2011Plot the leastsquaresregressionline on ascatter plot.19Be able to find Zscores andknow when Z-Scores areappropriate touse.Be able totransform the datausing the variousmethods toproduce a model.Be able tocorrectly identifysuitabletransformations inan attempt tolinearise the data.15Be able to describea box-plot in termsof it’s shape, centreand spread. Knownot to use therange.Be able to usetransformedequations topredict values212Be able toroundnumbersappropriatelyBe able to find ifthere is anassociation using atwo-way table byfirst converting topercentages thencomparing86Interpret they-intercept ofa regressionline.149Interpret thecoefficient ofdetermination.153Know what astructuralchange is andwhat effect ithas on datamodellingCreate afrequencytableBe able to findthe lower andupper fencesto determine ifoutliers exist24Be able todescribe a scatterplot in terms ofstrength, directionand form.7102317Difference betweenNumericalcontinuous,Numerical discrete,Categorical ordinaland CategorialnominalBe able to testthe assumptionthat the data islinear using aresidual plot.22Know when to usethe different datadisplays Bar Chart,Segmented BarChart, Histogram,Box-Plot, Stem &leaf, scatter plotInterpret theslope of aregressionline.Be able to create aBar Chart,Segmented BarChart, Histogram,Box-Plot or a Stem& leaf, scatter plot121825Be able tocompare box-plots in terms oftheir shape,centre andspread.Be able tofindpercentagesKnow to set theaxes scales sothat the data iseffectivelydisplayed (usesmost of theavailable space)Be able to discussthe validity ofpredictions inregards tointerpolation andextrapolation16Find theleastsquaresregressionline.Be able toname theexplanatoryand responsevariables1342011Plot the leastsquaresregressionline on ascatter plot.19Be able to find Zscores andknow when Z-Scores areappropriate touse.Be able totransform the datausing the variousmethods toproduce a model.Be able tocorrectly identifysuitabletransformations inan attempt tolinearise the data.15Be able to describea box-plot in termsof it’s shape, centreand spread. Knownot to use therange.Be able to usetransformedequations topredict values212Be able toroundnumbersappropriatelyBe able to find ifthere is anassociation using atwo-way table byfirst converting topercentages thencomparing86Interpret they-intercept ofa regressionline.149Interpret thecoefficient ofdetermination.153Know what astructuralchange is andwhat effect ithas on datamodellingCreate afrequencytableBe able to findthe lower andupper fencesto determine ifoutliers exist24Be able todescribe a scatterplot in terms ofstrength, directionand form.7102317Difference betweenNumericalcontinuous,Numerical discrete,Categorical ordinaland CategorialnominalBe able to testthe assumptionthat the data islinear using aresidual plot.22Know when to usethe different datadisplays Bar Chart,Segmented BarChart, Histogram,Box-Plot, Stem &leaf, scatter plotInterpret theslope of aregressionline.Be able to create aBar Chart,Segmented BarChart, Histogram,Box-Plot or a Stem& leaf, scatter plot121825Be able tocompare box-plots in terms oftheir shape,centre andspread.Be able tofindpercentagesKnow to set theaxes scales sothat the data iseffectivelydisplayed (usesmost of theavailable space)Be able to discussthe validity ofpredictions inregards tointerpolation andextrapolation16Find theleastsquaresregressionline.Be able toname theexplanatoryand responsevariables134

Summary Book Bingo - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. 20
  2. 11
  3. Plot the least squares regression line on a scatter plot.
  4. 19
  5. Be able to find Z scores and know when Z-Scores are appropriate to use.
  6. Be able to transform the data using the various methods to produce a model.
  7. Be able to correctly identify suitable transformations in an attempt to linearise the data.
  8. 15
  9. Be able to describe a box-plot in terms of it’s shape, centre and spread. Know not to use the range.
  10. Be able to use transformed equations to predict values
  11. 21
  12. 2
  13. Be able to round numbers appropriately
  14. Be able to find if there is an association using a two-way table by first converting to percentages then comparing
  15. 8
  16. 6
  17. Interpret the y-intercept of a regression line.
  18. 14
  19. 9
  20. Interpret the coefficient of determination.
  21. 1
  22. 5
  23. 3
  24. Know what a structural change is and what effect it has on data modelling
  25. Create a frequency table
  26. Be able to find the lower and upper fences to determine if outliers exist
  27. 24
  28. Be able to describe a scatter plot in terms of strength, direction and form.
  29. 7
  30. 10
  31. 23
  32. 17
  33. Difference between Numerical continuous, Numerical discrete, Categorical ordinal and Categorial nominal
  34. Be able to test the assumption that the data is linear using a residual plot.
  35. 22
  36. Know when to use the different data displays Bar Chart, Segmented Bar Chart, Histogram, Box-Plot, Stem & leaf, scatter plot
  37. Interpret the slope of a regression line.
  38. Be able to create a Bar Chart, Segmented Bar Chart, Histogram, Box-Plot or a Stem & leaf, scatter plot
  39. 12
  40. 18
  41. 25
  42. Be able to compare box-plots in terms of their shape, centre and spread.
  43. Be able to find percentages
  44. Know to set the axes scales so that the data is effectively displayed (uses most of the available space)
  45. Be able to discuss the validity of predictions in regards to interpolation and extrapolation
  46. 16
  47. Find the least squares regression line.
  48. Be able to name the explanatory and response variables
  49. 13
  50. 4