Consider the linear regression model Y; = Bo + B1X; +U; for each i = 1,...,n with n = 1, 000. X; represents the annual income ofindividual i (measured in $10,000) and Y; represents the home size (measured in square feet). We run an OLS regression and get:B1n = 43.2, SE(B1m)= 10.2,Ban0,m = 700, SE(B0,n) = 7.4.Assuming that the sample size is large enough, which one of the following is true about the 99% confidence interval for B1?a. The 99% confidence interval for Bị is [43.2 – V10.2 * 2.57, 43.2 + V10.2 * 2.57]O b. The 99% confidence interval for B1 is [43.2 – 10.2 * 1.96, 43.2+ 10.2 * 1.96]The 99% confidence interval for B1 is 43.2 – 10.2 * 2.57, 43.2 +10.2 * 2.57O d. None of the answers

The independent variable is annual income of individual. The dependent variable is home size. This…

Answered: Consider the linear regression model Y;…

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 real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes
Q5/ Use Linear Regression to fit the following data: X 1 2 4 5 6 Y 4 10 10 9 3
There is a linear relationship between the number of chirps made by the stiped ground cricket and the air temperature. It was determined that the linear regression model is: y = 25.2 + 3.3x where x is the number of chirps per minute and y is the estimated temperature in degrees Fahrenheit. What is the predicted number of chirps made when the temperature is 60 degrees Fahrenheit? Round to the nearest integer. Do not include units.
An instructor asked a random sample of eight students to record their study times at the beginning of a course. She then made a table for total hours studied (x) over 2 weeks and test score (y) at the end of the 2 weeks. The table is given below. Complete parts (a) through (f). x 10 13 10 18 6 15 16 21 y 93 79 81 74 85 81 85 80 a. Find the regression equation for the data points. b. Graph the regresson equation c. Describe the apparent relationship between the two variables. d. Identify the predictor and response variables. e. Identify outliers and potential influential observations. f.Predict the score for a student that studies for 17 hours.
A student used multiple regression analysis to study how family spending (y) is influenced by income (X1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained.
Please solve what’s not filled in.
Which of the following is true of a linear regression line? a. Located as close as possible to all the points of a scatter chart. B. Is defined by an equation having 2 parameters: the slope and the intercept c. Provides an approximate relationship between the values of two parameters d. All of the above
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs = 247733.3 +(43.2000) x Billable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) b. Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, bo. if appropriate. Choose the correct answer below. OA. The value of by is the predicted overhead costs for 0 billable hours. OB. For each increase of 1 unit in overhead costs, the predicted billable hours are estimated to increase by bo OC. It is not appropriate to interpret by. because its value is the predicted…
8
Define both x and y in all problems. x is the cause, and y is the effect. This is the most important step when doing linear regression, otherwise, all the remanding parts will be wrong.
In the graph shown below consider the horizontal line to represent the average y value for the data set and the slant line to represent the linear regression equation. use only the graph of the data to do the following:

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