The following model studies the effect that a chemical spill disaster had on nearby housing values. They used data from a sample of houses sold in 1980, before chemical spill disaster happened, and a separate sample ofhouses sold in 1985, after chemical spill had occurred.where the variables used in the analysis are:log(price) is natural log of selling price in $y85 is a dummy variable equal to 1 if a house was sold in 1985, 0 otherwisefar is a dummy variable equal to 1 if a house was far from the affected area, 0 otherwiseIf the chemical spill reduces the value of houses closer to the affected area, which of the following statement is correct?Select one:O a. a₁ is expected to be positiveO b.O c.O d.log(price) = a + Boy85 + a₁ far + B₁y85 * far + ua₁ is expected to be negativeB₁ is expected to be positiveB₁ is expected to be negative

Given regression model is:log(price)=α0+β0y85+α1far+β1y85*far+u, where: log(price) is the natural…

Answered: The following model studies the effect…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...

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Enter the data from Table 2 into a graphing calculator and graph the ranking scatter plot. Determine whetherthe data from the table would likely represent a function that is linear, exponential, or logarithmic.
Explain why the values of an increasing exponentialfunction will eventually overtake the valuesof anincreasing linear function.
Does a linear, exponential, or logarithmic model best fit the data in Table 2? Find the model.
What is the y -intercept on the graph of the logistic model given in the previous exercise?
Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
Graph the following logistic function, first discussed in Example 5. Use window settings of 0,20 for x and 0,1,500,000 for y. Pt=1,200,0001+1199e-0.4t
Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
A psychologist conducted a study to examine the nature of the relation, if any, between an employee’s emotional stability and the employee’s ability to perform in a task group. Emotional stability was measured by a written test for which the higher the score, the greater the emotional stability. Ability to perform in a task group was evaluated by the supervisor. An employee was given a 1 if they were able to perform a task in the group, and 0 if they were unable. Following output gives the results from a logistic regression analysis on the data provided by a sample of 27 employees. (a) what is the estimated logistic regression line? (b) Convert your slope estimate into an estimate for the odds ratio, and interpret the results. (c) Estimate the odds of the being able to perform in a task group for an employee with an emotional stability score of 510.
The data below is hurricane Irma’s wind speeds at the Key West Airport starting at midnight on September 10, 2017.(linear model) h, hours since midnight 6 9 12 24 36 44 W, wind speed in MPH 140 140 80 48 30 20 Hurricane Irma’s wind speeds at the Key West Airport starting at midnight on September 10, 2017: (Exponential Model) h, hours since midnight 6 9 12 24 36 44 W, wind speed in MPH 140 140 80 48 30 20 A.) According to your linear model for Hurricane Irma's wind speeds, what will be the wind speed 20 hours after midnight on September 10th? B.) According to your exponential model for Hurricane Irma's wind speeds, what will the wind speed be 20 hours after midnight on September 10th? C.) Look at the two models and how well the curves fit the data on your chart; also look at your predictions for the wind speeds after 20 hours. Which model seems more appropriate and why? (Fill in the banks) The_______model seems more appropriate. My prediction for the wind speed at hour…
Q3) A set of data is shown to have a linear relationship if transformed intothe function log(y)=2+5x. Transform that relationship back into a directrelationship between x and y. What is the value of y when x = 0.1?
The table below is a record of the number of miles driven between stops for gas and the amount of money spent to fill up the gas tank in Sophia’s car over several months. Sophia’s Gas Fill Up LogMiles Driven, x 346 309 336 310 325 274 324Cost to Fill up ($), y 27.69 25.08 25.74 24.56 24.94 22.05 26.28 Step 2 of 2: If Sophia needs to drive 315 miles home from college and leaves with a full tank, how much should she budget to fill up when she gets home? Assume the regression equation is appropriate for prediction. Round your answer to the nearest cent.
We estimate the following model for wages for major league baseball players: ln⁡(salary)= β0 +β1years + β2gamesyr + β3frstbase + β4scndbase + β5shrtstop + β6thrdbase + β7outfield + u where ln(salary) = the natural log of a player’s salary years = the player’s years in the major league gamesyr = the player’s average number of games played per year and frstbase, scndbase, shrtstop, thrdbase and outfield are each a dummy variable equal to 1 if the player plays that position on the field (when their team is fielding) and 0 otherwise. These are mutually exclusive dummy variables and there is one omitted fielding position, catcher. We find: *Regress lsalary years gamesyr frstbase scndbase shrtstop thrdbase outfield Source SS df MS Model Residual 300.454626 191.720909 7 345 42.9220894 0.55571278 Total 492.175535 352 1.39822595 lsalary coef. std. err. t P > |t| [95% Conf. Interval] years 0.0672574 0.0125509 5.36 0.000…

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