Part A 1. Create and print a SAS dataset or R dataframe named Flour.
2. Use SAS or R to find the simple linear regression model for predicting NBags from Weight. nbags= -5.86436+0.02180*weight
3. Use proc means to compute the means and standard deviations
PART A 1. Read in the data from "paper.txt", print it with R. Everything was input correctly.
2. Added cat statement using R
3. White paper Sample mean : 0.09565517 Sample standard deviation : 0.02963863 Sample median : 0.098 Sample IQR: 0.006 Values for
PART A 1. Use scan function to create an R dataset.
2. Use cat and other function to add explanations.
3. Create a normal plot for nusers.
4. Compute a 95% confidence interval with R. Xbar: 17.954 Se: 0.447 Df: 49 Confidence interval: (17.057, 18.851)
5.
1. The difference between F test and t test are:
F-values are all non-negative.
The distribution is non-symmetric.
The mean is approximately 1.
There are two independent degrees of freedom, one for the numerator, and one for the denominator.
There are man
Part A 1. Create a SAS or R dataset and print it.
2. Create a regression model for predicting hours from type and rpm. Use a dummy variable for type. The regression model is: Hours = 76.65886 0.06421 * rpm 22.00468 * dummy_var
3. Create a scatterplot of
Technical Report for Final Project
Group Member: Keng Zhang, Shuxiang Xu
Introduction
This project is designed for building a model for predicting the price of
2005 GM Chevrolet used cars.
The variables in original dataset are price, mileage, make, model,