DOE, Errors, Count Data Regression - Data Analytics Assignment Help

Assignment Task

Question 1: Design an experiment with X factors and Y level, where X is last digit of your entry number and Y is second last digit of your entry number.

Question 2: What are D and I optimal designs? Perform these on above dataset.

Question 3: What regression methods are useful when outcome of the experiment is discrete data? Given dataset is a data frame with 214 observation containing examples of the chemical analysis of 7 different types of glass. The problem is to forecast the type of class on basis of the chemical analysis. The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence (if it is correctly identified!). Fit a logistic regression to find the type of Glass. (Hint: don’t forget to change the data type (factor or number or other) after loading the data into R.)

Chemicals and abbreviations used in the datasheet.
RI refractive index
Na Sodium
Mg Magnesium
Al Aluminum
Si Silicon
K Potassium
Ca Calcium
Ba Barium
Fe Iron
Type Type of glass (class attribute)

Question 4: Speed data is given in the corresponding sheet. Choose column number corresponding to your entry number in the sheet “Question 4.xlsx” and find the standard error about the mean.

Question 5: Researchers are interested in finding the relationship between crashes on rural twolane highways and the width of the right lane and the width of the right shoulder. a subset of Texas on-system crashes for the years 1999–2001 was obtained for the analysis (fitzpatrick et al., 2005).

There are 2,729 two-lane roadway segments in the database, and crashes are examined in terms of the variable “total crashes for three years.” Total crashes are the total number of all crashes (includes all types of crashes) during the three-year observation period on each roadway segment.

In this example, the dependent variable, Y, is “total crashes for three years,” and the independent variables of interest are right lane (width in feet) and right shoulder (width in feet). Fit the Poisson regression model to find the number of crashes. (Hint: don’t forget to change the data type (factor or number or other) after loading the data into R.)

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