Consumer research presentation
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This document summarizes the results of a regression analysis conducted by Consumer Research, Inc. Team #4. The analysis used income and household size as independent variables to predict annual credit card charges. It presents the estimated regression equation and uses it to predict charges for a 3-person household with $40,000 annual income. It then suggests some additional independent variables like average age, purchase preference, and gender that could improve the model by reducing errors and multicollinearity issues.
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This document provides an overview of analytics. It defines analytics as using data, technology, analysis and models to help managers make better decisions. It discusses different types of analytics including descriptive, predictive and prescriptive. Descriptive analytics examines past performance, predictive analytics predicts the future by detecting patterns in data, and prescriptive analytics identifies the best alternatives. The document also covers topics such as tools, data, models, and using analytics to solve business problems.
Bank churn with Data Science
In this presentation I apply Logistic Regression, Support Vector Machine and Random Forest to gather insights that lead to why leave a bank
Beyond the Spreadsheet
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Business Analytics.pptx
This document provides an introduction to analytics. It discusses how analytics uses data, information technology, statistical analysis and models to help managers make better decisions. Some potential applications of analytics discussed include pricing, customer segmentation, merchandising and location selection. The document also discusses descriptive, predictive and prescriptive analytics and some common analytics tools and challenges. It provides an overview of how analytics can be used to solve business problems.
Neural Network Model
The objective was to develop a neural network model to predict loan defaults using variables like number of derogatory reports, number of delinquent credit lines, debt-to-income ratio, age of oldest credit line, and loan divided by value. The best model had a training profit of $25.48 million and testing profit of $27.48 million, outperforming logistic regression. Key changes were reducing variables and lowering the probability cutoff, which increased profits and lift. The neural network model was simpler, more consistent and predictable than complex models, with training and testing profits varying less than $150,000.
9 Salary Surveys A Snapshot Market data obtained from salary surv.docx
Here is a 4-page response to the questions in APA format with citations:
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Reduction in customer complaints - Mortgage Industry
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
BA 301.01Chapter 3 HomeworkIdentifyQ1) Examine the data se.docx
BA 301.01
Chapter 3 Homework
Identify
Q1) Examine the data set on the worksheet Q1. This should be familiar; you saw this data set in last week’s homework. Recall that this is a survey of 500 households. The definitions of the variables can be found by reading the comments (the little red triangles).
a) Suppose we wanted to examine the distribution of the variable Utilities. It would be appropriate to create a histogram, but we would like to identify measures like the mean, median, and quartiles. What other type of graph could you create?
b) In order to examine the relationship between the variables First_Income and Second_Income, what type of graph should be created? What summary measures, if any, should be calculated?
c) In order to examine the relationship between the variables Location and Monthly_Payment, what type of graph should be created? What summary measures, if any, should be calculated?
Execute
Q2) Examine the data set in the worksheet Q2. This data set lists the 15 players on the 2015-2016 roster of the Los Angeles Lakers. The variables include the name, position, and salary for each player.
Create a side-by-side boxplot for Salary, broken out by Position. In other words, there should be 5 boxplots, one showing the distribution of salaries for those who play center, one showing the distribution of salaries for those who play point guard, and so on. Specifically, this should all be in a single graph.
Q3) Examine again the data in sheet Q1 (there is no sheet Q3.) We would like to know if the size on one’s family has an effect on their income. Keeping in mind which is the explanatory variable and which is the response variable, create a scatterplot of the variables First_Income and Family Size. Be sure to include the correlation coefficient.
Q4) Who has been most likely to access the internet over the past decade? Consider the survey data collected from 1,000 randomly selected internet users, given in Q4. Create pivot tables to answer each of the following questions, and be sure to highlight your answer:
a)
i. What proportion (percentage) of these internet users is currently employed?
ii. What is the average salary of the employed internet users in this sample?
b) What proportion (percentage) of these internet users is single with no formal education beyond the high
school?
c) What proportion (percentage) of these internet users are men under the age of 30?
Interpret
Q5) Interpret the following. What do you learn? What conclusions can you draw?
In May of 2008, a study was published examining CEO compensation (salary and bonus.) In last week’s homework, you viewed a boxplot of the distribution of compensation, as well as summary measures. Here is the same data, now broken out by company type (there were 10 different company types surveyed: from Basic Materials to Utilities). Side-by-side boxplots are on the next page. Again, what can you conclude?
Comp 08 (Basi ...
Churn in the Telecommunications Industry
Strategic Business Analysis Capstone Project Telecommunications Churn Management
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BUS 640 Become Exceptional--bus640.com
For more course tutorials visit
www.bus640.com
Week 1
Problem 1:
A generous university benefactor has agreed to donate a large amount of money for student scholarships. The money can be provided in one lump sum of $12 million in Year 0 (the current year), or in parts, in which $7 million can be provided at the end of Year 1, and another $7 million can be provided at the end of Year 2.
BUS 640 Remember Education--bus640.com
For more course tutorials visit
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Week 1
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A generous university benefactor has agreed to donate a large amount of money for student scholarships. The money can be provided in one lump sum of $12 million in Year 0 (the current year), or in parts, in which $7 million can be provided at the end of Year 1, and another $7 million can be provided at the end of Year 2.
BUS 640 Introduction Education--bus640.com
For more course tutorials visit
www.bus640.com
Week 1
Problem 1:
A generous university benefactor has agreed to donate a large amount of money for student scholarships. The money can be provided in one lump sum of $12 million
BUS 640 Inspiring Innovation -- bus640.com
For more course tutorials visit
www.bus640.com
Week 1
Problem 1:
A generous university benefactor has agreed to donate a large amount of money for student scholarships. The money can be provided in one lump sum of $12
BUS 640 Education Planning--bus640.com
For more course tutorials visit
www.bus640.com
Week 1
Problem 1:
A generous university benefactor has agreed to donate a large amount of money for student scholarships. The money can be provided in one lump sum of $12 million in Year 0 (the current year), or in parts, in which $7 million can be provided at the end of Year 1, and another $7 million can be provided at the end of Year 2.
BUS 640 HOMEWORK Achievement Education--bus640homework.com
FOR MORE CLASSES VISIT
www.bus640homework.com
Week 1
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A generous university benefactor has agreed to donate a large amount of money for student scholarships. The money can be provided in one lump sum of $12 million in Year 0 (the current year), or in parts, in which $7 million can be provided at the end of Year 1, and another $7 million can be provided at the end of Year 2.
Describe your answer for each item below
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- 4. Regression (Income as independent variable)
- 6. Regression (Household size as independent variable)
- 8. Prediction Predicted annual credit card charge for a three- person household with an annual income of $40,000. AMOUNT = 1305 + 33.1 ($40) + 356 (3) AMOUNT = 1305 + 1324 + 1068 AMOUNT = 3697
- 9. Independent variables that could be added to the model. Perhaps we could add some qualitative independent variables that could describe some characteristics of the consumers regarding their credit card charges. Now, bear in mind that the standard error of the estimate is somewhat large for this model, and that might be corrected by the introduction of some other variables that complete this model and that correct the problem of multicollinearity. Here are some of those possible variables: Average age of household. Independent variable that can give us additional refining to the model. Purchase preference: cash or credit (qualitative variable). Dummy variable that can add some more precision into the model if done properly, and it is not correlated with the other proposed since it is the only one to involve cultural habits; therefore, it has to do with the introduction of a qualitative variable. Male or Female and its subsequent percentage (qualitative variable). Data easy to collect, and one that can help us big time in refininf the model.
- 10. References Fundamentals of Business Statistics, Fifth Edition. Deniss J. Sweeney, Thomas A. Williams, David R. Anderson.