you distinguish
between a Type I and a Type
II error?
How would you recognize
whether to use a one-tailed or
two-tailed test?

How would you use a table to
find the critical values for a ztest with one sample?
How do you perform a a-test to
test a mean with one sample?

How would you use a table to
find the critical values for a ttest?
How do you apply the t-test to
test a mean?
Vocabulary:
Vocabulary:
Vocabulary:
Hypothesis test
Statistical hypothesis
Null hypothesis
Alternative hypothesis
Type I error
Type II error
Level of significance
Critical region
Left-tailed test
Right-tailed test
Two-tailed test
z-test
Standardized test statistic
t-test
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Interpreting a Hypothesis Test
2.2.11.D, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
Hypothesis Testing for Proportions
2.2.11.D, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:




How are p-values used to
determine the results of a
hypothesis test?
How are critical values used
to determine the results of a
hypothesis test?
Vocabulary:
How do you apply the z-test to
test a population proportion?
Vocabulary:
Vocabulary:
Chapter:
Chapter:
p-value
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Hypothesis Testing with Two Samples
Subjects: Math
Days: continued from previous section
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Hypothesis testing can be used to compare two populations.
Unit Essential Question(s):
How can hypothesis testing be used to determine if differences in
samples represent actual differences in populations?
Concept:
Concept:
Concept:
Large Independent Samples
2.2.11.D, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
Small Independent Samples
2.2.11.F, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
Dependent Sample
2.2.11.F, 2.4.11.A, 2.4.11.B,
2.4.11.C, 2.5.11.B, 2.6.11.H
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:

How do you perform a two-sample
t-test for the difference between two
means using small independent
sample?

Vocabulary:
Vocabulary:
Vocabulary:
Independent
2-sample z-test
Pooled
Dependent
Paired samples
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Vocabulary:
Vocabulary:
How do you perform a twosample z-test for the
difference between two
means using large
independent samples?

How do you decide whether
two samples are independent
or dependent?
How do you perform a t-test to
test the mean of the differences
for a population of paired data?
Difference Between
Proportions
2.2.11.F, 2.4.11.B, 2.5.11.B,
2.6.11.B, 2.6.11.H
How do you perform a z-test
for the difference between two
population proportions?
Vocabulary:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Correlation Analysis
Subjects: Math
Days: 10
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Correlation can be used to determine the significance of the
relationship between two quantitative variables.
Unit Essential Question(s):
What is the significance of correlation in analyzing paired data?
Concept:
Concept:
Concept:
Correlation Coefficients
2.2.11.A, 2.8.11.S
Line of Best-fit
2.2.11.A, 2.2.11.B, 2.2.11.C,
2.6.11.C, 2.6.11.D, 2.8.11.E,
2.8.11.K, 2.8.11.L, 2.8.11.S
Standard Error of Estimate
2.2.11.A
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



How do you analyze a
population correlation
coefficient?

How do you calculate the
equation of a regression line?
What conclusions can you draw
by using a regression equation
to predict y-values?
What conclusions can you draw
from finding the standard error
of estimate for a regression
line?
Vocabulary:
Vocabulary:
Vocabulary:
Correlation coefficient
Hypothesis test
Correlation
Independent variable
Dependent variable
Causation
Regression line
Residuals
Line of best-fit
Standard error of estimate
Coefficient of determination
Explained variation
Unexplained variation
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Prediction Intervals
2.2.11.A, 2.2.11.B, 2.2.11.C,
2.6.11.D
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



Vocabulary:
Vocabulary:
Chapter:
Chapter:
What generalizations can you
infer from the construction of
a confidence interval for y?
Vocabulary:
Interval
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Chi-Square and F-Tests
Subjects: Math
Days: 15
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
Sometimes it is necessary to test a hypothesis that compares
three or more populations.
Unit Essential Question(s):
Why is the process for comparing three or more populations
different than comparing two populations?
Concept:
Concept:
Concept:
Chi-Square Distribution
2.2.11.A, 2.2.11.D, 2.6.11.A,
2.8.11.D, 2.11.11.A, 2.11.11.B
Chi-Square Test
2.2.11.A, 2.2.11, 2.2.11.E, 2.2.11.F,
2.4.11.A, 2.4.11.C
Independence Tests
2.2.11.A, 2.2.11.F, 2.3.11.A,
2.6.11.F
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:





How do you interpret a chisquare distribution?
How do you interpret
information on a chi-square
distribution table?
How would you demonstrate
the use of the chi-square
distribution to construct a
confidence interval for the
variance and standard
deviation?

How do you use a table to find
the critical values for a chisquare test?
How would you apply the chisquare to test a variance or
standard deviation?

How would you apply a
contingency table to find
expected frequencies?
How would you associate the
use of chi-square distributions
to examine whether two
variable are independent?
Vocabulary:
Vocabulary:
Vocabulary:
Chi-square distribution
Chi-square test
Contingency table
Chapter:
Chapter:
Chapter:
Concept:
Concept:
Concept:
Fitting a Claimed Distribution
2.2.11.A, 2.3.11.A, 2.6.11.H
F-Tests
2.2.11.A, 2.3.11.A, 2.5.11.B
ANOVA Test
2.2.11.A, 2.2.11.F, 2.3.11.A
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



How can we use a chi square
distribution to test whether a
frequency distribution fits a
claimed distribution?

What generalization can you
interpret from an F-distribution?
How would you summarize
your findings of critical values
from an F-table?
How would you integrate the
use of a one-way analysis of
variance to examine claims
involving three or more means?
Vocabulary:
Vocabulary:
Vocabulary:
Observed frequency
Expected frequency
Goodness-of-fit test
F-distribution
ANOVA
Chapter:
Chapter:
Chapter:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07
EAST PENNSBORO AREA
SCHOOL DISTRICT
COURSE:
283 Statistics
Unit: Non Parametric Tests
Subjects: 283 Statistics
Days: 6
Grade(s): 11-12
Key Learning(s):
Instructional Tools:
It is possible to analyze a set of data when little or no information
is known about the population’s distribution.
Unit Essential Question(s):
What are the advantages and disadvantages of using
nonparametric tests?
Concept:
Concept:
Concept:
Sign Test
2.2.11.A, 2.3.11.A
Rank-Sum Test
2.2.11.A, 2.3.11.A
Spearman Rank Test
2.2.11.A, 2.3.11.A
Lesson Essential Questions:
Lesson Essential Questions:
Lesson Essential Questions:



How can you compare the
difference between dependent
samples by conducting the
sign test?
How can you compare the
difference between
independent samples?
How would you summarize
whether the correlation
between two variables is
significant when calculating
using the Spearman Rank
Correlation Coefficient?
Vocabulary:
Vocabulary:
Vocabulary:
Nonparametric test
Sign test
Rank-Sum test
Spearman rank correlation
coefficient
Chapter:
Chapter:
Chapter:
Study Island Lesson:
Study Island Lesson:
Study Island Lesson:
The expected level of achievement is 80% mastery of all concepts.
The expected level of achievement is 90% mastery of all concepts for academic and honors students.
Board Approval 7/5/07