Statistics
7th Grade


Alabama Course of Study Standards:
10

Examine a sample of a population to generalize information about the population. Differentiate between a sample and a population.
 Compare sampling techniques to determine whether a sample is random and thus representative of a population, explaining that random sampling tends to produce representative samples and support valid inferences.
 Determine whether conclusions and generalizations can be made about a population based on a sample.
 Use data from a random sample to draw inferences about a population with an unknown characteristic of interest, generating multiple samples to gauge variation and making predictions or conclusions about the population.
 Informally explain situations in which statistical bias may exist.

Arkansas Academic Standards:
7.SP.A.1

Understand that: Statistics can be used to gain information about a population by examining a sample of the population
 Generalizations about a population from a sample are valid only if the sample is representative of that population
 Random sampling tends to produce representative samples and support valid inferences

Common Core State Standards:
Math.7.SP.1 or 7.SP.A.1

Understand that statistics can be used to gain information about a population by examining a sample of the population; generalizations about a population from a sample are valid only if the sample is representative of that population. Understand that random sampling tends to produce representative samples and support valid inferences. 
Georgia Standards of Excellence (GSE):
7.PAR.4.10

Predict characteristics of a population by examining the characteristics of a representative sample. Recognize the potential limitations and scope of the sample to the population. 
North Carolina  Standard Course of Study:
7.SP.1

Understand that statistics can be used to gain information about a population by: Recognizing that generalizations about a population from a sample are valid only if the sample is representative of that population.
 Using random sampling to produce representative samples to support valid inferences.

New York State Next Generation Learning Standards:
7.SP.1

Construct and interpret boxplots, find the interquartile range, and determine if a data point is an outlier. Note: Students in grade 7 are not expected to construct boxplots that include outliers in the data, but students are expected to interpret boxplots that may contain outliers. 
Ohio's Learning Standards:
7.SP.1

Understand that statistics can be used to gain information
about a population by examining a sample of the population. Differentiate between a sample and a population.
 Understand that conclusions and generalizations about a
population are valid only if the sample is representative of that
population. Develop an informal understanding of bias.

Arkansas Academic Standards:
7.SP.A.2

 Use data from a random sample to draw inferences about a population with a specific characteristic
 Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions
For example: Estimate the mean word length in a book by randomly sampling words from the book, or predict the winner of a school election based on randomly sampled survey data. Gauge how far off the estimate or prediction might be.

Common Core State Standards:
Math.7.SP.2 or 7.SP.A.2

Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions. For example, estimate the mean word length in a book by randomly sampling words from the book; predict the winner of a school election based on randomly sampled survey data. Gauge how far off the estimate or prediction might be. 
Georgia Standards of Excellence (GSE):
7.PAR.4.12

Use data from repeated random samples to evaluate how much a sample mean is expected to vary from a population mean. Simulate multiple samples of the same size. 
North Carolina  Standard Course of Study:
7.SP.2

Generate multiple random samples (or simulated samples) of the same size to gauge the variation in estimates or predictions, and use this data to draw inferences about a population with an unknown characteristic of interest. 
Ohio's Learning Standards:
7.SP.2

Broaden statistical reasoning by using the GAISE model: Formulate Questions: Recognize and formulate a statistical
question as one that anticipates variability and can be answered
with quantitative data. For example, “How do the heights of
seventh graders compare to the heights of eighth graders?”(GAISE Model, step 1)
 Collect Data: Design and use a plan to collect appropriate data to
answer a statistical question. (GAISE Model, step 2)
 Analyze Data: Select appropriate graphical methods and
numerical measures to analyze data by displaying variability
within a group, comparing individual to individual, and comparing
individual to group. (GAISE Model, step 3)
 Interpret Results: Draw logical conclusions and make
generalizations from the data based on the original question.
(GAISE Model, step 4)

Wisconsin Academic Standards:
7.SP.A.2

Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions. For example, estimate the mean word length in a book by randomly sampling words from the book; predict the winner of a school election based on randomly sampled survey data. Gauge how far off the estimate or prediction might be. 
Alabama Course of Study Standards:
11

Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities,
measuring the difference between the centers by expressing it as a multiple of a measure of variability. 
Arkansas Academic Standards:
7.SP.B.3

Draw conclusions about the degree of visual overlap of two numerical data distributions with similar variability such as interquartile range or mean absolute deviation, expressing the difference between the centers as a multiple of a measure of variability such as mean, median, or mode
For example: The mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability on either team; on a dot plot, the separation between the two distributions of heights is noticeable. 
Common Core State Standards:
Math.7.SP.3 or 7.SP.B.3

Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability (mean absolute deviation) on either team; on a dot plot, the separation between the two distributions of heights is noticeable. 
Louisiana Academic Standards:
7.SP.B.3

Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities using quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered. 
Massachusetts Curriculum Frameworks:
7.SP.B.3

Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team and both distributions have similar variability (mean absolute deviation) of about 5 cm. The difference between the mean heights of the two teams (10 cm) is about twice the variability (5 cm) on either team. On a dot plot, the separation between the two distributions of heights is noticeable. 
Mississippi College and CareerReadiness Standards:
7.SP.3

Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability on either team; on a dot plot, the separation between the two distributions of heights is noticeable. 
North Carolina  Standard Course of Study:
7.SP.3

Recognize the role of variability when comparing two populations. Calculate the measure of variability of a data set and understand that it describes how the values of the data set vary with a single number.
 Understand the mean absolute deviation of a data set is a measure of variability that describes the average distance that points within a data set are from the mean of the data set.
 Understand that the range describes the spread of the entire data set.
 Understand that the interquartile range describes the spread of the middle 50% of the data.
 Informally assess the difference between two data sets by examining the overlap and separation between the graphical representations of two data sets.

New York State Next Generation Learning Standards:
7.SP.3

Informally assess the degree of visual overlap of two quantitative data distributions. 
Ohio's Learning Standards:
7.SP.3

Describe and analyze distributions. Summarize quantitative data sets in relation to their context by using mean absolute deviation (MAD), interpreting mean as a balance point.
 . Informally assess the degree of visual overlap of two numerical data distributions with roughly equal variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players
on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability (mean absolute deviation) on either team; on a dot plot (line plot), the separation between the two distributions of heights is noticeable.

Wisconsin Academic Standards:
7.SP.B.3

Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability (mean absolute deviation) on either team; on a dot plot, the separation between the two distributions of heights is noticeable. 
Alabama Course of Study Standards:
12

Make informal comparative inferences about two populations using measures of center and variability and/or mean
absolute deviation in context. 
Arkansas Academic Standards:
7.SP.B.4

Draw informal comparative inferences about two populations using measures of center and measures of variability for numerical data from random samples
For example: Decide whether the words in a chapter of a seventhgrade science book are generally longer than the words in a chapter of a fourthgrade science book. 
Common Core State Standards:
Math.7.SP.4 or 7.SP.B.4

Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations. For example, decide whether the words in a chapter of a seventhgrade science book are generally longer than the words in a chapter of a fourthgrade science book. 
Georgia Standards of Excellence (GSE):
7.PR.6.6

Use appropriate graphical displays and numerical summaries from data distributions with categorical or quantitative (numerical) variables as probability models to draw informal inferences about two samples or populations. 
Mississippi College and CareerReadiness Standards:
7.SP.4

Use measures of center and measures of variability (i.e. interquartile range) for numerical data from random samples to draw informal comparative inferences about two populations. For example, decide whether the words in a chapter of a seventhgrade science book are generally longer than the words in a chapter of a fourthgrade science book. 
North Carolina  Standard Course of Study:
7.SP.4

Use measures of center and measures of variability for numerical data from random samples to draw comparative inferences about two populations. 
New York State Next Generation Learning Standards:
7.SP.4

Use measures of center and measures of variability for quantitative data from random samples or populations to draw informal comparative inferences about the populations. Note: Measures of center are mean, median, and mode. The measures of variation include range and the interquartile range. 
Tennessee Academic Standards:
7.SP.B.4

Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations. For example, decide whether the words in a chapter of a 7^{th} science book are generally longer than the words in a chapter of a 4^{th} science book. 
Wisconsin Academic Standards:
7.SP.B.4

Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations. For example, decide whether the words in a chapter of a seventhgrade science book are generally longer than the words in a chapter of a fourthgrade science book. 
Pennsylvania Core Standards:
CC.2.4.7.B.1

Draw inferences about populations based on random sampling concepts. 
Pennsylvania Core Standards:
M07.DS.1.1.1

Determine whether a sample is a random sample given a realworld situation. 
Pennsylvania Core Standards:
M07.DS.1.1.2

Use data from a random sample to draw inferences about a population with an unknown characteristic of interest 
Pennsylvania Core Standards:
CC.2.4.7.B.2

Draw informal comparative inferences about two populations. 
Pennsylvania Core Standards:
M07.DS.2.1.1

Compare two numerical data distributions using measures of center and variability. 
