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--  MS EXCEL CASE STUDY
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Problem

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Procedure

- Data Preparation
- Calculation of crop productivity, convert the data of "Total land holding", "Soil depth " amenable to analysis
- Summarize the data with suitable statistical measures and frequency distribution
- Regression Analysis
- View data using database features
- Represent the data through appropriate graphs and tables
   
 
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PROCEDURE

Select MS EXCEL software since it has all the analytical functions required to carryout the analysis. EXCEl is an integrated environment with various features. Some of them are:
  • Allows to perform all types of arithmetic calculations
  • Allows to perform many types of Statistical Analysis from simple to advanced.
  • Allows to represents data in various graphical forms and hold graphical objects like pictures and images
  • Allows presentation of data in various Tabular formats and view data systematically
  • Allows to display data through Thematic Maps
  • Allows to automate applications using Macros
  • Allows to add new data analytical procedures using Visual Basic code
Excel like all other applications has tool bars, short cut Menus, templates, Wizards and online help.
  • Windows Environment: Excel like all other all other applications has tools bars, Shortcut Menus, Templates, Wizards and online help.
  • Workbooks: An Excel document is referred to as workbook. There are 256 worksheets in each workbook. Each worksheet contains 65,536 (256X256) rows and 256 (AA, BB,..BA,BB, ….IV) columns. Each single cell in a worksheet can accommodate about 32,000 characters.
  • Formula bar: Below the tool bar (i.e. Standard and Formatting), there is a Formula Bar.
  • Cell Address: On the left hand side of Formula Bar is the address Box. The address of the cell which is active is shown in the box. Ex. A1 refers the cell in first column and first row.

Hints

  1. Data Preparation
  2. Calculation of crop productivity, convert the data of "Total land holding", "Soil depth " amenable to analysis
  3. Summarize the data with suitable statistical measures and frequency distribution
  4. Regression Analysis
  5. View data using database features
  6. Represent the data through appropriate graphs and tables
  7. Prepare the report based on the results