1. + Learning Intention 10 To understand and apply our understanding of spreadsheet software functions and techniques for efficiently and effectively manipulating and validating data.

2. + Spreadsheet functions The most basic capabilities: arithmetic (+ - * / ) Summary statistics: SUM, AVERAGE, standard deviation, MAX, MIN Data access and categorisation: VLOOKUP, HLOOKUP Data reorganisation: Pivot tables, sorting, grouping Data formatting: conditional formatting, charts Decision making: IF, AND, OR, CASE

3. + Efficiency A measure of how little time, cost and/or effort is applied in order to achieve intended results. Measures of an efficient solution include the: speed of processing functionality of the solution ease of use of the solution cost of information file manipulation

4. + Effectiveness A measure of how well something works, such as a solution, a file and information management strategy and a network, that is, the extent to which it achieves its intended results.

5. + Measures of an effective solution include: Completeness Readability attractiveness clarity accuracy accessibility timeliness communication of message relevance and usability

6. + Measures of an effective file and information management strategy include: integrity of data security ease of retrieval currency of files.

7. + Effective and efficient manipulation (processing) techniques include: using different worksheets to separate key information from setup figures and bulk data sets using colour coding to highlight types of content (e.g. blue cells can be typed in, yellow cells contain key output items) using cell formatting such as text size, colour and styles to highlight main output using cell and range naming to make formulas more readable and avoid fill- down errors when referring to absolute cell addresses in copied formulae using cell protection to prevent damage to formulae using macros to automate tasks for unskilled users (e.g. jumping to another sheet, or inserting a new row) using the most efficient function for the job, e.g. ABS(x) is more efficient than SQRT(x*x) using SUM to calculate a total of many cells is much better than listing every cell with "+" between them using CASE instead of many nested IF statements

8. + using cell and range naming to make formulas more readable and avoid fill-down errors when referring to absolute cell addresses in copied formulae Open up the spreadsheet from Think About IT 1-19 (ice cream favourites Name the cell with total for boys Total_Boys Refer to named cell in formula Repeat for girls

9. + Using cell protection to prevent damage to formulae All cells are protected by default To unprotect cells Select cells to be unprotected Right click>>Format Cells>>Protection Deselect Locked To Protect worksheet Click Tools>>Protection>>Protect Sheet Click OK

10. + using different worksheets to separate key information from setup figures and bulk data sets Download Preparation SAC 1 data Go to www.bom.gov.auwww.bom.gov.au Click climate and past weather Click weather station data Change data about to Temperature Enter station number 086071 Click get data Click all years of data Open.csv Change worksheet name to ‘data’ Save as MelbourneWeather.xlsx Add new worksheet, rename (this is where your summary data/graphs would go)

11. + Validation ensures the raw data are valid - in other words the data is within reasonable limits and is of the right type. Typical validation checks for: the correct range (e.g. a 27 year old person enrolling in kindergarten in not reasonable). Also can check that a value exists in a predefined list of options (e.g. states and territories of Australia) the correct data type (e.g. that a number has been entered for an age, that text has been entered for a name, that an account number fits a fixed format, that an email address has one and only one @ and one or more dots. the data has been entered at all - an existence check. Some data (e.g. a name) may be compulsory, other data (e.g. a fax number) may not. Validation rules can be applied to reject input that lacks key data.

12. + Validating our Melbourne Weather Data (Preparation SAC 1 Q4 – part 1) Range: Product code = IDCJAC0010 Station number = 86071 Months between 1 and 12 Days between 1 and 31 Years between 1855 and 2015 Temperatures between -88 and 70 Quality only = Y

13. + Validating our Melbourne Weather Data (Preparation SAC 1 Q4 – part 1) Data type: Months are numbers (=isnumber()) Days are numbers Years are numbers Temperatures are numbers Existence Checking For each field ISBLANK()

14. + Validating our Melbourne Weather Data (Preparation SAC 1 Q4 – part 2) The graphical representations also need to be validated. How can we validate the graphs created for the Preparation SAC 1? Range checking? Existence checking? Data validation (max figures, etc)