7. Two analysts at a bank want to determine an appropriate credit limit for new customers with agiven credit score using existing data on credit limit decisions. The first analyst studies customerswith 'good' credit, and the second studies customers with 'excellent' credit. For the purpose of thisexercise, suppose these are the only two categories. The analysts separately report the followingfitted equations:'Good' i'Excellent' ŷi==1000 +0.5scorei.1500 +0.7scoreį.a) Draw these fitted regression lines on the same diagram.b) Suppose 'Good' credit is scored between 0-400 and 'Excellent' credit 400-800. Add to your fittedequations a cloud of points you might reasonably expect to see.c) Explain how you could combine the information from both of these univariate regressions byrunning a single (multivariate) linear regression. Deduce from the information provided what theparameter estimates would be in your multivariate regression. What is the estimated size of the‘jump'? (The ‘jump' does not occur at 0). What is the estimated 'kink'?

Answered: Image /qna-images/answer/b5dbe506-1395-4a8e-951d-f90986289607.jpg

Answered: a) Draw these fitted regression lines…

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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The manager of the Bayville police department motor pool wants to develop a forecast model for annual maintenance on police cars, based on mileage in the past year and age of the cars. The following data have been collected for eight different cars: a. Using Excel, develop a multiple regression equation for these data. b. What is the coefficient of determination for this regression equation? c. Forecast the annual maintenance cost for a police car that is 5 years old and will be driven 10,000 miles in 1 year.
For a two-variable linear regression, if the sample correlation between the depen- dent and independent variables is –0.7, then the independent variable explains 49% of the variation in the dependent variable. Is this statement true? Explain your answer.
There are 6 parts for this Question. Please help
The adjusted R-squared accounts for the amount of variance explained while also adjusting for the number of independent variables in the linear regression equation. True or False
Stoaches are fictional creatures that nest in truffula forests. A researcher wants to know whether there is a relationship between a stoach’s wingspan (?W, the predictor) and its nest height (?H, the response). A sample of 88 stoaches is observed, and for each, the wing-span (in cm) and the nest height (in m) are recorded. The observed data meet the assumptions for a linear regression, so the researcher fits the regression model and obtains a regression equation ℎ̂=−0.813+0.177?,h^=−0.813+0.177w, with standard error for the coefficient of ?w equal to 0.448. Determine the ?p-value from a test for a statistically significant linear dependence of nest height on wing-span. (Give your answer to 4 decimal places.
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 many variables 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 regression model, 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, the following results were obtained: y=74.80+ 17.80x Give 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. 1000 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 0 rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…
Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions?
What is C,D and E? And how do i calculate it??
Suppose you wanted to test whether or not the payoff to an additional year of education was the same for men and women in the STEM majors. How would you set up your regression analysis in this case
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