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normal score -2.141198121 -1.660697611 -1.400745061 -1.211232131 -1.057414228 -0.92524456 -0.807541042 -0.700090213 -0.600178776 -0.505933654 -0.41598722 -0.329291347 -0.245006223 -0.162429373 -0.08094729 0 0.08094729 0.162429373 0.245006223 0.329291347 0.41598722 0.505933654 0.600178776 0.700090213 0.807541042 0.92524456 1.057414228 1.211232131 1.400745061 1.660697611

normal score -2.141198121 -1.660697611 -1.400745061 -1.211232131 -1.057414228 -0.92524456 -0.807541042 -0.700090213 -0.600178776 -0.505933654 -0.41598722 -0.329291347 -0.245006223 -0.162429373 -0.08094729 0 0.08094729 0.162429373 0.245006223 0.329291347 0.41598722 0.505933654 0.600178776 0.700090213 0.807541042 0.92524456 1.057414228 1.211232131 1.400745061 1.660697611

MATLAB: An Introduction with Applications
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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Create a normal probability plot.

normal score -2.141198121 -1.660697611 -1.400745061 -1.211232131 -1.057414228 -0.92524456 -0.807541042 -0.700090213 -0.600178776 -0.505933654 -0.41598722 -0.329291347 -0.245006223 -0.162429373 -0.08094729 0 0.08094729 0.162429373 0.245006223 0.329291347 0.41598722 0.505933654 0.600178776 0.700090213 0.807541042 0.92524456 1.057414228 1.211232131 1.400745061 1.660697611
Transcribed Image Text:normal score -2.141198121 -1.660697611 -1.400745061 -1.211232131 -1.057414228 -0.92524456 -0.807541042 -0.700090213 -0.600178776 -0.505933654 -0.41598722 -0.329291347 -0.245006223 -0.162429373 -0.08094729 0 0.08094729 0.162429373 0.245006223 0.329291347 0.41598722 0.505933654 0.600178776 0.700090213 0.807541042 0.92524456 1.057414228 1.211232131 1.400745061 1.660697611
Step 2: It was determined in step 1 that one of the predictor variables should not be included in a linear multiple regression model. The model that will be created now has k = 3 predictor variables. y = α + B₁x₁ + ₂x₂ + √3x3 + e The hypothesis statement is Ho: B₁ B₂ = 3 = 0 = H₂: At least one of three 6,'s is not ◊ is not zero. Create a linear multiple regression model to predict the 2016 6-year graduation rate using the three remaining predictor values. Let the significance level be a = 0.05. Save the standardized residuals from the analysis so that the assumption on the random deviation, e, can be verified with a normal probability plot. To create a normal probability plot, also known as a Q-Q plot, of the standardized residuals, you will need the normal scores.csv file that contains normal scores for a n = 30 sample. Save the Standardized Residuals from the analysis, sort them in ascending order from the output and pair them with the normal scores from the document to form (normal percentile, Standardized Residuals) ordered pairs for the normal probability plot.
Transcribed Image Text:Step 2: It was determined in step 1 that one of the predictor variables should not be included in a linear multiple regression model. The model that will be created now has k = 3 predictor variables. y = α + B₁x₁ + ₂x₂ + √3x3 + e The hypothesis statement is Ho: B₁ B₂ = 3 = 0 = H₂: At least one of three 6,'s is not ◊ is not zero. Create a linear multiple regression model to predict the 2016 6-year graduation rate using the three remaining predictor values. Let the significance level be a = 0.05. Save the standardized residuals from the analysis so that the assumption on the random deviation, e, can be verified with a normal probability plot. To create a normal probability plot, also known as a Q-Q plot, of the standardized residuals, you will need the normal scores.csv file that contains normal scores for a n = 30 sample. Save the Standardized Residuals from the analysis, sort them in ascending order from the output and pair them with the normal scores from the document to form (normal percentile, Standardized Residuals) ordered pairs for the normal probability plot.
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