SAS ASSIGNMENT:
1. Using SAS: Date Fields Creation
Create fields to identify the last file entry, first gift, and last gift.
Calculate the median months between the first and last gift.
2. Using SAS: Histogram Analysis
Generate histograms for the ENTRY_DATE field.
Provide an analysis explaining any unusual patterns observed in the histograms.
3. Using SAS: Additional Date Fields
Determine the number of individuals added to the file in 1998.
Identify the year with the lowest average LAST_GIFT_AMT.
Filter the data to find records where CLUSTER_CODE equals 9 and LAST_GIFT_DATE_YEAR equals 1997.
4. Using R: Date Fields Replication
Write R code to replicate the creation of the three date fields mentioned in task 1.
Produce output that shows the correct minimum, median, mean, and maximum values for these fields.
5. Using R: Gift Analysis
Develop R code to create the field LAST_GIFT_DATE_YEAR.
Calculate and display the mean LAST_GIFT_AMT by LAST_GIFT_YEAR.
6. Using SAS: Handling Missing Values for DONOR_AGE
Perform a data integrity check on DONOR_AGE.
Discuss whether to remove records with missing values and provide a rationale.
7. Using SAS: Missing Values Imputation for DONOR_AGE
Observe the current values and apply various imputation methods: mean, hot-deck, stochastic regression, and predictive mean matching (PMM).
Provide a detailed analysis and recommendation based on the imputation results.
8. Using SAS: Handling Missing Values for Categorical Variables
Conduct a data integrity check on WEALTH_RATING.
Apply mode and PMM imputation methods.
Analyze the outcomes and make recommendations.
9. Using SAS: Analysis of Extreme Distributions
Assess whether the distribution of LIFETIME_GIFT_AMOUNT is extreme.
Identify and apply the appropriate transformation to address skewness.
Use the BoxCox transformation method to discuss the optimal lambda and new skewness value.
10. Using SAS and R: Advanced Statistical Techniques
Calculate the median origination date in years and format as MM/DD/YYYY.
Implement kNN imputation in R and evaluate if the results alter any recommendations.
This assignment emphasizes the practical application of SAS and R programming in data management, specifically in handling dates, analyzing distributions, and imputing missing data.
