Publications by Muhammad Farhaad
Unraveling the Patterns: A Logistic Regression Analysis
Introduction In this analysis, we delve into the intriguing world of logistic regression to unravel the associations between various explanatory variables and a binary response variable. The dataset at hand required careful preparation, with the response variable being categorical with more than two categories. We collapsed it down to two categ...
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A Multiple Regression Analysis
Introduction In this blog entry, we delve into the results of a multiple regression analysis examining the associations between various explanatory variables and the number of nicotine dependence symptoms. Our primary focus is on understanding the relationship between major depression and nicotine dependence symptoms, while considering potentia...
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K-Means Cluster Analysis in R
Introduction This week’s assignment revolves around conducting a k-means cluster analysis, an unsupervised machine learning method. The primary objective is to partition observations in a data set into distinct clusters based on the similarity of responses on multiple variables. The clustering variables should predominantly be quantitative, a...
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Lasso Regression Analysis with K-Fold Cross Validation
Lasso Regression Analysis with K-Fold Cross Validation Linear regression models are widely used for predicting a response variable based on one or more predictor variables. However, when dealing with a large number of predictors, the model can become complex and prone to overfitting. Lasso regression, a shrinkage and variable selection method, ...
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Classification Tree Analysis for Car Showroom Data
Introduction In this blog, we will explore a classification tree analysis using a car showroom dataset. The goal is to predict a binary categorical response variable based on various explanatory variables. Let’s start by loading the necessary libraries and generating some synthetic data for demonstration purposes. Data Generation Let’s cre...
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Random Forest Analysis with R
Introduction In this blog post, we will explore the application of Random Forest analysis using R. We’ll generate a random dataset and use the randomForest package to build a predictive model and evaluate the importance of explanatory variables in predicting a binary, categorical response variable. Generate Random Dataset Let’s start by cr...
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Sample Description - Understanding Sleep Patterns in College Students
Study Population The study focused on college students enrolled in a large urban university in the United States. The target population included undergraduate and graduate students from various academic disciplines and backgrounds. Level of Analysis The level of analysis for this study was individual. The research aimed to explore sleep patter...
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Data Collection Procedures - Sleep Patterns Study
Data Collection Procedures: a) Study Design: The data for this study were generated through a combination of surveys and wearable sleep monitoring devices. This approach involved both self-reporting by participants and objective measurement of sleep patterns through the devices. b) Original Purpose: The original purpose of the data collection w...
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Variable Description - Sleep Patterns Study
Variables Description: a) Explanatory and Response Variables: The study examined the following variables: Explanatory Variables: Sleep Duration: The amount of time participants spent sleeping each night. Lifestyle Factors: Including exercise frequency, screen time before bedtime, and caffeine intake. Demographic Information: Such as age, gende...
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Linear Regression Analysis with Visualizations
Introduction In this blog post, we’ll explore the association between our explanatory variables and a response variable using a linear regression model. We’ll also visualize the data to gain additional insights. Before diving into the results, we’ll walk through the data preparation steps for both categorical and quantitative explanatory ...
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