Description
I need help solving problem 9 and graphing. And problems 10,11,12,13, and 14. Thanks!
Unformatted Attachment Preview
Graph the system of inequalities (shade each half-plane solution, not only the
overall solution).
9. ys
2r + y < 10
y>-1
For each point listed below, circle every inequality for which it is a solution.
10. (0,0)
vs +3
2x + y< 10
y >-1
11. (-4,-2)
ys*x+3
+
3
2x + y < 10
y>-1
12. (0-4)
ys*x+3
2x + y < 10
y>-1
13. (0,3)
ys-x+3
2x + y< 10
y>-1
14. (5,0)
ys -x +3
2x + y < 10
y>-1
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Create Association Rules on the dataset and Apply decision tree induction algorithm using R
Part I: For this part, you need to explore the bank data (bankdata_csv_all.csv), available in attachments, and an accom ...
Create Association Rules on the dataset and Apply decision tree induction algorithm using R
Part I: For this part, you need to explore the bank data (bankdata_csv_all.csv), available in attachments, and an accompanying description (bankdataDescription.doc) of the attributes and their values.The dataset contains attributes on each person’s demographics and banking information in order to determine they will want to obtain the new PEP (Personal Equity Plan). Your goal is to perform Association Rule discovery on the dataset using R. First perform the necessary preprocessing steps required for association rule mining, specifically the id field needs to be removed and a number of numeric fields need discretization or otherwise converted to nominal. Next, set PEP as the right hand side of the rules, and see what rules are generated. Select the top 5 most “interesting” rules and for each specify the following: Support, Confidence and Lift values An explanation of the pattern and why you believe it is interesting based on the business objectives of the company. Any recommendations based on the discovered rule that might help the company to better understand behavior of its customers or to develop a business opportunity. Note that the top 5 most interesting rules are most likely not the top 5 in the strong rules.They are rules, that in addition to having high lift and confidence, also provide some non-trivial, actionable knowledge based on underlying business objectives. To complete this assignment, write a short report describing your association rule mining process and the resulting 5 interesting rules, each with their three items of explanation and recommendations.For at least one of the rules, discuss the support, confidence and lift values and how they are interpreted in this data set. You should write your answers as if you are working for a client who knows little about data mining. Your report should give your client some insightful and reliable suggestions on what kinds of potential buyers your client should contact, and convince your client that your suggestions are reliable based on the evidence gathered from your experiment results. In more detail, your answers should include: Description of preprocessing steps Description of parameters and experiments in order to obtain strong rules Give the top 5 most interesting rules and the 3 items listed above for each rule. Part II: In this part of homework, you are expected to Apply decision tree induction tree algorithm to solve a mystery in history: who wrote the disputed essays, Hamilton or Madison? 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week 8 math302
nario Background:
A marketing company based out of New York City is doing well and is looking to expand internationally. The CEO and VP of Operations decide to enlist the help of a consulting firm that you work for, to help collect data and analyze market trends.
You work for Mercer Human Resources. The Mercer Human Resource Consulting website lists prices of certain items in selected cities around the world. They also report an overall cost-of-living index for each city compared to the costs of hundreds of items in New York City (NYC). For example, London at 88.33 is 11.67% less expensive than NYC.
More specifically, if you choose to explore the website further you will find a lot of fun and interesting data. You can explore the website more on your own after the course concludes.
https://mobilityexchange.mercer.com/Insights/ cost-of-living-rankings#rankings
Assignment Guidance:
In the Excel document, you will find the 2018 data for 17 cities in the data set Cost of Living. Included are the 2018 cost of living index, cost of a 3-bedroom apartment (per month), price of monthly transportation pass, price of a mid-range bottle of wine, price of a loaf of bread (1 lb.), the price of a gallon of milk and price for a 12 oz. cup of black coffee. All prices are in U.S. dollars.
You use this information to run a Multiple Linear Regression to predict Cost of living, along with calculating various descriptive statistics. This is given in the Excel output (that is, the MLR has already been calculated. Your task is to interpret the data).
Based on this information, in which city should you open a second office in? You must justify your answer. If you want to recommend 2 or 3 different cities and rank them based on the data and your findings, this is fine as well.
Deliverable Requirements:
This should be ¾ to 1 page, no more than 1 single-spaced page in length, using 12-point Times New Roman font. You do not need to do any calculations, but you do need to pick a city to open a second location at and justify your answer based upon the provided results of the Multiple Linear Regression.
The format of this assignment will be an Executive Summary. Think of this assignment as the first page of a much longer report, known as an Executive Summary, that essentially summarizes your findings briefly and at a high level. This needs to be written up neatly and professionally. This would be something you would present at a board meeting in a corporate environment. If you are unsure of an Executive Summary, this resource can help with an overview. What is an Executive Summary?
Things to Consider:
To help you make this decision here are some things to consider:
Based on the MLR output, what variable(s) is/are significant?
From the significant predictors, review the mean, median, min, max, Q1 and Q3 values?
It might be a good idea to compare these values to what the New York value is for that variable. Remember New York is the baseline as that is where headquarters are located.
Based on the descriptive statistics, for the significant predictors, what city has the best potential?
What city or cities fall are below the median?
What city or cities are in the upper 3rd quartile?
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Create Association Rules on the dataset and Apply decision tree induction algorithm using R
Part I: For this part, you need to explore the bank data (bankdata_csv_all.csv), available in attachments, and an accom ...
Create Association Rules on the dataset and Apply decision tree induction algorithm using R
Part I: For this part, you need to explore the bank data (bankdata_csv_all.csv), available in attachments, and an accompanying description (bankdataDescription.doc) of the attributes and their values.The dataset contains attributes on each person’s demographics and banking information in order to determine they will want to obtain the new PEP (Personal Equity Plan). Your goal is to perform Association Rule discovery on the dataset using R. First perform the necessary preprocessing steps required for association rule mining, specifically the id field needs to be removed and a number of numeric fields need discretization or otherwise converted to nominal. Next, set PEP as the right hand side of the rules, and see what rules are generated. Select the top 5 most “interesting” rules and for each specify the following: Support, Confidence and Lift values An explanation of the pattern and why you believe it is interesting based on the business objectives of the company. Any recommendations based on the discovered rule that might help the company to better understand behavior of its customers or to develop a business opportunity. Note that the top 5 most interesting rules are most likely not the top 5 in the strong rules.They are rules, that in addition to having high lift and confidence, also provide some non-trivial, actionable knowledge based on underlying business objectives. To complete this assignment, write a short report describing your association rule mining process and the resulting 5 interesting rules, each with their three items of explanation and recommendations.For at least one of the rules, discuss the support, confidence and lift values and how they are interpreted in this data set. You should write your answers as if you are working for a client who knows little about data mining. Your report should give your client some insightful and reliable suggestions on what kinds of potential buyers your client should contact, and convince your client that your suggestions are reliable based on the evidence gathered from your experiment results. In more detail, your answers should include: Description of preprocessing steps Description of parameters and experiments in order to obtain strong rules Give the top 5 most interesting rules and the 3 items listed above for each rule. Part II: In this part of homework, you are expected to Apply decision tree induction tree algorithm to solve a mystery in history: who wrote the disputed essays, Hamilton or Madison? About the Federalist Papers About the disputed authorshipComputational approach for authorship attribution Quote from the Library of Congress http://www.loc.gov/rr/program/bib/ourdocs/federalist.html The Federalist Papers were a series of eighty-five essays urging the citizens of New York to ratify the new United States Constitution. Written by Alexander Hamilton, James Madison, and John Jay, the essays originally appeared anonymously in New York newspapers in 1787 and 1788 under the pen name "Publius." A bound edition of the essays was first published in 1788, but it was not until the 1818 edition published by the printer Jacob Gideon that the authors of each essay were identified by name. The Federalist Papers are considered one of the most important sources for interpreting and understanding the original intent of the Constitution. The original essays can be downloaded from the Library of Congress. http://thomas.loc.gov/home/histdox/fedpapers.html In the author column, you will find 74 essays with identified authors: 51 essays written by Hamilton, 15 by Madison, 3 by Hamilton and Madison, 5 by Jay. The remaining 11 essays, however, is authored by “Hamilton or Madison”. These are the famous essays with disputed authorship. Hamilton wrote to claim the authorship before he was killed in a duel. Later Madison also claimed authorship. Historians were trying to find out which one was the real author. In 1960s, statistician Mosteller and Wallace analyzed the frequency distributions of common function words in the Federalist Papers, and drew their conclusions. This is a pioneering work on using mathematical approaches for authorship attribution. Nowadays, authorship attribution has become a classic problem in the data mining field, with applications in forensics (e.g. deception detection), and information organization. The Federalist Paper data set (fedPapers85.csv) is provided in LMS. The features are a set of “function words”, for example, “upon”. The feature value is the percentage of the word occurrence in an essay. For example, for the essay “Hamilton_fed_31.txt”, if the function word “upon” appeared 3 times, and the total number of words in this essay is 1000, the feature value is 3/1000=0.3% Organize your report using the following template: Section 1: Data preparation You will need to separate the original data set to training and testing data for classification experiments. Describe what examples in your training and what in your test data. Section 2: Build and tune decision tree models First build a DT model using the default setting, and then tune the parameters to see if better model can be generated. Compare these models using appropriate evaluation measures. Describe and compare the patterns learned in these models. Section 3: Prediction After building the classification model, apply it to the disputed papers to find out the authorship and report the performance accuracy of your models.
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PSY 520 Grand Canyon University Ch 13 14 & 15 Null Hypothesis Statistics Exercises
PSY520 Topic 5 exercises Complete the following exercises located at the end of each chapter and put them into a Word document to be submitted as directed by the instructor. Show all relevant work; use the equation editor in Microsoft Word when necessary. 1.Chapter 13, numbers 13.6, 13.8, 13.9, and 13.10 2.Chapter 14, numbers 14.11, 14.12, and 14.14 3.Chapter 15, numbers 15.7, 15.8, 15.10 and 15.14
DATA 565 UOP Wk 1 Scope & Descriptive Statistics & the Box Plot Worksheet
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DATA 565 UOP Wk 1 Scope & Descriptive Statistics & the Box Plot Worksheet
Assignment ContentResources: Pastas R Us, Inc. Database & Microsoft Excel®, Wk 1: Descriptive Statistics Analysis AssignmentPurpose This assignment is intended to help you learn how to apply statistical methods when analyzing operational data, evaluating the performance of current marketing strategies, and recommending actionable business decisions. This is an opportunity to build critical-thinking and problem-solving skills within the context of data analysis and interpretation. You’ll gain a first-hand understanding of how data analytics supports decision-making and adds value to an organization. Scenario: Pastas R Us, Inc. is a fast-casual restaurant chain specializing in noodle-based dishes, soups, and salads. Since its inception, the business development team has favored opening new restaurants in areas (within a 3-mile radius) that satisfy the following demographic conditions:Median age between 25 – 45 years oldHousehold median income above national averageAt least 15% college educated adult populationLast year, the marketing department rolled out a Loyalty Card strategy to increase sales. Under this program, customers present their Loyalty Card when paying for their orders and receive some free food after making 10 purchases.The company has collected data from its 74 restaurants to track important variables such as average sales per customer, year-on-year sales growth, sales per sq. ft., Loyalty Card usage as a percentage of sales, and others. A key metric of financial performance in the restaurant industry is annual sales per sq. ft. For example, if a 1200 sq. ft. restaurant recorded $2 million in sales last year, then it sold $1,667 per sq. ft.Executive management wants to know whether the current expansion criteria can be improved. They want to evaluate the effectiveness of the Loyalty Card marketing strategy and identify feasible, actionable opportunities for improvement. As a member of the analytics department, you’ve been assigned the responsibility of conducting a thorough statistical analysis of the company’s available database to answer executive management’s questions.Report: Write a 750-word statistical report that includes the following sections:Section 1: Scope and descriptive statisticsSection 2: AnalysisSection 3: Recommendations and ImplementationSection 1 - Scope and descriptive statisticsState the report’s objective.Discuss the nature of the current database. What variables were analyzed?Summarize your descriptive statistics findings from Excel. Use a table and insert appropriate graphs.Section 2 - Analysis Using Excel, create scatter plots and display the regression equations for the following pairs of variables:“BachDeg%” versus “Sales/SqFt”“MedIncome” versus “Sales/SqFt”“MedAge” versus “Sales/SqFt”“LoyaltyCard(%)” versus “SalesGrowth(%)”In your report, include the scatter plots. For each scatter plot, designate the type of relationship observed (increasing/positive, decreasing/negative, or no relationship) and determine what you can conclude from these relationships. Section 3: Recommendations and implementationBased on your findings above, assess which expansion criteria seem to be more effective.Could any expansion criterion be changed or eliminated? If so, which one and why?Based on your findings above, does it appear as if the Loyalty Card is positively correlated with sales growth? Would you recommend changing this marketing strategy?Based on your previous findings, recommend marketing positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?)Indicate what information should be collected to track and evaluate the effectiveness of your recommendations. How can this data be collected? (Hint: Would you use survey/samples or census?) Cite references to support your assignment.Format your citations according to APA guidelines.
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week 8 math302
nario Background:
A marketing company based out of New York City is doing well and is looking to expand internationa ...
week 8 math302
nario Background:
A marketing company based out of New York City is doing well and is looking to expand internationally. The CEO and VP of Operations decide to enlist the help of a consulting firm that you work for, to help collect data and analyze market trends.
You work for Mercer Human Resources. The Mercer Human Resource Consulting website lists prices of certain items in selected cities around the world. They also report an overall cost-of-living index for each city compared to the costs of hundreds of items in New York City (NYC). For example, London at 88.33 is 11.67% less expensive than NYC.
More specifically, if you choose to explore the website further you will find a lot of fun and interesting data. You can explore the website more on your own after the course concludes.
https://mobilityexchange.mercer.com/Insights/ cost-of-living-rankings#rankings
Assignment Guidance:
In the Excel document, you will find the 2018 data for 17 cities in the data set Cost of Living. Included are the 2018 cost of living index, cost of a 3-bedroom apartment (per month), price of monthly transportation pass, price of a mid-range bottle of wine, price of a loaf of bread (1 lb.), the price of a gallon of milk and price for a 12 oz. cup of black coffee. All prices are in U.S. dollars.
You use this information to run a Multiple Linear Regression to predict Cost of living, along with calculating various descriptive statistics. This is given in the Excel output (that is, the MLR has already been calculated. Your task is to interpret the data).
Based on this information, in which city should you open a second office in? You must justify your answer. If you want to recommend 2 or 3 different cities and rank them based on the data and your findings, this is fine as well.
Deliverable Requirements:
This should be ¾ to 1 page, no more than 1 single-spaced page in length, using 12-point Times New Roman font. You do not need to do any calculations, but you do need to pick a city to open a second location at and justify your answer based upon the provided results of the Multiple Linear Regression.
The format of this assignment will be an Executive Summary. Think of this assignment as the first page of a much longer report, known as an Executive Summary, that essentially summarizes your findings briefly and at a high level. This needs to be written up neatly and professionally. This would be something you would present at a board meeting in a corporate environment. If you are unsure of an Executive Summary, this resource can help with an overview. What is an Executive Summary?
Things to Consider:
To help you make this decision here are some things to consider:
Based on the MLR output, what variable(s) is/are significant?
From the significant predictors, review the mean, median, min, max, Q1 and Q3 values?
It might be a good idea to compare these values to what the New York value is for that variable. Remember New York is the baseline as that is where headquarters are located.
Based on the descriptive statistics, for the significant predictors, what city has the best potential?
What city or cities fall are below the median?
What city or cities are in the upper 3rd quartile?
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