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AliceXiang99

Alice

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Project 2: Regularized Regression and SVM Techniques in Regression and Classification of Census Income Data
A Comprehensive Overview of Classification Algorithms in the Prediction of Vehicle Accidents
Model Creation for the Prediction of Student Performance and Improvement using Linear and Logistic Regression Methods
STA552: A Review of Missing Data Imputation and Feature Engineering Techniques in Preparation of Analytic Dataset of 1989 Study on School and Individual Factors on Dutch 7th Grade Students Test Scores
Examination of Feature Variables in 1989 Study on School and Individual Factors on Dutch 7th Grade Students Test Scores
STA551 Final Presentation: A Cumulative Assessment on the Effects of Different Factors on the Final Exam Grades of Students
An Overview of Different Unsupervised Learning Algorithms with Student Lifestyle Dataset
An Overview of Different Supervised Learning Algorithms in the Prediction of Heart Disease
STA551 project 3
STA551: A Cumulative Assessment on the Effects of Different Factors on the Final Exam Grades of Students
Project 1: Data Integration and EDA
STA551 Week One: Online CV
STA490 Week 11 Assignment: Survey Validation for Student Satisfaction Survey Multi Item Questions
STA 490 Assignment 7: Sampling Methods for Bank Loan Data
Week 6 Assignment: EDA amd Applications of Combined Bank Loan Data
Final Presentation: MLR on the CO2 Emissions of Vehicles
Week 3 Assignment: Effective Presentation
STA490 - Week 2 Assignment: RMarkdown Presentation
STA 321 Final Project: Time Series Models with Champagne Sales from 1964 to 1972
STA 321 Assignment 13: Exponential Smoothing Methods with Champagne Sales from 1964 to 1972
DocumentSTA 321 Assignment 12: Deconstructed Time Series of Natural Gas Prices from 2004 to 2020
STA 321 Assignment 11: Time Series on New Houses Sold between 1963 and 2023
STA 321 Assignment 10: Dispersed Poisson Regression on NYC Cycling Data for the Brooklyn Bridge
STA 321 Assignment 9: Poisson Regression on NYC Cycling Data for the Brooklyn Bridge
The primary random response variable is the number of cyclists on the Brooklyn Bridge. The predictor variables will be the day of the week, the high and low temperatures, the precipitation, and the total number of cyclists that day.
STA 321 Assignment 8: Stroke Prediction with Logistic Regression using Cross ValidationPublish Document
The objective of this assignment is to build a logistic regression model to predict stroke in a patient according to various risk factors.
STA 321 Assignment 7: Multiple Logistic Regression on Stroke Risk Factors
The objective for this assignment is to identify different risk factors for a stroke.
STA 321 Assignment 6: Simple Logistic Regression on Stroke Probability and Average Blood Glucose Levels
STA 321 Mini-Project: MLR and Bootstrapping Models for CO2 Emissions of Vehicles
The primary question is how different predictor variables relate to the CO2 emissions of the vehicle.
STA 321 Assignment 4: MLR on CO2 Emissions of Different Types of Cars
STAT321 - Week 3 Assignment
STA504 HW 1
Non-Parametric Bootstrap Confidence Intervals for Variable: Eggs
We will be doing a brief analysis on the dataset protein. This dataset includes the percentage of protein intake from different categories of food of countries all over the world, as well as obesity and COVID-19 data for these countries. In this week’s assignment, we will analyze one specific variable in this dataset by using two differing methods to construct a confidence interval for the mean of the sample for the chosen variable, and then we will compare the two confidence intervals. For my assignment, I chose the variable eggs.