# Bus 308 statistics for managers week 1 assignment ashford latest

Score:Week 1.Measurement and Description - chapters 1 and 2                                                                                                                                                                       <1 point>1Measurement conclusions.  Data, plain numerically coded changeables, can be one of 4 razes -                                                  nominal, ordinal, interspace, or connection.  It is expressive to authenticate which raze a changeable is, as                                                 this impression the peel of decomposition we can do after a while the grounds.  For pattern, vivid statistics                                                  such as media can simply be done on interspace or connection raze grounds.                                                    Please schedule below each letter, the changeables in our grounds set that belong in each collection.                                                  NominalOrdinalIntervalRatio                                                                                                                                                                                                                                                                                                                                                                                                                 b.For each changeable that you did not ole connection, why did you constitute that determination?                                                                                                                                                                                                                                                                                                                                                  <1 point>2The pristine trudge in analyzing grounds sets is to discover some abstract vivid statistics for key changeables.                                                For remuneration, compa, age, toil rating, and service; discover the moderation, gauge derangement, and file for 3 collections: overall pattern, Females, and Males.                                           You can use either the Grounds Decomposition Vivid Statistics utensil or the Fx =average and =stdev functions.                                                   (the file must be base using the disagreement among the =max and =min functions after a while Fx) functions.                                                Note: Situate grounds to the fair, if you use Vivid statistics, situate that to the fair as polite.                                                   SalaryCompaAgePerf. Rat.Service                                                   OverallMean                                                         Standard Deviation                                                         Range                                                        FemaleMean                                                         Standard Deviation                                                         Range                                                        MaleMean                                                         Standard Deviation                                                         Range                                                                                                                <1 point>3What is the appearance for a:    Probability                                                  a.       Randomly clarified individual entity a manly in degree E?                                                    b.      Randomly clarified manly entity in degree E?                                                        Note disunite b is the identical as attached a manly, what is probabilty of entity in degree E?                                                 c.     Why are the results opposed?                                                                                                              <1 point>4For each collection (overall, womanlys, and manlys) discover:   OverallFemaleMale                                              a.The esteem that cuts off the top 1/3 remuneration in each collection.     Hint: can use these Fx functions                                          b.The z charges for each esteem:        Excel's standize function                                           c.The natural flexion appearance of deferred this charges:     1-normsdist function                                           d.What is the tentative appearance of entity at or deferred this remuneration esteem?                                                 e.The esteem that cuts off the top 1/3 compa in each collection.                                                   f.The z charges for each esteem:                                                      g.The natural flexion appearance of deferred this charges:                                                   h.What is the tentative appearance of entity at or deferred this compa esteem?                                                 i.How do you expound the kindred among the grounds sets?  What do they moderation environing our similar pay for similar toil scrutiny?                                                                                                                                                                                                                          <2 points>5.      What conclusions can you constitute environing the conclusion of manly and femanly pay similarity?  Are all of the results agreeing?                                                What is the disagreement among the sal and compa measures of pay?                                                                                                                                                                       Conclusions from looking at remuneration results:                                                                                                                                                                         Conclusions from looking at compa results:                                                                                                                                                                         Do twain remuneration measures exhibition the identical results?                                                                                                                                                                         Can we constitute any conclusions environing similar pay for similar toil yet?