Grader Instructions Excel 2019 Projects Chapter 11 Mill 2

Grader Instructionsexcel 2019 Projectexp19 Excel Ch11 Ml2 Game Stud

Copy the file named Exp19_Excel_Ch11_ML2_GameStudio.xlsx, electronically format data, perform text and database functions, create an advanced filter, and insert formulas, then save and submit the file.

Paper For Above instruction

Introduction

Microsoft Excel is a versatile tool that facilitates data management, analysis, and presentation through various functions and features. In the context of managing a game development company's data across multiple locations, Excel's capabilities can be leveraged to streamline workflows, perform complex calculations, and generate insightful reports. This paper details a step-by-step process for utilizing Excel 2019 to format, analyze, and filter data related to a hypothetical gaming studio operating in Portland, Seattle, and Salt Lake City, illustrating practical applications of text functions, database functions, advanced filters, and formula management.

Data Preparation and Formatting

The initial phase involves downloading and opening the provided Excel file named Exp19_Excel_Ch11_ML2_GameStudio.xlsx. The dataset encompasses employee records with fields such as names, addresses, job titles, departments, and salaries. To enhance data clarity, several formatting procedures are implemented.

First, the task requires concatenating the employee's first, middle, and last names into a single cell. Using the TEXTJOIN function, the range B2:D2 is combined with spaces as delimiters while ignoring blank cells. This consolidates the full name information into a single column, simplifying readability. For example, in cell E2, the formula is:=TEXTJOIN(" ", TRUE, B2:D2). This formula is then copied down to all relevant rows (E3:E49), ensuring consistency across records.

Text-to-Columns and Data Separation

Next, focus shifts to parsing the jobs and department data stored in column F. The data is separated by commas, such as “Development, Programmer,” requiring transformation into two distinct columns. The Text to Columns feature is utilized on the range F2:F49, selecting comma as the delimiter. This effectively splits the information into separate department and job title columns, facilitating targeted analysis later on.

Extracting City and State Data

The dataset also includes addresses with city, state, and ZIP code information. To isolate city names, nesting the LEN function within the LEFT function is employed. Specifically, in cell I2, the formula=LEFT(H2, LEN(H2) - 4) is used, where H2 contains the address string. This computes the total length of the address, subtracts four characters to exclude the state abbreviation, space, and comma, leaving only the city name. The function is copied down to I49. For state abbreviations, the RIGHT function extracts the last two characters, converted to uppercase with the UPPER function, e.g.,=UPPER(RIGHT(H2, 2)), and similarly copied through the dataset.

Filtering Data with Criteria and Advanced Filter

A criteria range is established by copying the header row (A1:K1) to cell A51 and then entering specific filters in row 52—such as Department: "Programming" and City: "Salt Lake City." Using this criteria range, an advanced filter is executed to extract matching records into a designated output range starting at A54. Columns B, C, D, and H are hidden in the original dataset for presentation clarity. Additionally, column F's width is adjusted to 21 units for better visibility.

Database Functions for Data Analysis

On the Information sheet, database functions provide valuable insights into employee salaries. The DSUM function calculates total salaries for programmers in Salt Lake City,e.g.,=DSUM(A$1:K$49, "Salary", criteria_range). Similarly, DAVG computes the average salary, DMAX retrieves the highest salary, DMIN the lowest, and DCOUNT the number of programmers matching the criteria. These functions leverage the dataset range and criteria range to deliver accurate summaries.

Formatting and Concatenating Text

Resulting values are formatted appropriately; salaries in accounting number format and counts with comma style. The CONCAT function creates descriptive labels; for example, concatenating the department and city (from cells F52 and I52) forms phrases like “Programming in Salt Lake City.”

Lookup Functions: MATCH and INDEX

To retrieve employee details by ID, the MATCH function identifies the row position of an ID, e.g.,=MATCH(E2, A2:A49, 0). The INDEX function then uses this position to extract data such as last names, addresses, or salaries from the dataset. Changing the ID in E2 to test the lookup functions verifies their accuracy. Additional formulas retrieve data at different columns, adjusting the column number parameter as needed.

Formula Display and Final Adjustments

Formulas referencing other formulas are displayed using the cell's formula view. For example, in H2 and beyond, functions such as =FORMULATEXT(B2) reveal the content of referenced cells—helpful for auditing or instructional purposes. The width of column H is increased to 57 for clarity.

Final Page Setup

A consistent footer is added to all sheets following the specified format: Left alignment with the student's name, center with the sheet name code, and right alignment with the file name. This ensures professional presentation and easy identification of the document.

Conclusion

Using Excel 2019, these techniques demonstrate effective data management—combining text functions, database calculations, advanced filtering, and lookup formulas—tailored specifically to a multi-location game studio's employee database. Mastery of these functions enhances analytical capabilities, supports efficient reporting, and improves data accuracy, vital for decision-making in a dynamic business environment.

References

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