R Programming Assignment Help

R Task on ANOVA Model

rm(list = ls())
options(warn = -1)
library(readxl)

## Reading the data from excel
Project_2_Data <- read_excel(“Stat 481 Project 2 Data.xls”)
str(Project_2_Data)

## Cleaning and attributing the dtaa
Project_2_Data$courses = as.factor(Project_2_Data$courses)
Project_2_Data$gender = as.factor(Project_2_Data$gender)
levels(Project_2_Data$gender) <- c(“Female”, “Male”)
levels(Project_2_Data$courses) <- c(“Algebra”, “Algebra&Geometry”, “Calculus”)

attach(Project_2_Data)

## Descriptives
library(ggplot2)
library(hrbrthemes)
library(dplyr)
library(tidyr)
library(viridis)
temp = aggregate(score~courses+gender, Project_2_Data, FUN = mean)

qqnorm(score)
ggplot(Project_2_Data, aes(x = score)) + geom_histogram()

summary(Project_2_Data)
p1 <- ggplot(data=Project_2_Data, aes(x=score, fill=courses)) + geom_density(adjust=1.5, alpha=.4) + theme_ipsum()

p2 <- ggplot(data=Project_2_Data, aes(x=score, fill=gender)) + geom_density(adjust=1.5, alpha=.4) + theme_ipsum()
## Model
## Test of normality and other assumptions
ks.test(score, pnorm, mean = mean(score), sd= sd(score))
bartlett.test(score~courses, data = Project_2_Data)
bartlett.test(score~gender, data = Project_2_Data)

## Linear model
model1 = anova(score ~ courses + gender, data = Project_2_Data)
model1
summary(model1)
## Post Hoc
library(DescTools)
PostHocTest(model1, method = “bonferroni”)
PostHocTest(model1, method = “hsd”)

Report Writing Help

Report on Dissecting Pharmacy Accusations

Summary of the Accusation against All Meds Solution Inc., James Darren Dizon Simbulan, Owner and James Darren Dizon Simbulan

Relevant Statutes and Regulations

The accusation falls under the jurisdiction of the California Business and Professions Code (BPC). It is noteworthy that section 118, subdivision (b) states that the suspension, expiration or forfeiture by operation of law of a license shall not hinder the Board of authority or Continue reading

Python Assignment Help

Machine Learning using Python for Gradescope Task

Solution for Machine Learning using Python for Gradescope Task

#!/usr/bin/env python
 # coding: utf-8

# Essential Problem 1:
 # a). Here at least one point is required in each grid, thus the least number of data points are 100
 # b). Here the dimension has changed to 3. Thus the least number of data points are 10^3 = 1000.
 # c). Here the dimension has changed to 3. Thus the least number of data points are 10^(10)

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