eSpark is recommended for grades Kβ5, but provides content outside of this range to allow for classroom differentiation.

6.SP.1

**Intro to Statistics**

Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

6.SP.2

**Center, Spread and Shape**

Understand that a set of data collected to answer a statistical question has a distribution which can be described by its center, spread, and overall shape.

6.SP.3

**Measures of Center and Variation**

Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how its values vary with a single number.

6.SP.4

**Displaying Data**

Display numerical data in plots on a number line, including dot plots, histograms, and box plots.

6.SP.5.a

**Summarizing Data Sets**

Reporting the number of observations.

6.SP.5.b

**Summarizing Data Sets**

Describing the nature of the attribute under investigation, including how it was measured and its units of measurement.

6.SP.5.c

**Summarizing Data Sets**

Giving 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.

6.SP.5.d

**Summarizing Data Sets**

Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.

eSpark is recommended for grades Kβ5, but provides content outside of this range to allow for classroom differentiation.

7.SP.1

**Inferential Statistics**

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.

7.SP.2

**Inferential Statistics**

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.

7.SP.4

**Measures of Central Tendency**

Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations.

7.SP.5

**Probability of a Chance Event**

Understand that the probability of a chance event is a number between 0 and 1 that expresses the likelihood of the event occurring. Larger numbers indicate greater likelihood. A probability near 0 indicates an unlikely event, a probability around 1/2 indicates an event that is neither unlikely nor likely, and a probability near 1 indicates a likely event.

7.SP.6

**Predict and Compare Probability**

Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability.

7.SP.7.a

**Predict and Compare Probability**

Develop a uniform probability model by assigning equal probability to all outcomes, and use the model to determine probabilities of events.

7.SP.7.b

**Predict and Compare Probability**

Develop a probability model (which may not be uniform) by observing frequencies in data generated from a chance process.

7.SP.8.a

**Probabilities of Compound Events**

Understand that, just as with simple events, the probability of a compound event is the fraction of outcomes in the sample space for which the compound event occurs.

7.SP.8.b

**Summarizing Data Sets**

Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.

eSpark is recommended for grades Kβ5, but provides content outside of this range to allow for classroom differentiation.

8.SP.1

**Construct, Explain Scatter Plots**

Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.

8.SP.2

**Line of Best Fit**

Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.

8.SP.4

**Two-Way Table**

Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables.

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