QUESTION 1Consider a regression model of workers' wages using vector-matrix notation andeducation data. Data are provided in the table below:Constant11PersonEducation113215310a) Using the information in the table above, discuss whether there is linearindependence in the above modelb) Compute the coefficient vector of this modelc) The researcher transformed the wage variable into natural log values using Stataand the education values were considered in terms of years. How could youinterpret the coefficient on education?d) Use the estimated coefficient vector to compute the total sum of squared errorεε.Wage1.822.141.56e) Compute the R² and explain how well education explains the variation in wages.f) Briefly comment on the reliability of the above result.

Given information: Person Wage Constant Education 1 1.82 1 13 2 2.14 1 15 3 1.56 1 10…

Answered: QUESTION 1 Consider a regression model…

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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Question 38. You have performed a linear regression analysis to explore sunflowers' growth (in meters per month) depending on the watering (in litres per day). You have estimated the regression coefficient to be ß = 1.6. What can you conclude? a) There is a significant correlation between watering and growth. b) An average sunflower growths 1.6 meters per month. c) If you give it an additional litre of water per day, there will be an additional average growth of 1.6 meters per month. d) According to the model assumptions, an additional litre of water per day will result in additional 19.2 meters of growth after one year. e) You should consider further influencing quantities.
Answer d, e, f and g
Listed below are altitudes (in thousands of feet) and outside temperatures (in degrees Fahrenheit) recorded by the author of our textbook while flying from New Orleans to Atlanta.Altitude3101422283133Temp573724-5-30-41-54Does a strong linear correlation exist between altitude and temperature? How do you know?If a strong linear correlation exists, answer the following:a.) Find a regression model (ax+b) for temperature as a function of altitude.b.) If the altitude is 18,500 feet, what would be the expected temperature?c.) If the temperature is at the freezing mark, what would be the expected altitude?
Solve the second question in regression analysis
1. Question: Malaria is a leading cause of infectious disease and death worldwide. It is also a popular example of a vector-borne disease that could be greatly aﬀected by the inﬂuence of climate change. Table 1 is a summary from a linear regression that uses dewpoint (°C) to predict malaria prevalence in West Africa.Fig. 1: Regression(a) Write the equation of the least square regression line. (b) Find the correlation coeﬃcient r. (c) IsthereastrongcorrelationbetweendewpointandmalariaprevalenceinWestAfrica? (d) Is there a negative association between dewpoint and malaria prevalence in West Africa?
Which assumption in linear regression states that residuals should be independent for each value of the independent variable? Question 10 options: Normality of errors Independence of errors Linearity Homoscedasticity
Which of the following is/are true for logistic regression model? I. The independent variable must be continuous. II. The dependent variable can only have 2 levels/categories. I only II only…
d and e
Interpret the coefficient for tenure in terms of percentage change in odds of churning (tenure is calculated/recorded in years).
QUESTION THREE: (a) Explain 5 parameters used in multivariate regression modelling-.
(b.) Consider the fictitious set of data shown below, where the line through the data is the fitted simple linear regression line. Sketch a residual plot (It doesn't need to be perfect) on the right side of this graph. What type of transformation is needed to get a proper SLR model? Write the general form of this new SLR model. §
QUESTION 1 Mr Stat was hired to do some research for the local potion master. He ran a Minitab analysis and found the regression equation for the age increase in years based on the quantity in milliliters, of Ageing Potion one consumed. Age Increase = 0.045*Quantity %3D a) What is the explanatory variable? Quantity b) What is the response variable? Age Increase Suppose the quantities in the st anged from 150ml to 740ml. If unable to predict, answer with n/a. c) How many years would someone's age increase if they drank 200ml? d) How many years would someone's age increase if they drank 100ml? n/a

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