#data

Universal_bank = read.csv(“UniversalBank.csv”, header = T)

dim(Universal_bank)

head(Universal_bank) Continue reading

#data

Universal_bank = read.csv(“UniversalBank.csv”, header = T)

dim(Universal_bank)

head(Universal_bank) Continue reading

#include <stdlib.h>

#include <stdio.h>

#include <string.h>

#include <ctype.h>

/* Stores parameters that specify how to the program should behave. * Continue reading

import numpy as np

print(”)

print(“Enter two numbers, low then high.”)

l = int(input(“low = “))

h = int(input(“high = “))

summary_statistics_A <- function(matrix){

vec = sort(as.vector(matrix))

len = length(vec)

if(isSymmetric(matrix) && is.numeric(matrix)){

min = vec[1]

Continue reading

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”)

creditDF <- read.csv(“Downloads/Credit.csv”)

str(creditDF)

# Q1)

# Exploratory Data Analysis Continue reading

Post a total of 3 substantive responses over 2 separate days for full participation. This includes your

initial post and 2 replies to other students.

Respond to the following in a minimum of 175 words: Continue reading

**Solution for Statistics – Supply and Demand Task**

(a) β̂1 = −0.75317

Confidence interval is: ( −0.8050502, −0.7012837 )

(b) For a variable to be valid instrument for log_p , it should be correlated with log_p but

uncorrelated with error term (UI

)

**Selected supply and Logistics company: Muscat International shipping and Logistics**

Muscat international shipping and Logistics has a great track record for the logistics services in the company. The company has over fifteen years of experience in logistics services. The system which is applied in Muscat international is related to the smooth and pre-post shipment of the freight (Ho, Zheng, Yildiz, & Talluri, 2015).The system which Muscat international shipping and logistics apply for controlling the mode of transportation uses the Cargo wise system for most of the entries. This systemis adopted conventionally from three to four years and it has benefit to provide a solution to the company. Continue reading

Use of Statistics has been on the rise. Today, every company is using statistical and analytical tools to analyze data in a matter of few minutes. Earlier, people used to gather data through various sources and to analyze this data it used to take weeks and months together. Continue reading

Artificial intelligence is taking the world by storm. This has made has a strong mark in the technology and manufacturing industries. This is also helping organizations to analyze the data. Today, technology has gone to the extent where a computer is able to talk to humans, recognize their faces and even is turning off the lights and refrigerators in our homes. Continue reading

A student of statistical modeling needs to know and understand **Regression analysis. **This is used to find out the relationships which are there between variables. There are different kinds of techniques which can be used for analyzing and modeling several variables and this is also when the relationship needs to be focused on the independent variables and the dependent variables. Continue reading

Plenty of statistical analysis software like Minitab, SAS, and SPSS are available for statistical analysis. You should check the below-mentioned points to judge the accessibility of statistical software- Continue reading

**What is Regression Analysis?**

Regression Analysis is a statistical approach to predict the change in a dependent variable like revenue, sales etc. due to change in one or more than one variables like income, population etc. Regression assists financial and investment managers to evaluate the assets and know the relation between variables like commodity prices and business stocks dealing in such commodities. Continue reading