GCSE Data Handling Coursework 1 Examining the Data examine carefully the data you are given it’s important to get a feel for the raw data before you use calculations & charts explain a context for the coursework – why might it be important to study this data? What could the data tell you? write a couple of sentences about what each line of the data represents can you make any observations or identify trends. Do any interesting questions emerge? is any data missing – if so what do you propose to do about them? Omit and/or use predictions from trends?

GCSE Data Handling Coursework 2 Further Analysis using Excel (or any suitable spreadsheet) analyse the raw data. Use totals (the count function). This can tell us quite a lot about the data and is useful for looking at strata (the gender split, age profile etc) calculate obvious averages (mean, median, mode) summarise totals and means in a table you should now have a reasonable ‘feel’ for the data and in a better position to sample think about an initial hypothesis – is it likely to lead to another one?

GCSE Data Handling Coursework 3 Questions raised are there any limitations of the data – any unseen factors? can you distinguish what factors cause others? are there any sources of bias? are we comparing like with like?

GCSE Data Handling Coursework 4 Sampling explain why sampling is necessary –samples reduce the number of calculations made –the amount of data is easier to deal with Random Sampling (Simple) explain why use random sampling order the sample according to the random number select the first n amount of records for the sample (n ≥ 30)

GCSE Data Handling Coursework 5 Random Stratified Sampling explain why a stratified sample is a fairer representation of the population –keep the same ratio of the main groups –it is a way of avoiding bias use random sampling to pick the required number of records from each group draw boxplots of the sample and of the population, compare to see if the sample is representative –are the means and range similar? –are the Upper and Lower Quartiles similar?

GCSE Data Handling Coursework 6 Missing Data 1.missing data can be ignored – but this is unwise 2.use mean values 3.select a record that is similar and use its values for the missing values 4.correlation is a thorough method to use : –but only if correlation exists –plot a scatter graph and use the line of best fit (trend line) to estimate missing values

GCSE Data Handling Coursework 7 Hypotheses at intermediate – 2 hypotheses are required, at higher level – 3 the initial hypothesis shapes the bulk of the coursework –this needs to be stated (not a question) plan what steps you are going to take to prove the hypothesis use diagrams, charts and statistical calculations to prove (or even disprove) your hypothesis (see next slide) write up your results and findings with reference to these results & diagrams (which must be included) the 2 nd (and 3 rd ) hypothesis must flow from the preceding one

GCSE Data Handling Coursework 8 Compare distributions of groups Eg males vs females, 2004 results vs 2005 etc Comment on the shapes of the distributions, diagrams, and calculations in relation to the hypothesis. DiagramsCalculations Scatter GraphsMean Bar ChartsMode Comparative Bar ChartsMedian Stem & Leaf diagramsRange Box PlotsInterquartile Range Pie Charts Histograms

GCSE Data Handling Coursework 9 Scatter Diagrams Excel will –draw the line of best fit (linear tend line) –display the equation of the line (y=mx+c) –display R 2 – this tells us how strong the correlation is [R 2 = 1 is perfect correlation, R 2 = 0 no correlation, if R 2 ≥ 0.6 then use it to find missing values]

GCSE Data Handling Coursework 10 Extras argue your case in terms of results but remember that one set of statistics doesn’t necessarily show the whole picture back up your findings with references to articles in the media or on the internet conclude with reference to the hypotheses but try and use some real life situations and bring in factors that are not analysed