Publications by Lumumba W.Victor
Hypothesis Testing: Physiochemical Parameters During Dry and Wet Seasons
PHYSIOCHEMICAL PARAMETERS Physicochemical parameters are key indicators of water quality and environmental health. They include measurements such as temperature, pH, conductivity, turbidity, total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), and the presence of specific meta...
4322 sym 7 img 3 tbl
Survey Data Analysis: Analyzing Covid-19 Data
ANALYZING SURVEY DATA Reading the data To take a look at survey data, we will work with a dataset adaptedfrom Pew Research Center’s American Trends Panel, Wave 67, 05/2020 Available online at www.pewresearch.org/american-trends-panel-datasets. You can download the data and its description from: http://kateto.net/css/pew_data.zip First we wil...
16069 sym 6 img 17 tbl
A comparative Analysis of Ordinary Least Square Model with Machine Learning Models; k-NN, SVM
Use the data called heart attached to this document and run a multiple regression to determine if smoking and biking significantly causes heart disease. Your report should include the following: Load the data library(stargazer) library(ggplot2) library(tidyverse) library(sjPlot) library(corrplot) data <- read.csv("regression.csv") head(dat...
20186 sym R (28646 sym/90 pcs) 14 img 3 tbl
Comparison of Ordinary Linear Model with Machine Leaning Model \; k-NN and SVM in Modeling MPG
Use the data called heart attached to this document and run a multiple regression to determine if smoking and biking significantly causes heart disease. Your report should include the following: Load the data library(stargazer) library(ggplot2) library(tidyverse) library(sjPlot) library(corrplot) data <- read.csv("regression.csv") head(dat...
16357 sym R (30204 sym/93 pcs) 14 img 1 tbl
Chi-square test of independence and linear regression model
Set up Rstudio Setting up RMarkdown when opening it enables you to create dynamic, reproducible, and visually appealing reports, presentations, and documents, that can help you communicate your data analysis and research findings more effectively. Load the following libraries library(tidyverse) library(tidyr) library(magrittr) library(kableE...
8696 sym R (8947 sym/46 pcs) 1 img 7 tbl
CHUKA UNIVERSITY, MATH 851 SAMPLE SURVEY, PRACTICAL QUESTIONS
At an experimental station, there are 100 fields sown with wheat. Each field was divided into 16 plots of equal size (1/16th hectare). Out of 100 fields, 10 were selected by SRSWOR. From each selected field, 4 plots were chosen by SRSWOR. The yields in kg/plot are given below. knitr::include_graphics("optimal.png") I. Estimate the wheat yield p...
3746 sym 1 img
Basic Simple Linear Regression Modes
Load the following libraries library(stargazer) Please cite as: Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.3. https://CRAN.R-project.org/package=stargazer library(tinytex) library(dplyr) Attaching package: 'dplyr' The following objects are masked from 'package:stats': ...
331 sym R (5324 sym/45 pcs) 3 tbl
LOGISTIC REGRESSION
Load the Data logistic <- read.csv("logistic.csv") attach(logistic) head(logistic, 5) Step 1: Installing the packages The following packagaes are required sjmisc and sjPlot. To create the multinomial models the nnet package needs to be installed. library(sjmisc) library(sjPlot) library(nnet) library(wakefield) library(dplyr) library(nnet) ...
5581 sym R (135578 sym/87 pcs)
Simple Moving Average for Order 3, 5 and 10
Load the Following Libraries library(tidyverse) library(tsibble) library(feasts) library(lubridate) library(fpp3) library(kableExtra) library(ggplot2) library(ggpubr) library(TTR) library(TTR) library(xts) library(lmtest) library(base) library(forecast) library(ggdist) library(ggthemes) Rstudio Assignment on Public Expenditure ...
2725 sym R (3183 sym/19 pcs) 8 img
COMPARISON OF VARIOUS CLASSIFICATION MODELS: APPLICATION OF MACHINE LEARNING ALGORITHMS
CLASSIFICATION Classification is a fundamental concept in machine learning that involves categorizing data into predefined classes or labels based on the features or attributes of the data. Classification models are widely used in various fields, including finance, healthcare, natural language processing, image recognition, and more. Here’s a...
45390 sym R (96969 sym/324 pcs) 43 img 3 tbl