Please answer all 3 questions
1 answer:
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
<h3>How to determine the residuals?</h3>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:
#SPJ1
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=>
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Generally since the sample proportion is not given we will assume it to be
Generally the sample size is mathematically represented as
=>
=>
Generally the margin of error is mathematically represented as
Generally if the level of confidence increases, the critical value of increase and from the equation for margin of error we see the the critical value varies directly with the margin of error , hence the margin of error will increase also
So If the confidence level is increased, then the sample size would need to increase because a higher level of confidence increases the margin of error.