STATISTICS COURSE HELP

Energy Usage A medical device manufacturer has been collecting data on a variety of weather-related variables (e.g Avg Monthly Temp) as well as some operational data (e.g Monthly units produced) for one of their manufacturing facilities. Using an appropriate statistical modeling technique, create a model that is able to forecast monthly energy use going forward. Which metrics are most explanatory at describing energy use in a given month? Which ones are the least important but still helpful in forecasting? And which ones do not appear to be useful at all? provide the model equation in your presentation along with a detailed summary interpretation of the output. Data for the problem Month Year Avg Temp Min Temp Max Temp Pct Sun Units Produced Total Equip Run Time Energy Jan-14 50 39 62 63 1202 2452 37176 Feb-14 55 43 65 64 1326 3181 39892 Mar-14 61 48 71 76 112...

 A manufacturer of mobile phones is interested in studying the durability of the device when dropped. A drop test study was performed dropping phones at various heights (from 1 to 8 feet in height) and observing the number tested that experience some type of fracture of the screen. Two material types were part of the study (A and B with B being the more expensive and hopefully more durable material - 50% more expensive to manufacture). Using the data in the .xls file FRACTURE TEST, build a logistic regression model (for each material type) that predicts the proportion of the screens expected to fracture as a function of the height of the drop. Use the results to describe the durability of the screens comparing the two materials. Examples of some questions to address are: Is Material B substantially more durable than A? Do you think it justifies the additional Manufacturing cost? What height do your models predict 25%, 50%, 75%, and 100% of the screens will fracture for each materia...

A sports team is interested in better understanding the impact that the maximum volume of oxygen an athlete can intake has on their performance. To study this, twenty athletes on a team had measurements taken on the amount they are able to intake and the amount of time (in minutes) it takes for them to complete a 2-mile run. Using the data in the .xls file ATHLETIC PERFORMANCE, build a simple linear regression model using Oxygen Uptake as the predictor Variable and Time as the response variable. Use the results to describe the relationship between these two variables. Examples of some questions to address are: Describe the Expected Time as a function of Oxygen Uptake, How much of the variation in Time between the 20 athletes is your model with Oxygen Uptake able to account for/explain?, What is your prediction of an athlete's completion time of a 2-mile run if they had an Oxygen Uptake of 40, 50, and 60? Which of these predictions do you feel is most/least precise? Do any of t...