(Question 4) In the simple linear regression model,Y; = Bo + B, X, + e,suppose that E[eX.] = 2. Show that this model can be rewritten with a newintercept (8) such that E[e,X.] = 0%3D

In a simple linear regression model, we have an equation that relates a dependent variable (Y) to an…

Answered: (Question 4) In the simple linear…

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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Plot the linear regression line y = 3 + 0.5x without using excel with proper labelling
If the equation for the regression line is y^2=7x-7, then a value of x=2 will result in a predicted value for y of
A negative correlation between variables X and Y will always result in a positive slope in the linear regression model. Cannot tell from the given information. False True
Finally, the researcher considers using regression analysis to establish a linear relationship between the two variables – hours worked per week and yearly income. (a) Estimate a simple linear regression model and present the estimated linear equation. Display the regression summary table and interpret the intercept and slope coefficient estimates of the linear model. (b) Display and interpret the value of the coefficient of determination, R-squared (R2). Data Hours Per Week Yearly Income ('000's) Class 18 43.8 13 44.5 18 44.8 25.5 46.0 11.5 41.2 18 43.3 16 43.6 27 46.2 27.5 46.8 30.5 48.2 24.5 49.3 32.5 53.8 25 53.9 23.5 54.2 30.5 50.5 27.5 51.2 28 51.5 26 52.6 25.5 52.8 26.5 52.9 33 49.5 15 49.8 27.5 50.3 36 54.3 27 55.1 34.5 55.3 39 61.7 37 62.3 31.5 63.4 37 63.7 24.5 55.5 28 55.6 19 55.7 38.5 58.2 37.5 58.3 18.5 58.4 32 59.2 35…
Please answer both parts
I am doing as you instructed, but I keep getting an error "regression - input range non-numeric data". What can I do to solve this? Please help!
The regression equation between X and Y is Y = 1.2 X + 6 Predict the value for x = 5?
The authors of the paper "Power-Load Prediction Based on Multiple Linear Regression Model"t were interested in predicting the load on the electric power system in China using data on y = Power consumption (in hundreds of millions of kwh), x, Population (in millions), and x, = Gross domestic %3D product (in billions of dollars), for 21 years. The model equation proposed in the paper is y = -113,527 + 0.974x, + 0.057x, + e. (a) According to this model, what is the mean power consumption (in hundreds of millions of kwh) for a year if the population was 160,000 million and the gross domestic product was 600,000 billion dollars? hundreds of millions of kwh (b) Interpret the value of B, in this model. When the [ gross domestic product v is fixed, the mean increase in [power consumption (in hundreds of millions of kwh) V associated with a 1-million unit increase in [population is 0.974.
A) A linear regression has a =6 and b=5 what is y predicted as when x=9? B) A linear regression has b=3 and a=4.What is the predicted Y for x=7?
also compute the regression equation in which you predict Y using X as the predictor variable
Data was gathered from a random sample of young mothers between the ages of 15 and 19 years, and the relationship between the mother's age (measured in years) and the baby's birth weight (measured in grams) was observed to be linear, with r= 0.88. Further, the regression equation to predict a baby's birth welght based on the mother's age was found to be: Predicted birth weight = -1163.45 + 245.15(age). Based on this information, which one of the following statements is incorrect? O The predicted birth weight for a baby whose mother is 15 years old is 4840.7 grams. O If weight was measured in pounds instead of grams, the value of rwould still be 0.88. O If we switch the variables so that age is the response variable and birth weight is the explanatory variable, r would still be 0,88. O If a mother's age is 25 years, we would not want to use the regression equation to predict the baby's birth weight since this would be considered extrapolation. O Becauseris 0.88, we would consider the…
The equation used to predict annual cauliflower yield (in pounds per acre) is y = 24,121 +4.505x₁ -4.772x₂, where x₁ is the number of acres planted and x2 is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x₁ =35,100, x₂ =35,500 (b) x₁ = 36,800, X2=37,100 (c) x₁ = 37,900, x₂ = 38,000 (d) x₁ =41,100, x₂ = 41,200 (a) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (b) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (c) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (d) The predicted yield is pounds per acre (Round to one decimal place as needed.)

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(Question 4) In the simple linear regression model, Vipit tôn suppose that Ele|X₁] = 2. Show that this model can be rewritten with a new intercept (30) such that EleX]=0