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##### "1. A machine is supposed to mix peanuts, hazelnut...

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"1. A machine is supposed to mix peanuts, hazelnuts, cashews and pecans in the ratio 5:2:2:1. A can containing 500 of these mixed nuts was found to have 269 peanuts, 112 hazelnuts, 74 cashews, and 45 pecans. At the 0.05 level of significance, conduct a nonparametric test that the machine is mixing nuts in the ratio 5:2:2:1. 2. A mathematics placement test is given to all entering freshman at a small college. A student who receives a grade below 35 is denied admission to the regular mathematics course and placed in a remedial class. The placement test scores and final grades for several students who took the regular course are recorded and shown below Placement Test Course Grade 34 53 35 41 58 82 44 63 55 68 Conduct a regression analysis to predict course grade (dependent variable) on the basis of the placement test score (independent variable) a. Determine the coefficient of determination for this regression problem and comment on its capability to predict outcome b. Conduct an F test to determine the relationship between the two variables. Conduct the test at alpha = .05 level c. What is the predicted grade for a placement test score of 60? d. What is the upper and lower value for a 90% confidence interval of the grade estimate determined in part c 3. Three different machines are being considered for use in manufacturing rubber seals. The machines are being compared with respect to tensile strength of the product. A random sample of 4 seals from each machine is used to determine whether the mean tensile strength varies from machine to machine. Perform an ANOVA test to determine whether the tensile strengths are equivalent for the three machines. Use ? = .05. The following are tensile strength measurements in kilograms per square centimeter: Machine Samples M1 M2 M3 17.5 16.4 20.3 16.9 19.2 15.7 15.8 17.7 17.8 18.6 15.4 18.9 Sample Mean 17.20 17.18 18.18 Sample Standard Deviation 1.17 1.65 1.94 Grand Mean 17.52 4. The file midcity.xls (sheet 1 ? Data) contains data on 128 recent real-estate sales in Mid City. For each sale, the file shows the neighborhood (1, 2 or 3) in which the house is located, the number of offers made on the house, the square footage, the number of bathrooms, the number of bedrooms, and selling price. Neighborhoods 1 and 2 are more traditional neighborhoods, whereas neighborhood 3 is newer, more prestigious neighborhood. Use multiple regression to interpret the pricing structure of houses in Mid City and answer the following questions (note , the first column in the MidCity file is home or sample number. Do not include this variable in the regression equation): a. Comment on the models ability to predict price of the home based on the given variables. Is this a good predictor model? Why or why not? b. Is there a relationship between the independent and dependent variables? Why or why not (test at alpha = .05)? c. Comment on the contribution of each of the variables (including the intercept). State whether the variables (and intercept) contribute to the linear prediction of the model. Why or why not (test at alpha = .05)? d. What should the selling price be for a house in neighborhood 3, with 3000 square feet, 5 bedrooms, 3 bathrooms and 5 offers on the home? 5. The file midcity.xls (sheet 2 ? Data Modified) contains the same data on 128 recent real-estate sales in Mid City except one other variable (brick) has been added. For each sale, the file shows the neighborhood (1, 2 or 3) in which the house is located, the number of offers made on the house, the square footage, the number of bathrooms, the number of bedrooms, whether the home has brick or no brick and selling price. Neighborhoods 1 and 2 are more traditional neighborhoods, whereas neighborhood 3 is newer, more prestigious neighborhood. Use multiple regression to interpret the pricing structure of houses in Mid City and answer the following questions (note , the first column in the MidCity file is home or sample number. Do not include this variable in the regression equation): a. Comment on the models ability to predict price of the home based on the given variables. Is this a good predictor model? Why or why not? b. Is there a relationship between the independent and dependent variables? Why or why not (test at alpha = .05)? c. Comment on the contribution of each of the variables (including the intercept). State whether the variables (and intercept) contribute to the linear prediction of the model. Why or why not (test at alpha = .05)? d. What should the selling price be for a house in neighborhood 3, with 3000 square feet, 5 bedrooms, 3 bathrooms 5 offers and brick on the home? ",i need it all done with excel,i couldnt open the file i dont even want it anymore. it was too late i didnt see any answers. need my deposit back

Paper#13565 | Written in 18-Jul-2015

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