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

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