7SP-Draw+informal+comparative+inferences+about+two+populations

7.MP.1. Make sense of problems and persevere in solving them. 7.MP.2. Reason abstractly and quantitatively. 7.MP.3. Construct viable arguments and critique the reasoning of others. 7.MP.4. Model with mathematics. 7.MP.5. Use appropriate tools strategically. 7.MP.6. Attend to precision. 7.MP.7. Look for and make use of structure. 7.MP.1. Make sense of problems and persevere in solving them. 7.MP.2. Reason abstractly and quantitatively. 7.MP.3. Construct viable arguments and critique the reasoning of others. 7.MP.4. Model with mathematics. 7.MP.5. Use appropriate tools strategically. 7.MP.6. Attend to precision. 7.MP.7. Look for and make use of structure. || ===‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Learning Target/Task Analysis**===
 * ===**Common Core 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 (mean absolute deviation) on either team; on a dot plot, the separation between the two distributions of heights is noticeable.//
 * 7.SP.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 seventh-grade science book are generally longer than the words in a chapter of a fourth-grade science book.// || ===‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Anchor Standard/Mathematical Practice(s)**===
 * 7SP3**
 * 7SP4**
 * ===‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Information Technology Standard**=== || ===‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Revised Bloom's Level of thinking**=== ||


 * 7.SP.3: This is the students’ first experience with comparing two data sets. Students build on their understanding of graphs, mean, median, Mean Absolute Deviation (M.A.D.) and interquartile range from 6th grade. Students understand that**
 * 1. a full understanding of the data requires consideration of the measures of variability as well as mean or median,**
 * 2. variability is responsible for the overlap of two data sets and that an increase in variability can increase the overlap, and**
 * 3. median is paired with the interquartile range and mean is paired with the mean absolute deviation.**


 * Example:** Jason wanted to compare the mean height of the players on his favorite basketball and soccer teams. He thinks the mean height of the players on the basketball team will be greater but doesn’t know how much greater. He also wonders if the variability of heights of the athletes is related to the sport they play. He thinks that there will be a greater variability in the heights of soccer players as compared to basketball players. He used the rosters and player statistics from the team websites to generate the following lists.

Basketball Team – Height of Players in inches for 2010 Season 75, 73, 76, 78, 79, 78, 79, 81, 80, 82, 81, 84, 82, 84, 80, 84

Soccer Team – Height of Players in inches for 2010 73, 73, 73, 72, 69, 76, 72, 73, 74, 70, 65, 71, 74, 76, 70, 72, 71, 74, 71, 74, 73, 67, 70, 72, 69, 78, 73, 76, 69

To compare the data sets, Jason creates a two dot plots on the same scale. The shortest player is 65 inches and the tallest players are 84 inches. In looking at the distribution of the data, Jason observes that there is some overlap between the two data sets. Some players on both teams have players between 73 and 78 inches tall. Jason decides to use the mean and mean absolute deviation to compare the data sets.

The mean height of the basketball players is 79.75 inches as compared to the mean height of the soccer players at 72.07 inches, a difference of 7.68 inches.

The mean absolute deviation (MAD) is calculated by taking the mean of the absolute deviations for each data point. The difference between each data point and the mean is recorded in the second column of the table. The difference between each data point and the mean is recorded in the second column of the table. Jason used rounded values (80 inches for the mean height of basketball players and 72 inches for the mean height of soccer players) to find the differences. The absolute deviation, absolute value of the deviation, is recorded in the third column. The absolute deviations are summed and divided by the number of data points in the set. The mean absolute deviation is 2.14 inches for the basketball players and 2.53 for the soccer players. These values indicate moderate variation in both data sets.

Solution: There is slightly more variability in the height of the soccer players. The difference between the heights of the teams (7.68) is approximately 3 times the variability of the data sets (7.68 ÷ 2.53 = 3.04; 7.68 ÷ 2.14 = 3.59).

• Company A: 1.2 million, 242,000, 265,500, 140,000, 281,000, 265,000, 211,000 • Company B: 5 million, 154,000, 250,000, 250,000, 200,000, 160,000, 190,000
 * 7.SP.4 Students compare two sets of data using measures of center (mean and median) and variability MAD and IQR).Showing the two graphs vertically rather than side by side helps students make comparisons. For example, students would be able to see from the display of the two graphs that the ideas scores are generally higher than theorganization scores. One observation students might make is that the scores for organization are clustered around a score of 3 whereas the scores for ideas are clustered around a score of 5.**
 * Example 1:**The two data sets below depict random samples of the management salaries in two companies. Based on the salaries below which measure of center will provide the most accurate estimation of the salaries for each company?

Solution: The median would be the most accurate measure since both companies have one value in the million that is far from the other values and would affect the mean.

‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**I can use random sampling to draw inferences about population.**

 * (7SP3) I can compare two sets of data.**
 * (7SP4) I can use measures of central tendency to summarize data.**

‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Essential Vocabulary**

 * counting principle, sample space, compound event, independent event, dependent event, theoretical probability, experimental probability, variation/variability, distribution, measures of center, measures of variability**

‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Differentiation**

 * peer tutoring, guided notes, blogs, grouping, foldables, justification of answers, inquiry based learning to accelerate/remediate, conduct surveys and include the process of statistical measures to summarize data collected**

‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Intervention:**

 * DPI math resources: Central Tendency Discussion Cards**
 * How Much Do You Make--Central Tendency**

[[file:Measures_of_Central_Tendency_BINGO_Card_Activity.pdf]]
===‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Instructional Resources**===

[|Practice with Normal Distribution and Standard Deviation]


 * Math Partners resources,**

http://qta.quantiles.com/qtaxon/goal/29770/ http://qta.quantiles.com/qtaxon/goal/29771/

http://www.montgomery.k12.nc.us/18032092913393583/site/default.asp
 * http://www.kutasoftware.com,**
 * http://www.mathgoodies.com,**

[[file:7_Math_SPfinal.docx]]
===‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍**Notes and Additional Information**===