%Fit using polyfit in matlab for best fit checking plot

table = xlsread(‘Project04′,’Stepped-Shaft Torsion Data, A-E’)

[p S] = polyfit(table(:,1),table(:,2),5) Continue reading

%Fit using polyfit in matlab for best fit checking plot

table = xlsread(‘Project04′,’Stepped-Shaft Torsion Data, A-E’)

[p S] = polyfit(table(:,1),table(:,2),5) Continue reading

function A09Prob1_sphereVol_kang401(radius)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% ENGR 132

% Program Description

% The code computes volume of a sphere as a function of height of fluid

% The input to function call is radius

% The function does not return any variable Continue reading

A=imread(‘one.png’)

signImage=rgb2gray(A);

figure(1)

imshow(signImage)

%Detect features of first image that is read above%

signPoints=detectSURFFeatures(signImage)

figure(3)

imshow(signImage)

title(‘100 strongest features from sign image’)

hold on

%Top 100 strongest features

plot(selectStrongest(signPoints,100))

[signFeatures2, signPoints2] = extractFeatures(signImage, signPoints)

B=imread(‘two.png’)

signImage2=rgb2gray(B);

figure(4)

imshow(signImage2)

%Detect features of first image that is read above%

signPoints2=detectSURFFeatures(signImage2)

figure(5)

imshow(signImage2)

title(‘100 strongest features from sign image’)

hold on

%Top 100 strongest features

plot(selectStrongest(signPoints2,300))

[signFeatures2, signPoints2] = extractFeatures(signImage2, signPoints2)

%Punitive point matches in both the images%

picPairs=matchFeatures(signFeatures, signFeatures2);

matchedSignPoints = signPoints(picPairs(:,1),:);

matchedFindPoints = signPoints2(picPairs(:,2),:);

figure(6)

showMatchedFeatures(signImage,signImage2,matchedSignPoints, matchedFindPoints, ‘montage’)

title(‘Matched points both images’)

%Locate objects using Punitive matches%

%[tform, inlierBoxPoints, inlierScenePoints] =estimateGeometricTransform(matchedSignPoints, matchedFindPoints);

%figure;

%showMatchedFeatures(signImage, signImage2, inlierBoxPoints,inlierScenePoints, ‘montage’);

%title(‘Matched Points (Inliers Only)’);

library(readxl)

CSDATA <- read_excel(“CSDATA.xlsx”)

dim(CSDATA)

#Fitting the linear regression on all the independent variables Continue reading

“`{r setup, include = FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(cvTools)

#library(glmnet)

library(sandwich) 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