Directions: Type your responses to each question in a Word document. All analysis should be completed using JMP. Type all answers and use JMP-generated tables and graphs in a single Word document.
Show all steps and all work. You may assume an alpha level of 0.05 unless otherwise noted. To answer each question, copy the output from JMP, showing enough detail to understand the analysis setup and the critical output used to answer the question. Then, type your answer based on this output, even if it means repeating the same numbers shown in the JMP output. This is your formal answer, supported by analysis in JMP.
There are 108 points total in this assignment. Points are broken down by problem.
Exception: you do not need to write out equations and numbers for finding means and standard deviations.
Submitting your assignment: Put your name in the upper left corner of the first page. Email the Word or pdf document. The file name must be in the following format: LastNameFirstInitial_JMP1.docx or .pdf (10% penalty otherwise).
Module 3
Problem 1 (48 points): An engineer wants to predict the seal strength on packaging used for gauze surgical sponges based on suspected significant variables of temperature, pressure, time, humidity, and vendor. One hundred observations were taken, and a subset of the data is provided in the Moodle assignment (IE4362_JMP2_P1).
1. First examine the dataset to determine which variables are meaningful and which might be correlated (and therefore unnecessary). Do this through 2 steps:
a. (8) Examine the correlations between the input variables. Show the correlations table and discuss the relationship between each pair in terms of direction and strength.
b. (12) Develop a scatterplot and simple linear regression model between seal strength and each input variable. Discuss which variables appear to have a meaningful impact on strength, and how you came to that conclusion.
2. Conduct a forward stepwise regression to find the best model of seal strength from the 5 input variables provided.
a. (2) Based on the scatterplots and simple linear regressions developed in 1.b., which variable should be entered first?
b. (5) Show the JMP output for the final model.
c. (3) What is the overall significance of the model, and does it indicate a good fit for the data?
d. (3) How much variance is explained by the model, and does it indicate a good fit for the data?
e. (3) Which variables should be included in the final model, and why?
f. (3) How much variance is NOT explained by the model? List a few potential causes for the unexplained variance.
3. (6) Write the final model equation and specify the ranges of each variable for which the model is valid.
4. (3) Evaluate the final model: Would you recommend that the engineer use this model to predict seal strength? Give a yes/no answer and reasoning.
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