Be able todescribe a scatterplot in terms ofstrength, directionand form.142021Be able to find Zscores andknow when Z-Scores areappropriate touse.810Know what astructuralchange is andwhat effect ithas on datamodellingBe able to discussthe validity ofpredictions inregards tointerpolation andextrapolation36Difference betweenNumericalcontinuous,Numerical discrete,Categorical ordinaland Categorialnominal9715Be able to usetransformedequations topredict values16Know to set theaxes scales sothat the data iseffectivelydisplayed (usesmost of theavailable space)Be able to findthe lower andupper fencesto determine ifoutliers exist2419Be able tocompare box-plots in terms oftheir shape,centre andspread.Be able tofindpercentagesBe able tocorrectly identifysuitabletransformations inan attempt tolinearise the data.Interpret theslope of aregressionline.Interpret they-intercept ofa regressionline.22Be able to find ifthere is anassociation using atwo-way table byfirst converting topercentages thencomparingInterpret thecoefficient ofdetermination.25Plot the leastsquaresregressionline on ascatter plot.17Be able to testthe assumptionthat the data islinear using aresidual plot.Create afrequencytableBe able totransform the datausing the variousmethods toproduce a model.Be able to describea box-plot in termsof it’s shape, centreand spread. Knownot to use therange.112245Be able to create aBar Chart,Segmented BarChart, Histogram,Box-Plot or a Stem& leaf, scatter plotBe able toroundnumbersappropriately13Know when to usethe different datadisplays Bar Chart,Segmented BarChart, Histogram,Box-Plot, Stem &leaf, scatter plot111823Find theleastsquaresregressionline.Be able toname theexplanatoryand responsevariablesBe able todescribe a scatterplot in terms ofstrength, directionand form.142021Be able to find Zscores andknow when Z-Scores areappropriate touse.810Know what astructuralchange is andwhat effect ithas on datamodellingBe able to discussthe validity ofpredictions inregards tointerpolation andextrapolation36Difference betweenNumericalcontinuous,Numerical discrete,Categorical ordinaland Categorialnominal9715Be able to usetransformedequations topredict values16Know to set theaxes scales sothat the data iseffectivelydisplayed (usesmost of theavailable space)Be able to findthe lower andupper fencesto determine ifoutliers exist2419Be able tocompare box-plots in terms oftheir shape,centre andspread.Be able tofindpercentagesBe able tocorrectly identifysuitabletransformations inan attempt tolinearise the data.Interpret theslope of aregressionline.Interpret they-intercept ofa regressionline.22Be able to find ifthere is anassociation using atwo-way table byfirst converting topercentages thencomparingInterpret thecoefficient ofdetermination.25Plot the leastsquaresregressionline on ascatter plot.17Be able to testthe assumptionthat the data islinear using aresidual plot.Create afrequencytableBe able totransform the datausing the variousmethods toproduce a model.Be able to describea box-plot in termsof it’s shape, centreand spread. Knownot to use therange.112245Be able to create aBar Chart,Segmented BarChart, Histogram,Box-Plot or a Stem& leaf, scatter plotBe able toroundnumbersappropriately13Know when to usethe different datadisplays Bar Chart,Segmented BarChart, Histogram,Box-Plot, Stem &leaf, scatter plot111823Find theleastsquaresregressionline.Be able toname theexplanatoryand responsevariables

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