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Required This is take home final exam. You are expected to do your own work. If the exact same answer appears on two or more final exams then you...

please help me this homework, the date line is at the end of this week

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Required This is take home final exam. You are expected to do your own work. If the exact same answer appears on two or more final exams then you will prosecuted for academic dishonesty. The penalty would be a zero on the final exam. Be sure you explain your analysis. That is, you must explain why you are doing the specific analysis. Of course, it could be that I request that analysis. If I do you do not need to explain your answer. At a minimum you must fit the requested model, validate assumptions, and interpret the estimated regression coefficients. The final exam is due on Thursday, December 11, 2012, by 8:00 PM . Late finals will be penalized at the rate of 25% later per day. 1. Predicting Old Faithful eruptions 1.1 Data source The data for this problem is sampled using the JSL script O LD F AITHFUL . The script has been encrypted so information systems people can’t read the program code. Run the script to generate the JMP data table. Be sure to save the data table. 1.2 The story Your data table gives data about the eruptions of Old Faithful Geyser during October 1980. The variables are the Duration of an eruption measured in seconds of the current eruption and the Interval , the time to the next eruption measured in minutes. The data were collected by volunteers and provided by R. Hutchinson. Apart from missing the data from midnight to 6 AM, this is a complete record of the eruptions for that month. Old Faithful Geyser is an important tourist attraction, with up to several thousand people watching it on summer days. The National Park Service uses data like these to obtain prediction equation for the time with up to several thousand people watching it on summer days. The National Park Service uses data like these to obtain prediction equation for the time to the next eruption. Your job will be to develop a prediction equation that can be used to predict the interval to the next eruption of Old Faithful based on the prior eruption. 1.2 Required Our goal here is to compare the predictive ability of a simple linear regression model fit to the data against a polynomial model fit to the data. 1.2.1 Simple linear regression Fit a simple inner regression model to the data. The predictor variable is Duration and the response variable is Interval . Be sure to carry out all the appropriate diagnostic checks for model validity. Give a complete summary of the model. …………………………………………………………………………………………… ……………………………………………….
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Bivariate Fit of Interval By Duration 30 40 50 60 70 80 90 Interval (Seconds) 50 100 150 200 250 300 Duration (Seconds) Linear Fit Local Smoother Linear Fit Interval = 31.301 + 0.187*Duration The relationship between Interval and Duration appears to be line because the OLS line and the losses line are relatively close and parallel to each other. Summary of Fit RSquare 0.85 RSquare Adj 0.85 Root Mean Square Error 5.93 Mean of Response 67.09 Observations (or Sum Wgts) 249.00 Analysis of Variance Source DF Sum of Squares Mean Square Model 1.00 49196.61 Error 247.00 8677.35 C. Total 248.00 57873.95 Parameter Estimates Term Estimate Std Error Intercept 31.30 1.03 Duration 0.19 0.01 Local Smoother R-Square 0.858075Sum of Squares Error 8213.782Local Fit (lambda) Weight Function Smoothness (alpha): 0.667Robustness ……………………………………. . The standard form of the estimated simple linear regression model: = 31.03 + 0.18Duration (The estimated Type equation) The intercept coefficient has no practical interpretation. The estimate slope coefficient means that for every addition second of the eruption the waiting time to next eruption increase by an estimated average of 0.18 minutes The mean deviation form of the estimated regression equation: To write the equation in mean deviation form, we must know the mean of response variable and the predictor variable. From the regression report we have
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