Lab 2.4-2.5

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Lab 2.4 and Lab 2.5-Syeda Jaweria Hassan  Screenshot 2-4MA:  Screenshot 2-4MB: Lab 2-4 Objective Questions (LO 2-3) OQ1.What is the maximum loan amount that was approved for borrowers from PA?

Ans: $35,000 is the maximum loan amount that was approved for borrowers from PA. OQ2.What is the average interest rate assigned to a loan to an approved borrower from PA? Ans: 0.14 is the average interest rate assigned to a loan to an approved borrower from PA. OQ3.What is the average annual income of an approved borrower from PA? Ans:$70,046.69 is the average annual income of an approved borrower from PA. Lab 2-4 Analysis Questions (LO 2-3) AQ1.Compare the loan amounts to the validation given by LendingClub for borrowers from PA: Funded loans: $123,262.53 Number of approved loans: 8,427 Do the numbers in your analysis match the numbers provided by LendingClub? What explains the discrepancy, if any? Ans There's a difference in the funded loan amount. In the Power BI analysis, the value is 123262525, while the given value for funded loans is $123,262.53. This difference is because of a rounding error. AQ2. Does the Numerical Count provide a more useful/accurate value for validating your data? Why or why not do you think that is the case? Ans: Yes, Numerical Count is a useful and accurate method for validating the data. It works well because there are no missing pieces of information in the data, and I haven't found any repeated/duplicate values. So, using Numerical Count is a dependable way to make sure the data is accurate. AQ3. Compare and contrast: Why do Power Query and Tableau Desktop return different values for their summary statistics? Ans: Power Query and Tableau Desktop might give different summary numbers because they handle data in different ways. Power Query is more about getting data ready for analysis, while Tableau is mostly for making visuals. Power Query can change the data as it gets it, and Tableau calculates stuff on the spot when we are looking at it. The default ways they add up numbers or deal with missing info could be different, and that's why we see different results in their summaries. It's like they speak slightly different data languages, so understanding how each one works helps make sense of the numbers they show. AQ4. Compare and contrast: What are some of the summary statistics measures that are unique to Power Query? To Tableau Desktop? Ans: Power Query and Tableau Desktop are both powerful tools for data analysis, but they have distinct approaches to summary statistics. In Power Query, some unique summary statistics measures include "Count Values," which counts the number of unique values in a column, and "Error Count," which counts the number of errors in a column. On the other hand, Tableau Desktop

provides a wide range of summary statistics measures, including Sum, Average, Minimum, Maximum, Median, Standard Deviation, First Quartile, and Third Quartile. These measures allow users to gain insights into the distribution and characteristics of their data. While Power Query focuses on data transformation and preparation, Tableau Desktop is known for its powerful data visualization capabilities and interactive dashboards. Power Query's summary statistics measures:  Count Values  Error Count Tableau Desktop's summary statistics measures:  Sum  Average  Minimum  Maximum  Median  Standard Deviation  First Quartile  Third Quartile These tools serve different purposes, with Power Query being more focused on data preparation and Tableau Desktop excelling in data visualization and analysis. LAB 2.5:  Screenshot 2-5 MA  Screenshot 2-5MB

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Excel Workbook link for Lab 2.5 for clearer view: Lab 2.5.xlsx Objective Questions OQ1.How many schools report average SAT scores? Ans 1,305 schools report average SAT scores. OQ2.What is the average completion rate (C150 4) of all the schools? Ans: 0.477957437 is the average completion rate (C150 4) of all the schools. OQ3.How many schools report data to the U.S. Department of Education? Ans:7,703 schools report data to the U.S. Department of Education. Analysis Questions: AQ1. In the checksums, you validated that the average SAT score for all of the records is 1,059.07. When we work with the data more rigorously, several tests will require us to transform NULL or blank values. If you were to transform the NULL SAT values into 0, what would happen to the average (would it stay the same, decrease, or increase)? Ans: The average SAT score for all records is 1,059.07. If you were to transform the NULL SAT values into 0, the average would decrease. This is because including more 0 values in the calculation would lower the overall average score.This is because the NULL values are not included in the calculation of the average, but the 0 values would be included. Therefore, the average would be lower than the original average of 1,059.07.

AQ2. How would that change to the average affect the way you would interpret the data? Ans: If the NULL SAT values were transformed into 0, it would affect the way we interpret the data because it would artificially lower the average SAT score. This could lead to incorrect conclusions as it would no longer accurately represent the average performance of the students, as the 0 values would skew the results. AQ3.What would happen if we excluded all schools that don’t report an average SAT score? Ans: Firstly, If all schools that don't report an average SAT score are excluded, the average SAT score for the remaining schools would likely increase. This is because schools with lower average scores (or no reported scores) would no longer be pulling down the overall average. Secondly, if we excluded all schools that don’t report an average SAT score, we would be limiting our analysis to only those schools that report this data. This could lead to a biased sample and may not be representative of all schools. Additionally, we would lose valuable information about the schools that do not report this data.

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