options: Measurement & Data: students learn to work with data and measure attributes such as time, money, length, area, and volume. They may learn to compare measurements to operations and convert between different units of measure.

Statistics & Probability: students learn to analyze and produce data distributions, and build understanding of univariate and bivariate data. They learn to interpret data, make statistical inferences, justify conclusions, understand rules of probability, and use probability to make decisions.

Ratios & Proportional Relationships: students learn to recognize, describe, represent, and reason with ratios, rates, proportional relationships, and percent.

Operations & Algebra: students learn to solve problems using algebraic thinking and operations such as addition, subtraction, multiplication, and division. They may learn to identify and explain arithmetic patterns, evaluate or manipulate numerical expressions, and reason with equations and inequalities.

Functions: students learn to define, use, and evaluate functions to model relationships between quantities.

Number & Operations in Base Ten: students learn to work with the base-ten system and build place value understanding.

Counting & Cardinality: students learn to know number names and the count sequence, count to tell the number of objects, and compare numbers.

Modeling: students learn to choose and use appropriate mathematics and statistics to analyze real-world empirical situations, improve decisions, and report on their conclusions and the reasoning behind them.

Number Systems and Quantity: students learn to understand the complex number system and reason quantitatively.

Number & Operations - Fractions: students learn to understand fractions as numbers and may work with them using addition, subtraction, multiplication, and division.

Geometry: students learn to classify geometric figures by their properties, and understand and compare the relationships between them. Students develop and use formulas to compute lengths, areas, and volumes, and they use transformations to generate new shapes and compare existing ones. Geometry can be studied with and without coordinates.
problem_activity: Task
Many counties in the United States are governed by a county council. At public county council meetings, county residents are usually allowed to bring up issues of concern. At a recent public County Council meeting, one resident expressed concern that 3 new coffee shops from a popular coffee shop chain were planning to open in the county, and the resident believed that this would create an increase in property crimes in the county. (Property crimes include burglary, larceny-theft, motor vehicle theft, and arson -- From http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2010/crime-in-the-u.s.-2010/property-crime accessed on December 5, 2012.)
To support this claim, the resident presented the following data and scatterplot (with the least-squares line shown) for 8 counties in the state:
{"table": [{"_attributes": {"class": ["centered", "narrow"]}, "tbody": [{"tr": [{"th": [{"_value": "County"}, {"_value": "Shops"}, {"_value": "Crimes"}]}, {"td": [{"_value": "A"}, {"_value": "9"}, {"_value": "4000"}]}, {"td": [{"_value": "B"}, {"_value": "1"}, {"_value": "2700"}]}, {"td": [{"_value": "C"}, {"_value": "0"}, {"_value": "500"}]}, {"td": [{"_value": "D"}, {"_value": "6"}, {"_value": "4200"}]}, {"td": [{"_value": "E"}, {"_value": "15"}, {"_value": "6800"}]}, {"td": [{"_value": "F"}, {"_value": "50"}, {"_value": "20800"}]}, {"td": [{"_value": "G"}, {"_value": "5"}, {"_value": "2800"}]}, {"td": [{"_value": "H"}, {"_value": "24"}, {"_value": "15400"}]}]}]}]}
[IMG]
The scatterplot shows a positive linear relationship between "Shops" (the number of coffee shops of this coffee shop chain in the county) and "Crimes" (the number of annual property crimes for the county). In other words, counties with more of these coffee shops tend to have more property crimes annually.
Does the relationship between Shops and Crimes appear to be linear? Would you consider the relationship between Shops and Crimes to be strong, moderate, or weak?
Compute the correlation coefficient.  Does the value of the correlation coefficient support your choice in part (a)? Explain.
The equation of the least-squares line for these data is:
$$ \text{Predicted Crimes} = 1434 + 415.7 \text{(Shops)} $$
Based on this line, what is the estimated number of additional annual property crimes for a given county that has 3 more coffee shops than another county?
Do these data support the claim that building 3 additional coffee shops will necessarily cause an increase in property crimes?  What other variables might explain the positive relationship between the number of coffee shops for this coffee shop chain and the number of annual property crimes for these counties?
If the following two counties were added to the data set, would you still consider using a line to model the relationship? If not, what other types (forms) of model would you consider?
{"table": [{"_attributes": {"class": ["centered", "narrow"]}, "tbody": [{"tr": [{"th": [{"_value": "County"}, {"_value": "Shops"}, {"_value": "Crimes"}]}, {"td": [{"_value": "I"}, {"_value": "25"}, {"_value": "36900"}]}, {"td": [{"_value": "J"}, {"_value": "27"}, {"_value": "24100"}]}]}]}]}


correct_option_index: 1