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Managing Library Software Development: A Case Study In Developing An Inventory Management System For Off-Site Storage Using An Outside Contractor Aaron B. Bales, Robert Fox, Miranda R. VanNevel Somebody wanted to shift library for their requirements like having more number of books. They found the n ...
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Oakland University Confidence Intervals Task

CONFIDENCE INTERVALSOne of the most popular ways to estimate an unknown parameter is the confidence interval. Instead of ...

Oakland University Confidence Intervals Task

CONFIDENCE INTERVALSOne of the most popular ways to estimate an unknown parameter is the confidence interval. Instead of just using a point estimate, it gives an interval estimate. This gives more information about the unknown parameter. In this assignment, we will examine confidence interval. Download the dataset JNJ.csv from the course ,datawebsite.Using R, compute the sample mean of closing price of Johnson and Johnson's (JNJ) daily stock prices.Using R, compute the standard deviation. Also, compute the 90%, 95%, and 99% confidence intervals for the true mean closing JNJ stock price. Based on 3, discuss the pattern you see from these three confidence intervals and explain why such a pattern exists.What is the advantage(s) and disadvantage(s) of increasing the confidence levels? Explain.Interpret the confidence intervals you've calculated from part 3.Using a Word document, show the relevant calculations as well as the answers/solution. Copy the screenshots of the R output and paste them into the Word document. Your screenshot must include the current date using the function Sys.Date(

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Drills with R on K-NN models & Dissimilarities between data objects

1. Drills with R on K-NN modelsThis problem is related to Nearest neighbors classifiers described in section 9.5 in “Mod ...

Drills with R on K-NN models & Dissimilarities between data objects

1. Drills with R on K-NN modelsThis problem is related to Nearest neighbors classifiers described in section 9.5 in “Modern Statistics with R” - https://modernstatisticswithr.com: Fit a kNN classification model to the wine data, using pH, alcohol, fixed.acidity, and residual.sugar as explanatory variables. Evaluate its performance using 10-fold cross-validation, using AUC to choose the best k. To solve the problem, you'll need to load the data and libraries with: # Import data about white and red wines: white <- read.csv("https://tinyurl.com/winedata1",sep = ";") red <- read.csv("https://tinyurl.com/winedata2",sep = ";") # Add a type variable: white$type <- "white" red$type <- "red" # Merge the datasets: wine <- rbind(white, red) wine$type <- factor(wine$type) install.packages('caret', dependencies = TRUE) library(caret) # to visualize results you need the following install.packages('MLeval', dependencies = TRUE) library(MLeval) For the submission:1. Provide the commands in plain text that you used to solve the problem.Attach the figure that resulted after command: plots$rocOutput after executed command: plots$optres[[1]][13,]Attach the figure that resulted after command: plots$cc 2. Dissimilarities between data objectsThis project demonstrates how to measure similarities between data objects. These topics described are mostly in chapter 6 Statistical Machine Learning from ‘Practical Statistics for Data Scientists’. Cover in the project the following: Find some data examples and show examples of calculatingEuclidean distanceL1 distance Prove or disprove that Euclidean and L1 distance satisfyPositivity d(x,y) >= 0 for all x and y, d(x,y) == 0 only if x == y. Symmetry d(x,y) == d(y,x) for all x and y. Triangle Inequality d(x,z) <= d(x,y) + d(y,z) for all points x, y, and z Explain why it is not possible or why it is possible torearrange data so Euclidean distance gives the same meaning as Hamming distanceshow that measure d=1-cos(x,y) satisfies positivity, symmetry, and triangle Inequality Draw conclusions about what is important when choosing the distance measure for the evaluation of dissimilarities between data objects. Assignment 1 and 2 are to be done in 2 different papers in APA format

Arizona State University Python File Script Coding Sample Run

InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labAS ...

Arizona State University Python File Script Coding Sample Run

InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labASURITE is your user nameExample: part1-cjingers.py, part2-cjingers.pySubmit all .py files as part of your submissionDon't forget the interpreter line!The output of your script must match the same format shown in the sample run for each part.Part 1 Write a Python script that will prompt the user for monetary value (such as 11.26). Then, calculate the number of dollars, quarters, dimes, nickels, and pennies. Your solution should use the maximum possible number of dollars, then quarters and so on. For example; converting 11.26 to 1126 pennies is not acceptable.Sample runEnter an ammount of money: 11.56Your amount of $11.56 consists of; 11 dollars 2 quarters 0 dimes 1 nickels 1 penniesPart 2Given two points, the formula for computing distance is;(x2−x1)2+(y2−y1)2−−−−−−−−−−−−−−−−−−√(x2−x1)2+(y2−y1)2Write a Python script that prompts the user for two points and computes the distance between them.A point is comprised of two floating point numbers; representing the x and y coordinates.Sample runEnter x1 and y1 for Point 1: 1.5, -3.4Enter x2 and y2 for Point 2: 4, 5The distance between (1.5, -3.4) and (4, 5) is 8.8

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7 pages

Data Simulation And Analysis

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Data Simulation And Analysis

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Oakland University Confidence Intervals Task

CONFIDENCE INTERVALSOne of the most popular ways to estimate an unknown parameter is the confidence interval. Instead of just using a point estimate, it gives an interval estimate. This gives more information about the unknown parameter. In this assignment, we will examine confidence interval. Download the dataset JNJ.csv from the course ,datawebsite.Using R, compute the sample mean of closing price of Johnson and Johnson's (JNJ) daily stock prices.Using R, compute the standard deviation. Also, compute the 90%, 95%, and 99% confidence intervals for the true mean closing JNJ stock price. Based on 3, discuss the pattern you see from these three confidence intervals and explain why such a pattern exists.What is the advantage(s) and disadvantage(s) of increasing the confidence levels? Explain.Interpret the confidence intervals you've calculated from part 3.Using a Word document, show the relevant calculations as well as the answers/solution. Copy the screenshots of the R output and paste them into the Word document. Your screenshot must include the current date using the function Sys.Date(

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Handsonproject11 2

Enron was one of the world's leading electricity, natural gas, and communications companies to have existed in the US. The ...

Handsonproject11 2

Enron was one of the world's leading electricity, natural gas, and communications companies to have existed in the US. The company saw many profits ...

Drills with R on K-NN models & Dissimilarities between data objects

1. Drills with R on K-NN modelsThis problem is related to Nearest neighbors classifiers described in section 9.5 in “Mod ...

Drills with R on K-NN models & Dissimilarities between data objects

1. Drills with R on K-NN modelsThis problem is related to Nearest neighbors classifiers described in section 9.5 in “Modern Statistics with R” - https://modernstatisticswithr.com: Fit a kNN classification model to the wine data, using pH, alcohol, fixed.acidity, and residual.sugar as explanatory variables. Evaluate its performance using 10-fold cross-validation, using AUC to choose the best k. To solve the problem, you'll need to load the data and libraries with: # Import data about white and red wines: white <- read.csv("https://tinyurl.com/winedata1",sep = ";") red <- read.csv("https://tinyurl.com/winedata2",sep = ";") # Add a type variable: white$type <- "white" red$type <- "red" # Merge the datasets: wine <- rbind(white, red) wine$type <- factor(wine$type) install.packages('caret', dependencies = TRUE) library(caret) # to visualize results you need the following install.packages('MLeval', dependencies = TRUE) library(MLeval) For the submission:1. Provide the commands in plain text that you used to solve the problem.Attach the figure that resulted after command: plots$rocOutput after executed command: plots$optres[[1]][13,]Attach the figure that resulted after command: plots$cc 2. Dissimilarities between data objectsThis project demonstrates how to measure similarities between data objects. These topics described are mostly in chapter 6 Statistical Machine Learning from ‘Practical Statistics for Data Scientists’. Cover in the project the following: Find some data examples and show examples of calculatingEuclidean distanceL1 distance Prove or disprove that Euclidean and L1 distance satisfyPositivity d(x,y) >= 0 for all x and y, d(x,y) == 0 only if x == y. Symmetry d(x,y) == d(y,x) for all x and y. Triangle Inequality d(x,z) <= d(x,y) + d(y,z) for all points x, y, and z Explain why it is not possible or why it is possible torearrange data so Euclidean distance gives the same meaning as Hamming distanceshow that measure d=1-cos(x,y) satisfies positivity, symmetry, and triangle Inequality Draw conclusions about what is important when choosing the distance measure for the evaluation of dissimilarities between data objects. Assignment 1 and 2 are to be done in 2 different papers in APA format

Arizona State University Python File Script Coding Sample Run

InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labAS ...

Arizona State University Python File Script Coding Sample Run

InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labASURITE is your user nameExample: part1-cjingers.py, part2-cjingers.pySubmit all .py files as part of your submissionDon't forget the interpreter line!The output of your script must match the same format shown in the sample run for each part.Part 1 Write a Python script that will prompt the user for monetary value (such as 11.26). Then, calculate the number of dollars, quarters, dimes, nickels, and pennies. Your solution should use the maximum possible number of dollars, then quarters and so on. For example; converting 11.26 to 1126 pennies is not acceptable.Sample runEnter an ammount of money: 11.56Your amount of $11.56 consists of; 11 dollars 2 quarters 0 dimes 1 nickels 1 penniesPart 2Given two points, the formula for computing distance is;(x2−x1)2+(y2−y1)2−−−−−−−−−−−−−−−−−−√(x2−x1)2+(y2−y1)2Write a Python script that prompts the user for two points and computes the distance between them.A point is comprised of two floating point numbers; representing the x and y coordinates.Sample runEnter x1 and y1 for Point 1: 1.5, -3.4Enter x2 and y2 for Point 2: 4, 5The distance between (1.5, -3.4) and (4, 5) is 8.8