4-24 Use the data in Problem 4-22 and develop a regres- sion model to predict selling price based on the square footage, number of bedrooms, and age. Use this to predict the selling price of a 10-year-old, 2,000-square-foot house with three bedrooms. 2:4-22 The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Develop three regression models to pre- dict the selling price based upon each of the other factors individually. Which of these is best? SELLING SQUARE PRICE ($) 84,000 79,000 91,500 120,000 127,500 132,500 145,000 164,000 155,000 168,000 172,500 174,000 175,000 177.500 184,000 195.500 195,000 FOOTAGE BEDROOMS 1,670 1,339 1,712 1,840 2,300 2,234 2,311 2,377 2,736 2,500 2,500 2,479 2,400 3,124 2,500 4,062 2,854 2 2 3 3 3 3 3 3 4 3 4 3 3 4 3 4 3 AGE (YEARS) 30 25 30 40 18 30 19 7 10 1 3 3 1 0 2 10 3

4-24 Use the data in Problem 4-22 and develop a regres- sion model to predict selling price based on the square footage, number of bedrooms, and age. Use this to predict the selling price of a 10-year-old, 2,000-square-foot house with three bedrooms. 2:4-22 The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Develop three regression models to pre- dict the selling price based upon each of the other factors individually. Which of these is best? SELLING SQUARE PRICE ($) 84,000 79,000 91,500 120,000 127,500 132,500 145,000 164,000 155,000 168,000 172,500 174,000 175,000 177.500 184,000 195.500 195,000 FOOTAGE BEDROOMS 1,670 1,339 1,712 1,840 2,300 2,234 2,311 2,377 2,736 2,500 2,500 2,479 2,400 3,124 2,500 4,062 2,854 2 2 3 3 3 3 3 3 4 3 4 3 3 4 3 4 3 AGE (YEARS) 30 25 30 40 18 30 19 7 10 1 3 3 1 0 2 10 3

Survey Of Economics

10th Edition

ISBN:9781337111522

Author:Tucker, Irvin B.

Publisher:Tucker, Irvin B.

Chapter1: Introducing The Economic Way Of Thinking

Section1.A: Applying Graphs To Economics

Problem 2SQP

See similar textbooks

Similar questions

Briefly describe the concept of VIF (what is it, why do we use it) and explain how to calculate VIF in a model with 3 independent variables.
Which are behavioural equations in the model?
s EconomicsQ&A LibraryEach term (3 months) the current group of economics students completed a questionnaire as to how much they would spend on new purchases compared to how much they would save/pay off bills, if they suddenly and unexpectedly received a check for $1,000. The average MPC is shown in the table below. Month in which student poll was taken Average of students’ responses as MPC March 0.41 June 0.30 September 0.22 December 0.56 What do these MPC’s imply about the students’ thinking over the course of the year? (Enter response here.) What is likely happening in the economy during the same period of time? Each term (3 months) the current group of economics students completed a questionnaire as to how much they would spend on new purchases compared to how much they would save/pay off bills, if they suddenly and unexpectedly received a check for $1,000. The average MPC is shown in the table below. Month in which student poll was taken Average of students’…
estimate this model: purchases = β0 + β1social + β2aggregator + β3substack
3. Rubax, a U.S. manufacturer of athletic shoes, estimates the following linear trend model for shoe sales: Qt = a+bt+c1D1+c2D2+c3D3Where, Qt = sales of athletic shoes in the t-th quartert = 1, 2, . . . . , 28 [2011(I), 2011(II), . . . . , 2017(IV)]D1 = 1 if t is quarter I (winter); 0 otherwiseD2 = 1 if t is quarter II (spring); 0 otherwiseD3 = 1 if t is quarter III (summer); 0 otherwiseThe regression analysis produces the following results: Is there sufficient statistical evidence of an upward trend in shoe sales? explain Do these data indicate a statistically significant seasonal pattern of sales for Rubax shoes? If so, what is the seasonal pattern exhibited by the data? Using the estimated forecast equation, forecast sales of Rubax shoes for 2018(III) and 2019(II).
We have estimated the impact of gross domestic product (GDP), energy consumption (ENERGY) and population (POP) on CO2 emiisions (CO2) in Cyprus. The results are as follows;Dependent Variable: CO2Method: Least SquaresDate: 04/20/17 Time: 09:46Sample: 1990 2013Included observations: 24 Write down the economic function for the above estimation by using the information obtained from above table b- Write down the economic model for the above estimation by using the information obtained from above table . c- Write down the econometric model for the above estimation by using the information obtained from above table d- Write down the least squares line by using the information obtained from above tablee- Explain what each coefficient of the least squares line indicates
Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.
Question 2.A write a short note about the following variables and support your answers by giving examples for each variable: 1.VC: 2.MC:
Can you provide an explanation of the graph.
A10 Write three econometric models capturing 1) overall; 2) within; and 3) between variation
Given the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…
Pls help with below homework-) Which are the three methods to handle the panel data model with fixed effects ? Also explain each method