Questions and Answers of Business Statistics Communicating

Denise Lau is an avid football fan and religiously follows every game of the National Football League. During the 2017 season, she meticulously keeps a record of how each quarterback has played
Refer to the previous exercise for a description of the data set. Denise feels that, for her presentation, it would remove some biases if the player names and team names are suppressed. Remove these
Ian Stevens is a human resource analyst working for the city of Seattle. He is performing a compensation analysis of city employees. The accompanying data set contains three variables: Department,
Refer to the previous exercise for a description of the problem and data set. The financial analyst wants to find out if there are any missing values in the data set. a. Are there any missing
Investors usually consider a variety of information to make investment decisions. The accompanying table displays a sample of large publicly traded corporations and their financial information.
The accompanying table contains a portion of data from the National Longitudinal Survey (NLS), which follows over 12,000 individuals in the United States over time. Variables in this analysis include
New Age Solar sells and installs solar panels for residential homes. The company’s sales representatives contact and pay a personal visit to potential customers to present the benefits of
Being able to predict machine failures before they happen can save millions of dollars for manufacturing companies. Manufacturers want to be able to perform preventive maintenance or repairs in
The accompanying data set contains three predictor variables (x1, x2, and x3) and the target variable (y). Partition the data in the Exercise_9.19_Data worksheet to develop a naïve Bayes
The accompanying data set contains three predictor variables (x1, x2, and x3) and the target variable (y). Partition the data in the Exercise_9.18_Data worksheet to develop a naïve Bayes
The accompanying data set contains three predictor variables (x1, x2, and x3) and the target variable (y). Partition the data to develop a naïve Bayes classification model where “1” denotes the
The accompanying data set contains three predictor variables (x1, x2, and x3) and the target variable (y). Partition the data to develop a naïve Bayes classification model where “1” denotes the
The accompanying data set contains two predictor variables (x1 and x2) and the target variable (y). Partition the data to develop a naïve Bayes classification model where “1” denotes the
The accompanying data set contains three predictor variables (x1, x2, and x3) and the target variable (y). a. Bin predictor variables x1, x2, and x3. For Analytic Solver, choose the Equal count
The accompanying data set contains three predictor variables (x1, x2, and x3) and the target variable (y). a. Bin predictor variables x1 and x2. For Analytic Solver, choose the Equal count
The accompanying data set contains three predictor variables (x1, x2, and x3) and the target variable (y). a. Bin predictor variables x1, x2, and x3. For Analytic Solver, choose the Equal
The accompanying data set contains four predictor variables (x1, x2, x3, and x4) and the target variable (y). a. Bin predictor variables x1, x2, x3, and x4. For Analytic Solver, choose the Equal
Every year, hundreds of thousands of international students apply to graduate programs in the United States. Two of the most important admissions criteria are undergraduate GPAs and TOEFL scores. An
Admission to medical schools in the United States is highly competitive. The acceptance rate to the top medical schools could be as low as 2% or 3%. With such a low acceptance rate, medical school
Jerry Stevenson is the manager of a travel agency. He wants to build a model that can predict customers’ annual spending on travel products. He has compiled a data set that contains the following
An online retailer is offering a new line of running shoes. The retailer plans to send out an e-mail with a discount offer to some of its existing customers and wants to know if it can use data
A home improvement retail store is offering its customers store-branded credit cards that come with a deep discount when used to purchase in-store home improvement products. To maintain the
Forbes magazine published an article that studied career accomplishments and factors that might contribute to career success (August 30, 2018). It turns out that career success has less to do with
Predicting whether or not an entering freshman student will drop out of college has been a challenge for many higher education institutions. Nelson Touré, a senior student success adviser at an
A community center is launching a campaign to recruit local residents to help maintain a protected nature preserve area that encompasses extensive walking trails, bird watching blinds, wild flowers,
Nora Jackson owns a number of vacation homes on a beach. She works with a consortium of rental home owners to gather a data set to build a classification model to predict the likelihood of potential
Insurance companies use a number of factors to help determine the premium amount for car insurance coverage. Discounts or a lower premium may be given based on factors including credit scores,
A mobile gaming company wants to study a group of its existing customers about their in-game purchases. A data set, a portion of which is shown in the accompanying table, is extracted and includes
Michelle McGrath is a college student working to complete an undergraduate research project to fulfill her psychology degree requirements. She is interested in how physical and behavioral factors
Credit card fraud is becoming a serious problem for the financial industry and can pose a considerable cost to banks, credit card issuers, and consumers. Fraud detection using data mining techniques
Refer to Exercise 9.16 for the description of a solar panel company called New Age Solar and the Solar_Data worksheet. a. Bin the Age and Income variables in the Solar_Data worksheet as follows.
As millions of people in the U.S. are crippled by student loan debt and high unemployment, policymakers are raising the question of whether college is even a good investment. Richard Clancy, a
The classification tree below relates type of wine (A, B, or C) to alcohol content, flavonoids, malic acid, and magnesium. Classify each of the following wines of unknown class.a. Wine with alcohol =
The accompanying data set contains two predictor variables, age and income, and one binary target variable, newspaper subscription (subscribe), indicating whether or not the person subscribes to a
The accompanying data set contains two predictor variables, average annual number of sunny days (days) and average annual precipitation (precipitation), and one numeric target variable, average
Refer to the previous exercise for a description of the problem and data set. Build a default classification tree to predict whether a customer has plans to travel within the next year. Display the
After a data set was partitioned, the first partition contains 43 cases that belong to Class 1 and 12 cases that belong to Class 0, and the second partition contains 24 cases that belong to Class 1
Refer to the previous exercise for a description of the problem and data set. Create a classification tree model for predicting whether the community member is likely to enroll in summer courses
After a data set was partitioned using the split value of 45.5 for age. The age < 45.5 partition contains 22 patients with a diabetes diagnosis and 178 patients without a diabetes diagnosis, and
Jerry Stevenson is the manager of a travel agency. He wants to build a model that can predict whether or not a customer will travel within the next year. He has compiled a data set that contains the
The following data set in the Church_ Data worksheet is used to classify individuals as likely or unlikely to attend church using five predictor variables: years of education (Educ), annual income
Use the accompanying data set to answer the following questions. a. Which split value for age would best separate the newspaper subscribers from nonsubscribers based on the Gini impurity
Samantha Brown is Director of Continuing Education of a major university. The Continuing Education department offers a wide range of five-week courses to the community during the summer. Samantha
Sunnyville Bank wants to identify customers who may be interested in its new mobile banking app. The worksheet called Mobile_Banking_Data contains 500 customer records collected from a previous
The accompanying data set in the Exercise_10.7_Data worksheet contains four predictor variables (x1 to x4) and one binary target variable (y). Select the best-pruned tree for scoring and display the
Refer to the previous exercise for a description of the problem and data set. Build a default classification tree to predict whether a customer will download the mobile banking app. Display the
The accompanying data set in the Exercise_10.8_Data worksheet contains four predictor variables (x1 to x4) and one binary target variable (y). Select the best-pruned tree for scoring and display the
The accompanying data set contains five predictor variables (x1 to x5) and one binary target variable (y). Follow the instructions below to create classification trees using the Exercise_10.9_Data
Dereck Anderson is an institutional researcher at a major university. The university has set a goal to increase the number of students who graduate within four years by 20% in five years. Dereck is
Refer to the previous exercise for a description of the problem and data set. Build a default classification tree to predict whether an individual is likely to attend church. Display the default
The accompanying data set contains four predictor variables (x1 to x4) and one binary target variable (y). Follow the instructions below to create classification trees using the Exercise_10.10_Data
Refer to the previous exercise for a description of the problem and data set. Build a default classification tree to predict whether the gamer will make in-app purchases. Display the classification
Refer to the previous exercise for a description of the problem and data set. Create a classification tree model for predicting whether the student will be able to graduate within four years (Grad).
Monstermash, an online game app development company, wants to be able to predict which gamers are likely to make in-app purchases. Ranon Weatherby, the company’s data analyst, has compiled a data
Refer to the previous exercise for a description of the data set. Create a regression tree model for predicting house prices (Price). Select the best-pruned tree for scoring and display the
The accompanying data set contains two predictor variables, x1 and x2, and one numerical target variable, y. A regression tree will be constructed using the data set.a. List the possible split values
The accompanying data set contains three predictor variables, x1, x2, and x3, and one numerical target variable, y. A regression tree will be constructed using the data set.a. List the possible split
Refer to the previous exercise for a description of the data set. Build a default regression tree to predict the customer’s spending during the first three months of the year (Spending). Display
The accompanying data set contains two predictor variables, x1 and x2, and one numerical target variable, y. A regression tree will be constructed using the data set.a. Which split on x1 will
Refer to the previous exercise for a description of the data set. Build a default regression tree to predict the customer’s annual household spending on travel products (TravelSpend). Display the
Create a regression tree using the accompanying data set in Exercise_10.28_Data worksheet (predictor variables: x1 to x5; target: y). Select the best-pruned tree for scoring and display the
Create a regression tree using the accompanying data set (predictor variables: x1 to x4; target: y). Select the best-pruned tree for scoring and display the full-grown, best-pruned and minimum error
An online retail company is trying to predict customer spending in the first three months of the year. Brian Duffy, the marketing analyst of the company, has compiled a data set on 200 existing
Create a regression tree using the accompanying data set in the Exercise_10.30_Data worksheet (predictor variables: x1 to x4; target: y). a. Use the rpart function to build a default regression
Refer to the previous exercise for a description of the data set. Build a default regression tree to predict an NBA player’s salary (salary). Display the regression tree. a. What are the
Create a regression tree using the accompanying data set (predictor variables: x1 to x4; target: y). Select the best-pruned tree for scoring and display the full-grown, best-pruned, and minimum error
Kyle Robson, an energy researcher for the U.S. Energy Information Administration, is trying to build a model for predicting annual electricity retail sales for states. Kyle has compiled a data set
Create a regression tree using the accompanying data set (predictor variables: x1 to x4; target: y).a. Use the rpart function to build a default regression tree. Display the tree using the prp
Refer to the previous exercise for a description of the data set. Create a regression tree model for predicting per capita electricity retail sales (Sales). Select the best-pruned tree for scoring
Merrick Stevens is a sports analyst working for ACE Sports Management, a sports agency that represents over 200 athletes. He is interested in understanding the relationship between an NBA player’s
New Age Solar sells and installs solar panels for residential homes. The company’s sales representatives contact and pay a personal visit to potential customers to present the benefits of
Ben Derby is a highly paid scout for a professional baseball team. He attends at least five or six Major League Baseball games a week and watches as many recorded games as he can in order to evaluate
Create a bagging ensemble classification tree model using the accompanying data set (predictor variables: x1 to x4; target: y). a. What are the overall accuracy rate, sensitivity, and
Create a boosting ensemble classification tree model using the accompanying data set (predictor variables: x1 to x4; target: y). a. What are the overall accuracy rate, sensitivity, and
Create a random forest ensemble classification tree model using the accompanying data set (predictor variables: x1 to x4; target: y). Select two predictor variables randomly to construct each weak
Create a bagging ensemble classification tree model using the accompanying data set (predictor variables: x1 to x5; target: y). a. What are the overall accuracy rate, sensitivity, and
Create a boosting ensemble classification tree model using the accompanying data set (predictor variables: x1 to x5; target: y). a. What are the overall accuracy rate, sensitivity, and
Create a random forest ensemble classification tree model using the accompanying data set (predictor variables: x1 to x5; target: y). Select two predictor variables randomly to construct each weak
Consider the following LP problem where x1 and x2 represent the decision variables. Solve the LP problem to answer the following questions. a. What are the values of x1 and x2 at the
Perform k-means clustering on all the variables in the accompanying data set. Do not standardize the variables. a. Specify the k value as 2 and plot the cluster membership using the cluster and
A local coffee shop observes that, on average, four customers enter the store every 5 minutes during the rush hour between 6:30 am and 7:30 am each day. The number of customers arriving at the coffee
Hoping to increase its sales, a pizzeria wants to start a new marketing campaign promising its customers that if their order does not get delivered within an hour, the pizzas are free. Historically,
The regression tree below relates credit score to number of defaults (NUM DEF), revolving balance (REV BAL), and years of credit history (YRS HIST). Predict the credit score of each of the following
The accompanying data set contains three predictor variables, x1, x2, and x3, and one numerical target variable, y. A regression tree will be constructed using the data set.a. Which split on x1 will
Create a regression tree using the accompanying data set in the Exercise_10.31 worksheet (predictor variables: x1 to x4; target: y). a. Use the rpart function to build a default regression tree.
Mateo Derby works as a cyber security analyst at a private equity firm. His colleagues at the firm have been inundated by a large number of spam e-mails. Mateo has been asked to implement a spam
Daniella Lara, a human resources manager at a large tech consulting firm, has been reading about using analytics to predict the success of new employees. With the fast-changing nature of the tech
In recent years, medical research has incorporated the use of data analytics to find new ways to detect heart disease in its early stage. Medical doctors are particularly interested in accurately
Admission to medical school in the United States is highly competitive. The acceptance rate to the top medical schools could be as low as 2% or 3%. With such a low acceptance rate, medical school
Credit card fraud is becoming a serious problem for the financial industry and can pose a considerable cost to banks, credit card issuers, and consumers. Fraud detection using data mining techniques
Refer to Exercise 11 for a description of the data set. Partition the data into 60% training and 40% validation data. For Analytic Solver, use 12345 as the random seed and create 10 weak learners.
Refer to Exercise 13 for a description of the data set. a. Create a boosting ensemble classification tree model. What are the overall accuracy rate, sensitivity, and specificity of the model on
Refer to Exercise 15 for a description of the data set. a. Create a random forest ensemble classification tree model. Select two predictor variables randomly to construct each weak learner. What
Refer to Exercise 19 for a description of the data set. a. Create a random forest ensemble classification tree model. Select three predictor variables randomly to construct each weak learner.
Refer to Exercise 21 for a description of the data set. a. Create a boosting ensemble classification tree model. What are the overall accuracy rate, sensitivity, and specificity of the model on
Ramona Kim is a California Highway Patrol (CHP) officer who works in the city of San Diego. Having lost her own uncle in a car accident, she is particularly interested in educating local drivers
Perform agglomerative clustering on the accompanying data set. a. Include all five variables, first standardized to z-scores, for the analysis. Choose Euclidean for the distance between
Perform agglomerative clustering on the accompanying data set. Include all seven variables, first standardized to z-scores, for the analysis. Choose Euclidean for the distance between observations
Perform agglomerative clustering on the accompanying data set. Include all seven variables, first standardized to z-scores, for the analysis. Choose Euclidean for the distance between observations
Perform agglomerative clustering on the accompanying data set. a. Include all five variables, first standardized to z-scores. Use the Euclidean distance for similarity and single linkage for the

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