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”)

SQL Assignment Help

A SQL Task

—————CREATE CUSTOMER
CREATE TABLE CUSTOMER(CUSTOMERID INT NOT NULL PRIMARY KEY,
CUSTOMERFIRST VARCHAR(50) NOT NULL,
CUSTOMERLAST VARCHAR(50) NOT NULL ,
CUSTOMERSTREET VARCHAR(50) NOT NULL , 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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