In the analysis of housing prices, researchers often specify an econometric model of the following form: 5. Iprice, = B, + B,S, + ByN; + BspSD, +€, where Iprice, denotes the log of the sale price of house i, S, is a set of structural attributes of house i (such as living area, presence of a view, and number of bathrooms), N, is a set of neighborhood/community characteristics (such as the crime rate and air quality), SD,is a set of school district dummy variables that take the value of one if the ith house is located in that school distriet and €, is a random disturbance term. In a recent study, this specification was used to estimate the determinants of housing values using data on homes that sold within Los Angeles and Orange County. Definitions of the variables used in the study are reported below. Variable Definition Units of Measurement Structural Attributes PRICE Sale Price Dollars BATH Number of Bathrooms Number LIVAREA Interior Living Space Square Feet POOL Presence of Pool Zero/One VIEW Presence of a View Zero/One Neighborhood/Community Attributes BEACH Distance to Nearest Beach Miles BEACHSQ Distance to Beach Squared Miles CRIME Per Capita FBI Crime Index Crimes/Population TWORK Time to Work Minutes AIRQ Air Quality Miles of Visibility SCHOOL DISTRICT Location in Specific School Districts Zero/One Estimated Hedonic Equations Dependent Variable = Ln(Home Sale Price) Variable (Coefficient) Coefficient (St. Error) Structural Attributes BATH ( BI) .06 (.002) POOL (B2) .07 (.05) LIVAREA (B) 00035 (000002) VIEW (B4) .09 (.01) Neighborhood/Community Attributes BEACH (Bs) -015 4002) BEACHSQ (B6 ) 0.0002 (.0001) CRIME (B7) -.001 (.0001) TWORK (B) -.015 (.001) AIRQ (B») .02 (.001) School District Beverly Hills (Bi0) 39 (.002) Compton ( B1) 22 (.06) Orange (B12) .07 (05) Laguna Beach ( B13) .43 (.10) INTERCEPT ( Bo) 12.79 R-Square .55 Number of Observations 41,852

The linearization of a function works best when it is used to approximate a function, meaning behaviour and value, close to the point of tangency.  A. True B. False

Student Loans Data for selected years from 2011and projected to 2023 can be used to show that thebalance of federal direct student loans y, in billions ofdollars, is related to the number of years after 2010,x, by the function y = 130.7x + 699.7.a. Graph the function for x-values corresponding2010 to 2025.b. Find the value of y when x is 13.c. What does this function predict the balance offederal direct student loans will be in 2029?(Source: U.S. Office of Management and Budget)

Let f(x) be the height of a maple tree in inches and the number of years since 2000. A linear model for the data is f(x) = 9.44x + 84.182. 220 210- 200 190 180 170 160 150- 140- 130- 120 110 100 -90- ge 70- -60 -50+ 3 Ő 9 12 A) To the nearest inch, estimate the height of the maple tree in 2013. inches B) Use the equation to find the year in which the tree will be 250 inches tall.

Recently there has been a rise in fatal accidents caused by “distracted driving.” The United States National Highway Traffic Safety Administration (NHSTA) defines a distraction as anything that diverts attention from driving. The following model estimates the trend in distracted driving accidents over time based on data from the NHSTA:D = 0.015t - 29.97 In this model, D is the decimal to be turned into a percentage of all fatal accidents caused by distracted driving, and t is any calendar year between 2000 and 2020. Use the model to estimate the rate for 2005 and 2020. Note: this means to plug n 2005 for t and simplify and then turn your answer into a percent.  Then plug in 2020 for t and simplify and then turn your answer into a percent.