Preventing children from becoming involved in gangs
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What are some of the factors that draw juveniles toward gang involvement, and what are the challenges of preventing such involvement through programming?
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The Universal Bank data set will be needed for this assignment. To access the data set, review the "Universal Bank" topic ...
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The Universal Bank data set will be needed for this assignment. To access the data set, review the "Universal Bank" topic Resource.
Part 1: Using R, complete all portions of Problem 1 in Chapter 8. Please note, you can add comments using # in your code to address the narrative parts of the problem. Be sure to include your R code and R output as a .txt file with your submission.
Problems
Personal Loan Acceptance. The file UniversalBank.csv contains data on 5000 customers of Universal Bank. The data include customer demographic information (age, income, etc.), the customer’s relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign. In this exercise, we focus on two predictors: Online (whether or not the customer is an active user of online banking services) and Credit Card (abbreviated CC below) (does the customer hold a credit card issued by the bank), and the outcome Personal Loan (abbreviated Loan below).Partition the data into training (60%) and validation (40%) sets.
Create a pivot table for the training data with Online as a column variable, CC as a row variable, and Loan as a secondary row variable. The values inside the table should convey the count. In R use functions melt() and cast(), or function table().
Consider the task of classifying a customer who owns a bank credit card and is actively using online banking services. Looking at the pivot table, what is the probability that this customer will accept the loan offer? [This is the probability of loan acceptance (Loan = 1) conditional on having a bank credit card (CC = 1) and being an active user of online banking services (Online = 1)].
Create two separate pivot tables for the training data. One will have Loan (rows) as a function of Online (columns) and the other will have Loan (rows) as a function of CC.
Compute the following quantities [P(A ? B) means “the probability of A given B”]:
P(CC = 1 ? Loan = 1) (the proportion of credit card holders among the loan acceptors)
P(Online = 1 ? Loan = 1)
P(Loan = 1) (the proportion of loan acceptors)
P(CC = 1 ? Loan = 0)
P(Online = 1 ? Loan = 0)
P(Loan = 0)
Use the quantities computed above to compute the naive Bayes probabilityP(Loan = 1 ? CC = 1, Online = 1).
Compare this value with the one obtained from the pivot table in (b). Which is a more accurate estimate?
Which of the entries in this table are needed for computing P(Loan = 1 ? CC = 1, Online = 1)? In R, run naive Bayes on the data. Examine the model output on training data, and find the entry that corresponds to P(Loan = 1 ? CC = 1, Online = 1). Compare this to the number you obtained in (e).
Part 2: How can the bank use the information about online customers and those with credit cards to inform its strategy for increasing the number of personal loans accepted by customers? Present your findings and recommendations to management in the form of an executive summary that includes relevant data, charts, and tables in Microsoft Word.
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BUS 322 Lynn University Predicting Wages Regression Analysis Questions
Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more varia ...
BUS 322 Lynn University Predicting Wages Regression Analysis Questions
Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable.Key TermsAn explanatory variable (X) is a type of independent variable. The two terms are often used interchangeably. But there is a subtle difference between the two. When a variable is independent, it is not affected at all by any other variables. When a variable isn't independent for certain, it's an explanatory variable.An explained variable (Y) is a type of dependent variable. It is the variable in which we are interested. The effects of the explanatory variables are present in the explained variable.HypothesisHo: X is not a significant factor in explaining YHa: X is a significant factor in explaining YP Value -- This is the probability of making a Type I error.Rejection rule -- If P <= alpha, we reject the Ho.https://www.youtube.com/watch?v=1XX2pHa2mwoVideo helpQuestionsWhich factors are significant in predicting wages?How much explanatory power do these factors have together? Of those factors that are significant, which has the highest impact on wages?Use those factors to predict the wages for each of the following individualsJoe is a 48 year old married man with 22 years education, 25 years experience, and lives in Louisiana.Monica is a 50 year old single woman with 18 years education, 35 years experience, and lives in Texas.Jamal is a 58 year old married man with 16 years education, 40 years experience, and lives in NYC.Izzy is a 35 year old married man with 12 years education, 20 years experience, and lives in Florida.Input your own stats and see what your wages would be.Please submit word doc with question and excel file with excel work
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Identify a situation where stereotypes (as a schemata used in organizing perceptions) may be useful and helpful in a commu ...
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Identify a situation where stereotypes (as a schemata used in organizing perceptions) may be useful and helpful in a communication situation. Next, identify a situation in which that same stereotype becomes unproductive and perhaps damaging. Look for both the useful and necessary aspects of stereotyping, as well as the negative connotations this term often carries. That is, stereotypes allow humans to make predictions about situations and people, and that this is a necessary process so we are not constantly overwhelmed with new information. However, this can be unproductive if we do not continually seek to engage in person-centeredness to distinguish people from the social groups that categorize them.
Grand Canyon University Data Analytics Naïve Analysis & Summary
The Universal Bank data set will be needed for this assignment. To access the data set, review the "Universal Bank" topic ...
Grand Canyon University Data Analytics Naïve Analysis & Summary
The Universal Bank data set will be needed for this assignment. To access the data set, review the "Universal Bank" topic Resource.
Part 1: Using R, complete all portions of Problem 1 in Chapter 8. Please note, you can add comments using # in your code to address the narrative parts of the problem. Be sure to include your R code and R output as a .txt file with your submission.
Problems
Personal Loan Acceptance. The file UniversalBank.csv contains data on 5000 customers of Universal Bank. The data include customer demographic information (age, income, etc.), the customer’s relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign. In this exercise, we focus on two predictors: Online (whether or not the customer is an active user of online banking services) and Credit Card (abbreviated CC below) (does the customer hold a credit card issued by the bank), and the outcome Personal Loan (abbreviated Loan below).Partition the data into training (60%) and validation (40%) sets.
Create a pivot table for the training data with Online as a column variable, CC as a row variable, and Loan as a secondary row variable. The values inside the table should convey the count. In R use functions melt() and cast(), or function table().
Consider the task of classifying a customer who owns a bank credit card and is actively using online banking services. Looking at the pivot table, what is the probability that this customer will accept the loan offer? [This is the probability of loan acceptance (Loan = 1) conditional on having a bank credit card (CC = 1) and being an active user of online banking services (Online = 1)].
Create two separate pivot tables for the training data. One will have Loan (rows) as a function of Online (columns) and the other will have Loan (rows) as a function of CC.
Compute the following quantities [P(A ? B) means “the probability of A given B”]:
P(CC = 1 ? Loan = 1) (the proportion of credit card holders among the loan acceptors)
P(Online = 1 ? Loan = 1)
P(Loan = 1) (the proportion of loan acceptors)
P(CC = 1 ? Loan = 0)
P(Online = 1 ? Loan = 0)
P(Loan = 0)
Use the quantities computed above to compute the naive Bayes probabilityP(Loan = 1 ? CC = 1, Online = 1).
Compare this value with the one obtained from the pivot table in (b). Which is a more accurate estimate?
Which of the entries in this table are needed for computing P(Loan = 1 ? CC = 1, Online = 1)? In R, run naive Bayes on the data. Examine the model output on training data, and find the entry that corresponds to P(Loan = 1 ? CC = 1, Online = 1). Compare this to the number you obtained in (e).
Part 2: How can the bank use the information about online customers and those with credit cards to inform its strategy for increasing the number of personal loans accepted by customers? Present your findings and recommendations to management in the form of an executive summary that includes relevant data, charts, and tables in Microsoft Word.
22 pages
Lucid Motors
Lucid Motors is a US electric vehicle manufacturing firm based in Newark, California. It was founded in 2007 as a manufact ...
Lucid Motors
Lucid Motors is a US electric vehicle manufacturing firm based in Newark, California. It was founded in 2007 as a manufacturer of electric vehicle ...
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