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CategoryProgramming
SubjectR | R Studio
DifficultyCollege
StatusSolved
More InfoAssignment Statistics
65091

Short Assignment Requirements

The object of the project is to forecast IIT alumni donations to the annual giving campaign in Fall of 2014, using data from past donation history, demographics and information about the ask strings in mailed solicitations.You may build a forecasting model using the dataset in the CSV file donations on Blackboard. The dataset contains records of the donations returned by each alumnus during the period from September 2014 to January 2015. Donations are categorized by which campaign the donor responded to (Mail, Phone or website).You are to provide separate equations to predict both the probability of donation (discrete choice) and the amount of each donation given based on the individual donation history and information provided about the ask amounts sent to that donor.I will apply your formulas to a holdout sample randomly drawn from the original dataset (the data you are given will not include any of these individual).Donation Probabilities will be evaluated based on a likelihood model. I will apply your formula to the holdout data to get expected donation probabilities. The likelihood for each donor is your probability (p) if they donated and 1-p if they did not. I will multiply the likelihoods across the holdout sample to get a score. Highest likelihood wins. The predictions for contribution amounts will be scored based on Mean Absolute Deviation. The team with the lowest MAD, mean absolute deviation, will win.You will want to construct your own holdout samples to do model selection for this assignment,

Assignment Description

BUS 473

Forecasting Contest – Alumni Donations

 

Due November 8

 

The object of the project is to forecast IIT alumni donations to the annual giving campaign in Fall of 2014, using data from past donation history, demographics and information about the ask strings in mailed solicitations.

 

You may build a forecasting model using the dataset in the CSV file donations on Blackboard. The dataset contains records of the donations returned by each alumnus during the period from September 2014 to January 2015. Donations are categorized by which campaign the donor responded to (Mail, Phone or website).

 

You are to provide separate equations to predict both the probability of donation (discrete choice) and the amount of each donation given based on the individual donation history and information provided about the ask amounts sent to that donor.

 

I will apply your formulas to a holdout sample randomly drawn from the original dataset (the data you are given will not include any of these individual).

 

Donation Probabilities will be evaluated based on a likelihood model. I will apply your formula to the holdout data to get expected donation probabilities. The likelihood for each donor is your probability (p) if they donated and 1-p if they did not. I will multiply the likelihoods across the holdout sample to get a score. Highest likelihood wins. The predictions for contribution amounts will be scored based on Mean Absolute Deviation. The team with the lowest MAD, mean absolute deviation, will win.

 

You will want to construct your own holdout samples to do model selection for this assignment,

 

Teams will be ranked in order of combined scores, and graded accordingly.

 

Means for the dataset donations are below

 

       

Variable

Mean

Std Dev

Minimum

Maximum

N

ID

Mail

Gifts

CnGf_1_Amount

Left

Middle

Right

Largest

Last

LGDate

FY13

FY14

242461.86

0.2090341

0.3503123

38.0182749

284.9471408

195.0744834

108.1571360

189.9992167

84.0670399

19561.64

62.8279289

63.9850264

403006.10

0.4067165

0.4771824

74.9142395

311.7647845

203.6868867

104.1158000

3017.95

85.9510368

246.0710357

87.7286739

87.7040136

2277.00

0

0

0

50.0000000

25.0000000

10.0000000

1.0000000

0.7700000

18372.00

0

0

1040365.00

1.0000000

1.0000000

700.0000000

2500.00

1500.00

500.0000000

137004.00

800.0000000

19874.00

800.0000000

500.0000000

2081

2081

2081

2081

2081

2081

2081

2081

2081

2081

2081

2081

 

Note: This data is confidential to the university. Please do not share it with anyone outside the class and remove from your storage when the assignment is completed.

Assignment Description

Key for Donations Database

The Database contains response data from a mailing to 2081 IIT alumni.

Each alumnus received a mailed request for a donation that included specific suggested donations amounts, listed as an “ask string” in order, high, medium and low (Left, Middle, Right in the Dataset). For each recipient the ask amounts were customized based on the largest previous gift from that donor. In addition recipients were randomly divided into four treatment groups labeled A,B,C, D and the formula for computing the ask amount varied by treatment (A most aggressive – largest ask amounts – D lowest ask amounts). The purpose of the study was to determine if changing the ask amounts affected either the probability or amount of donations. You job is to provide the  models to predict whether a recipient made a donation and how much they gave based on their previous history, demographic information and ask amounts/treatment. Remember the first 4 fields are not usable as predictors in your model. They are the dependent variables you want to predict (plus the unique id number)

ID – unique id no for recipient

Mail – dummy indicated whether a mail donation was received

Gifts – dummy indicating whether any donation was received (includes Phonathon pledges)

CnGf_1_Amount – total amount of gifts, if any

TestGroup1 – treatment assignment

Gender (Male/Female)

St – State of Residence

Type- Graduate or Undergraduate degree

Class – year degree was awarded

Left – largest ask amount in solicitation

Middle – middle ask amount

Right- lowest ask amount

Largest – amount of largest previous gift (5 years)

Last - amount of most recent gift

LGDate – Last Gift Date – when most recent donation was recieved

FY13 amount donated in 2013

FY14 amount donated in 2014

Calculation (Random variable to separate training and holdout samples – should be 1 for training sample)

 

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