Description
A younger plants 8 berries. If he waters them, there is a 90% chance that the berry tree will grow extra berries.
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Explanation & Answer
3/8 x9/10 = 27/80
= 27/80 or 33.75%
Completion Status:
100%
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Determine whether the following equation defines y as a function of x:
x2 + y2 = 16A. Y is a function of xB. X is not a function of yC. X is a function of x.D. Y is not a function of x
Determine whether the following equation defines y as a function of x:
x2 + y2 = 16A. Y is a function of xB. X is not a function of yC. X is a function of x.D. Y is not a function of x
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.
4 pages
Scenario Analysis
Other factors to be consider when predicting future car sales Other than interest rates, other factors should also be cons ...
Scenario Analysis
Other factors to be consider when predicting future car sales Other than interest rates, other factors should also be considered when predicting ...
New York Survey Data
Instructions A consulting firm was hired to perform a survey on people living in New York City. The survey was completed m ...
New York Survey Data
Instructions A consulting firm was hired to perform a survey on people living in New York City. The survey was completed monthly for six months by 445 randomly-selected people in different boroughs. There were a number of items on the survey, but six basic biographical items will be studied for this exercise. The data for the people surveyed in one of these monthly surveys can be found in the Excel file Survey (attached below). The variables that were used for the basic biographical data are found on the last page of the exercise.In this exercise, some of the estimation techniques presented in the module will be applied to the New York survey results. You may assume that these respondents represent a simple random sample of all potential respondents within the community, and that the population is large enough that application of the finite population correction would not make an appreciable difference in the results.New York City governmental agency personnel like to have point estimates regarding variables describing the biographical information of the people living within the different boroughs. It is very helpful for them to have some idea regarding the likely accuracy of these estimates as well. Therein lies the benefit of the techniques presented in this module and applied here.Item A in the description of the data collection instrument lists variables 1–5, which represent the respondent’s general attitude toward each of the five boroughs. Each of these variables has numerically equal distances between the possible responses, and for purposes of analysis they may be considered to be of the interval scale of measurement.Determine the point estimate, and then construct the 95% confidence interval for μ1 = the average attitude toward Manhattan.Repeat part (a) for μ2 through μ5, the average attitudes toward Brooklyn, Queens, The Bronx and Staten Island, respectively.Given the breakdown of responses for variable 6 (highest level of education), determine the point estimate, and then construct the 95% confidence interval for p6 = the population proportion of doctoral degrees.Given the breakdown of responses for variable 7 (marital status of respondent), determine the point estimate, and then construct the 95% confidence interval for p7 = the population proportion in the “single or other” category.Assume the governmental agencies requested estimates of the mean attitudes towards each borough with a margin of error of 0.05 for each borough. If the governmental agency personnel want to have 95% confidence that the sample mean will fall within this margin of error, how large should the sample sizes be for each borough?Paper Requirements Write a report that uses the Written Assignment Requirements under the heading Expectations for CSU-Global Written Assignments found in the CSU-Global Guide to Writing and APA. Items that should be included, at a minimum, are a title page, an introduction, a body that answers the questions posed in the problem, and a conclusion paragraph that addresses your findings and what you have determined from the data and your analysis. As with all written assignments, you should have in-text citations and a reference page. Please include any tables of calculations, calculated values, and graphs associated with this problem in the body of your assignment response.Note: You must submit your Excel file with your report. This will aid in grading with partial credit if errors are found in the report.A. General Attitude toward Each Borough (Variables 1–5)1. Manhattan2. Brooklyn3. Queens4. The Bronx5. Staten IslandLike Very Much(5)(5)(5)(5)(5)Like(4)(4)(4)(4)(4)Neutral(3)(3)(3)(3)(3)Dislike(2)(2)(2)(2)(2)Dislike Very Much(1)(1)(1)(1)(1)B. Information about the Respondent (Variables 6–7)What is your highest level of education? (1) = Did not complete high school (2) = High school degree/GED (3) = Associate’s degree (4) = Bachelor’s degree (5) = Master’s degree (6) = Doctoral degreeMarital Status: (1) = Married, (2) = Single or other
Hypothetical sales
You manage Human Relations for your company. One of your sales managers has retired, leaving an opening. You are conside ...
Hypothetical sales
You manage Human Relations for your company. One of your sales managers has retired, leaving an opening. You are considering two different employees for the position. Both are highly qualified so you have decided to evaluate their sales performance for the past year. The data showing a sample of their respective sales performance is in the Excel worksheet.1. Annual sales data is provided for a sample of 50 employees.Determine the range of values in which you would expect to find the average weekly sales for the entire sales force in your company 90% of the timeWhat is the impact of increasing the confidence level to 95%?*What is the impact of increasing the sample size to 150, assuming the same mean and standard deviation, but allowing the confidence level to remain at 90%?*2. You want to determine whether there is a statistically different average weekly sales between Sales Rep A and Sales Rep B.Create Null and Alternative Hypothesis statements that would allow you to determine whether their sales performance is statistically different or not.Based on your hypothesis statements, provide an example of a Type I and a Type II error, respectively.Using a significance level of .05, conduct a t-test of independent samples when the standard deviation of the population is unknown to compare the average weekly sales of the two candidates. (NOTE: YOU CAN DO THIS VERY QUICKLY IN EXCEL USING THE DATA ANALYSIS TOOLPAK!)What is the p-value? Based on the p-value, is there a statistically significant difference between the two reps being considered for the manager's position? Explain your reasoning.Who would you recommend to be promoted to Sales Manager - Rep A or Rep B? Why?All Sales Representatives are expected to meet an average weekly sales quota of $4500.Create Null and Alternative Hypothesis statements that would allow you to determine whether the weekly sales of the individual you have decided to promote in #4 (Rep A or Rep B) exceeds the company quota.Based on your hypothesis statements, provide an example of a Type I and a Type II error, respectively.Using a significance level of .05, conduct a one sample mean hypothesis test to determine whether the performance of your chosen candidate exceeds the quota by a statistically significant amount?What is the p-value?Based on the p-value, what is your conclusion? SAMPLE - WEEKLY SALES SAMPLE OF WEEKLY SALES FOR REP A AND B # Sales Rep Weekly Sales $ Week # Weekly Sales - A Weekly Sales - B 1 1228 1 4657 5839 2 7374 2 6133 2602 3 1055 3 3438 2830 4 1859 4 7394 4763 5 3938 5 4327 3740 6 1692 6 2552 1315 7 569 7 7063 1599 8 4059 8 7844 1629 9 3689 9 6898 2416 10 607 10 4003 2107 11 1370 11 6884 4237 12 3735 12 4007 6322 13 3305 13 7214 2710 14 7228 14 2358 5890 15 6279 15 7745 5119 16 1671 16 1337 5184 17 5708 17 1052 3439 18 2569 18 6056 4828 19 4163 19 1495 3667 20 1519 20 3530 2518 21 7734 21 4749 6073 22 784 22 3833 5566 23 6766 23 7869 4555 24 7261 24 4541 5867 25 5034 25 6882 6039 26 7115 26 3868 1032 27 6291 27 5934 4834 28 6287 28 4447 3687 29 2080 29 5504 2214 30 7621 30 5554 4659 31 1047 32 6517 33 5172 34 3876 35 5429 36 4538 37 3786 38 2510 39 4863 40 7246 41 1175 42 641 43 4269 44 7034 45 3406 46 2256 47 3182 48 5178 49 4428 50 1189
MAT 274 Penn State University Probability and Inferential Statistics Paper
Need Q#3 done in 10 hours. Need Q#3 done in 10 hours Need Q#3 done in 10 hours. Please have a look at rubric also.Thanks
MAT 274 Penn State University Probability and Inferential Statistics Paper
Need Q#3 done in 10 hours. Need Q#3 done in 10 hours Need Q#3 done in 10 hours. Please have a look at rubric also.Thanks
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Most Popular Content
Determine whether the following equation defines y as a function of x:
x2 + y2 = 16A. Y is a function of xB. X is not a function of yC. X is a function of x.D. Y is not a function of x
Determine whether the following equation defines y as a function of x:
x2 + y2 = 16A. Y is a function of xB. X is not a function of yC. X is a function of x.D. Y is not a function of x
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.
4 pages
Scenario Analysis
Other factors to be consider when predicting future car sales Other than interest rates, other factors should also be cons ...
Scenario Analysis
Other factors to be consider when predicting future car sales Other than interest rates, other factors should also be considered when predicting ...
New York Survey Data
Instructions A consulting firm was hired to perform a survey on people living in New York City. The survey was completed m ...
New York Survey Data
Instructions A consulting firm was hired to perform a survey on people living in New York City. The survey was completed monthly for six months by 445 randomly-selected people in different boroughs. There were a number of items on the survey, but six basic biographical items will be studied for this exercise. The data for the people surveyed in one of these monthly surveys can be found in the Excel file Survey (attached below). The variables that were used for the basic biographical data are found on the last page of the exercise.In this exercise, some of the estimation techniques presented in the module will be applied to the New York survey results. You may assume that these respondents represent a simple random sample of all potential respondents within the community, and that the population is large enough that application of the finite population correction would not make an appreciable difference in the results.New York City governmental agency personnel like to have point estimates regarding variables describing the biographical information of the people living within the different boroughs. It is very helpful for them to have some idea regarding the likely accuracy of these estimates as well. Therein lies the benefit of the techniques presented in this module and applied here.Item A in the description of the data collection instrument lists variables 1–5, which represent the respondent’s general attitude toward each of the five boroughs. Each of these variables has numerically equal distances between the possible responses, and for purposes of analysis they may be considered to be of the interval scale of measurement.Determine the point estimate, and then construct the 95% confidence interval for μ1 = the average attitude toward Manhattan.Repeat part (a) for μ2 through μ5, the average attitudes toward Brooklyn, Queens, The Bronx and Staten Island, respectively.Given the breakdown of responses for variable 6 (highest level of education), determine the point estimate, and then construct the 95% confidence interval for p6 = the population proportion of doctoral degrees.Given the breakdown of responses for variable 7 (marital status of respondent), determine the point estimate, and then construct the 95% confidence interval for p7 = the population proportion in the “single or other” category.Assume the governmental agencies requested estimates of the mean attitudes towards each borough with a margin of error of 0.05 for each borough. If the governmental agency personnel want to have 95% confidence that the sample mean will fall within this margin of error, how large should the sample sizes be for each borough?Paper Requirements Write a report that uses the Written Assignment Requirements under the heading Expectations for CSU-Global Written Assignments found in the CSU-Global Guide to Writing and APA. Items that should be included, at a minimum, are a title page, an introduction, a body that answers the questions posed in the problem, and a conclusion paragraph that addresses your findings and what you have determined from the data and your analysis. As with all written assignments, you should have in-text citations and a reference page. Please include any tables of calculations, calculated values, and graphs associated with this problem in the body of your assignment response.Note: You must submit your Excel file with your report. This will aid in grading with partial credit if errors are found in the report.A. General Attitude toward Each Borough (Variables 1–5)1. Manhattan2. Brooklyn3. Queens4. The Bronx5. Staten IslandLike Very Much(5)(5)(5)(5)(5)Like(4)(4)(4)(4)(4)Neutral(3)(3)(3)(3)(3)Dislike(2)(2)(2)(2)(2)Dislike Very Much(1)(1)(1)(1)(1)B. Information about the Respondent (Variables 6–7)What is your highest level of education? (1) = Did not complete high school (2) = High school degree/GED (3) = Associate’s degree (4) = Bachelor’s degree (5) = Master’s degree (6) = Doctoral degreeMarital Status: (1) = Married, (2) = Single or other
Hypothetical sales
You manage Human Relations for your company. One of your sales managers has retired, leaving an opening. You are conside ...
Hypothetical sales
You manage Human Relations for your company. One of your sales managers has retired, leaving an opening. You are considering two different employees for the position. Both are highly qualified so you have decided to evaluate their sales performance for the past year. The data showing a sample of their respective sales performance is in the Excel worksheet.1. Annual sales data is provided for a sample of 50 employees.Determine the range of values in which you would expect to find the average weekly sales for the entire sales force in your company 90% of the timeWhat is the impact of increasing the confidence level to 95%?*What is the impact of increasing the sample size to 150, assuming the same mean and standard deviation, but allowing the confidence level to remain at 90%?*2. You want to determine whether there is a statistically different average weekly sales between Sales Rep A and Sales Rep B.Create Null and Alternative Hypothesis statements that would allow you to determine whether their sales performance is statistically different or not.Based on your hypothesis statements, provide an example of a Type I and a Type II error, respectively.Using a significance level of .05, conduct a t-test of independent samples when the standard deviation of the population is unknown to compare the average weekly sales of the two candidates. (NOTE: YOU CAN DO THIS VERY QUICKLY IN EXCEL USING THE DATA ANALYSIS TOOLPAK!)What is the p-value? Based on the p-value, is there a statistically significant difference between the two reps being considered for the manager's position? Explain your reasoning.Who would you recommend to be promoted to Sales Manager - Rep A or Rep B? Why?All Sales Representatives are expected to meet an average weekly sales quota of $4500.Create Null and Alternative Hypothesis statements that would allow you to determine whether the weekly sales of the individual you have decided to promote in #4 (Rep A or Rep B) exceeds the company quota.Based on your hypothesis statements, provide an example of a Type I and a Type II error, respectively.Using a significance level of .05, conduct a one sample mean hypothesis test to determine whether the performance of your chosen candidate exceeds the quota by a statistically significant amount?What is the p-value?Based on the p-value, what is your conclusion? SAMPLE - WEEKLY SALES SAMPLE OF WEEKLY SALES FOR REP A AND B # Sales Rep Weekly Sales $ Week # Weekly Sales - A Weekly Sales - B 1 1228 1 4657 5839 2 7374 2 6133 2602 3 1055 3 3438 2830 4 1859 4 7394 4763 5 3938 5 4327 3740 6 1692 6 2552 1315 7 569 7 7063 1599 8 4059 8 7844 1629 9 3689 9 6898 2416 10 607 10 4003 2107 11 1370 11 6884 4237 12 3735 12 4007 6322 13 3305 13 7214 2710 14 7228 14 2358 5890 15 6279 15 7745 5119 16 1671 16 1337 5184 17 5708 17 1052 3439 18 2569 18 6056 4828 19 4163 19 1495 3667 20 1519 20 3530 2518 21 7734 21 4749 6073 22 784 22 3833 5566 23 6766 23 7869 4555 24 7261 24 4541 5867 25 5034 25 6882 6039 26 7115 26 3868 1032 27 6291 27 5934 4834 28 6287 28 4447 3687 29 2080 29 5504 2214 30 7621 30 5554 4659 31 1047 32 6517 33 5172 34 3876 35 5429 36 4538 37 3786 38 2510 39 4863 40 7246 41 1175 42 641 43 4269 44 7034 45 3406 46 2256 47 3182 48 5178 49 4428 50 1189
MAT 274 Penn State University Probability and Inferential Statistics Paper
Need Q#3 done in 10 hours. Need Q#3 done in 10 hours Need Q#3 done in 10 hours. Please have a look at rubric also.Thanks
MAT 274 Penn State University Probability and Inferential Statistics Paper
Need Q#3 done in 10 hours. Need Q#3 done in 10 hours Need Q#3 done in 10 hours. Please have a look at rubric also.Thanks
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