For the horseshoe crab data, fit the logistic regression model for π = probability of a satellite, using weight as the predictor. Data source (Horseshoe crab data set of Table 4.3): https://users.stat.ufl.edu/~aa/cda/data.html Note: In the original data set, weights are in grams rather than kg. Correct the unit by adding a new column which convert weight into kg and run the logistic regression with correct weight variable. a. Report the ML prediction equation. b. Find at the weight values 1.20, 2.44, and 5.20 kg, which are the sample minimum, mean, and maximum. c. Find the weight at which = 0.50 d. Ignore part d e. Construct a 95% confidence interval to describe the effect of weight on the odds of a satellite. Interpret. f. Conduct the Wald or likelihood-ratio test of the hypothesis that weight has no effect. Report the P -value and interpret.
For the horseshoe crab data, fit the logistic regression model for π = probability of a satellite, using weight as the predictor. Data source (Horseshoe crab data set of Table 4.3): https://users.stat.ufl.edu/~aa/cda/data.html Note: In the original data set, weights are in grams rather than kg. Correct the unit by adding a new column which convert weight into kg and run the logistic regression with correct weight variable. a. Report the ML prediction equation. b. Find at the weight values 1.20, 2.44, and 5.20 kg, which are the sample minimum, mean, and maximum. c. Find the weight at which = 0.50 d. Ignore part d e. Construct a 95% confidence interval to describe the effect of weight on the odds of a satellite. Interpret. f. Conduct the Wald or likelihood-ratio test of the hypothesis that weight has no effect. Report the P -value and interpret.
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 94E
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For the horseshoe crab data, fit the logistic regression model for π = probability of a satellite, using weight as the predictor.
Data source (Horseshoe crab data set of Table 4.3): https://users.stat.ufl.edu/~aa/cda/data.html
Note: In the original data set, weights are in grams rather than kg. Correct the unit by adding a new column which convert weight into kg and run the logistic regression with correct weight variable.
a. Report the ML prediction equation.
b. Find at the weight values 1.20, 2.44, and 5.20 kg, which are the sample minimum, mean , and maximum.
c. Find the weight at which = 0.50
d. Ignore part d
e. Construct a 95% confidence interval to describe the effect of weight on the odds of a satellite. Interpret.
f. Conduct the Wald or likelihood-ratio test of the hypothesis that weight has no effect. Report the P -value and interpret.
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VIEWStep 2: Fit the logistic regression model for π = probability of a satellite, using weight as the predictor.
VIEWStep 3: Predict the the ML prediction equation using the weight as the predictor.
VIEWStep 4: Determine probability at given weight values and weight value at probability 0.50.
VIEWStep 5: Determine the 95% confidence interval to describe the effect of weight on the odds of a satellite.
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