A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the manyvariables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regressionmodel, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, thefollowing results were obtained:y = 74.80 + 17.80xGive a practical interpretation of the estimate of the slope of the least squares line.For each additional room in the house, we estimate the appraised value to increase $74,800.For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms.For a house with O rooms, we estimate the appraised value to be $74,800.For each additional room in the house, we estimate the appraised value to increase $17,800.000 0 A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the manyvariables thought to be an important predictor of appraised value is the number f rooms in the house. Consequently, the appraiser decided to fit the simple linear regressionmodel, yb₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, thefollowing results were obtained:y= 74.80+ 17.80xGive a practical interpretation of the estimate of the slope of the least squares line.For each additional room in the house, we estimate the appraised value to increase $74,800.For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms.For a house with O rooms, we estimate the appraised value to be $74,800.For each additional room in the house, we estimate the appraised value to increase $17,800.0000.

According to the information provided in the question, x and y are two variables such that ; y :…

Answered: A county real estate appraiser wants to…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled " The relation of dive duration (DD) to depth (D)." Duration DD is measured in minutes and depth D is in meters. The report then says, " The regression equation for this bird is: DD = 2.02 + 0.0081 D. (a) What is the slope of the regression line?. ANSWER minutes per meter. (b) According to the regression line, how long does a typical dive to a depth of 400 meters last? ANSWER minutes. Speak
A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. Data were collected that show the number of new customers added during each quarter between 2015 and 2018. A multiple regression model was developed with the number of new customers as the dependent and the following four independent variables: Period (1, …, 16): A variable that measures the trend; Q1 = 1 for first quarter, Q1 = 0 otherwise; Q2 = 1 for second quarter, Q2 = 0 otherwise; Q3 = 1 for third quarter, Q3 = 0 otherwise. Questions: 1. Explain each of the four slopes (Period, Q1, Q2, Q3). 2. How many new customers would you expect in the second quarter of the following year (2019)?

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