At Westonci.ca, we connect you with the best answers from a community of experienced and knowledgeable individuals. Discover detailed solutions to your questions from a wide network of experts on our comprehensive Q&A platform. Join our platform to connect with experts ready to provide precise answers to your questions in different areas.
Sagot :
The residuals of the linear regression equation are 0.329, -0.346, 0.064, -0.326, 0.049, -0.286, -0.161, 0.214, 0.429 and 0.059
How to determine the residuals?
The regression equation is given as:
y = 0.005x + 3.111
Next, we calculate the predicted values (y) at the corresponding x values.
So, we have:
y = 0.005 * 4 + 3.111 = 3.131
y = 0.005 * 3 + 3.111 = 3.126
y = 0.005 * 5 + 3.111 = 3.136
y = 0.005 * 3 + 3.111 = 3.126
y = 0.005 * 8 + 3.111 = 3.151
y = 0.005 * 15 + 3.111 = 3.186
y = 0.005 * 10 + 3.111 = 3.161
y = 0.005 * 15 + 3.111 = 3.186
y = 0.005 * 6 + 3.111 = 3.141
y = 0.005 * 6 + 3.111 = 3.141
The residuals are then calculated using:
Residual = Actual value - Predicted value
So, we have:
y = 3.46 - 3.131 = 0.329
y = 2.78 - 3.126 = -0.346
y = 3.2 - 3.136 = 0.064
y = 2.8 - 3.126 = -0.326
y = 3.2 - 3.151 = 0.049
y = 2.9 - 3.186 = -0.286
y = 3 - 3.161 = --0.161
y = 3.4 - 3.186 = 0.214
y = 3.57 - 3.141 = 0.429
y = 3.2 - 3.141 = 0.059
Hence, the residuals of the linear regression equation are 0.329, -0.346, 0.064, -0.326, 0.049, -0.286, -0.161, 0.214, 0.429 and 0.059
See attachment for the residual plot.
The residual plot shows that the linear model from the regression calculator is a good model because the points are not on a straight line
Read more about residuals at:
brainly.com/question/16180255
#SPJ1
Thanks for stopping by. We strive to provide the best answers for all your questions. See you again soon. Your visit means a lot to us. Don't hesitate to return for more reliable answers to any questions you may have. Westonci.ca is here to provide the answers you seek. Return often for more expert solutions.