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Business statistics for competitive ...
~
Fraser, Cynthia.
Business statistics for competitive advantage with Excel 2016[electronic resource] :basics, model building, simulation and cases /
紀錄類型:
書目-電子資源 : Monograph/item
杜威分類號:
330.015195
書名/作者:
Business statistics for competitive advantage with Excel 2016 : basics, model building, simulation and cases // by Cynthia Fraser.
作者:
Fraser, Cynthia.
出版者:
Cham : : Springer International Publishing :, 2016.
面頁冊數:
xiv, 475 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:
Springer eBooks
標題:
Commercial statistics.
標題:
Mathematical statistics.
標題:
Decision making - Mathematical models.
標題:
Statistics.
標題:
Statistics for Business/Economics/Mathematical Finance/Insurance.
標題:
Management.
標題:
Market Research/Competitive Intelligence.
ISBN:
9783319321851
ISBN:
9783319321844
內容註:
Statistics for Decision Making and Competitive Advantage -- Describing your Data -- Hypothesis Tests, Confidence Intervals to Infer Population Characteristics and Differences -- Simulation to Infer Future Performance Levels Given Assumptions -- Simple Regression for Long Range Forecasts -- Consolidating Multiple Naive Forecasts with Monte Carlo -- Presenting Statistical Analysis Results to Management -- Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression -- Association between Two Categorical Variables: Contingency Analysis with Chi Square -- Building Multiple Regression Models -- Indicator Variables -- Model Building and Forecasting with Multicollinear Time Series -- Nonlinear Multiple Regression Models -- Nonlinear Explanatory Multiple Regression Models.
電子資源:
http://dx.doi.org/10.1007/978-3-319-32185-1
Business statistics for competitive advantage with Excel 2016[electronic resource] :basics, model building, simulation and cases /
Fraser, Cynthia.
Business statistics for competitive advantage with Excel 2016
basics, model building, simulation and cases /[electronic resource] :by Cynthia Fraser. - Cham :Springer International Publishing :2016. - xiv, 475 p. :ill. (some col.), digital ;24 cm.
Statistics for Decision Making and Competitive Advantage -- Describing your Data -- Hypothesis Tests, Confidence Intervals to Infer Population Characteristics and Differences -- Simulation to Infer Future Performance Levels Given Assumptions -- Simple Regression for Long Range Forecasts -- Consolidating Multiple Naive Forecasts with Monte Carlo -- Presenting Statistical Analysis Results to Management -- Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression -- Association between Two Categorical Variables: Contingency Analysis with Chi Square -- Building Multiple Regression Models -- Indicator Variables -- Model Building and Forecasting with Multicollinear Time Series -- Nonlinear Multiple Regression Models -- Nonlinear Explanatory Multiple Regression Models.
ISBN: 9783319321851
Standard No.: 10.1007/978-3-319-32185-1doiSubjects--Uniform Titles:
Microsoft Excel (Computer file)
Subjects--Topical Terms:
172370
Commercial statistics.
LC Class. No.: HF1017
Dewey Class. No.: 330.015195
Business statistics for competitive advantage with Excel 2016[electronic resource] :basics, model building, simulation and cases /
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Statistics for Decision Making and Competitive Advantage -- Describing your Data -- Hypothesis Tests, Confidence Intervals to Infer Population Characteristics and Differences -- Simulation to Infer Future Performance Levels Given Assumptions -- Simple Regression for Long Range Forecasts -- Consolidating Multiple Naive Forecasts with Monte Carlo -- Presenting Statistical Analysis Results to Management -- Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression -- Association between Two Categorical Variables: Contingency Analysis with Chi Square -- Building Multiple Regression Models -- Indicator Variables -- Model Building and Forecasting with Multicollinear Time Series -- Nonlinear Multiple Regression Models -- Nonlinear Explanatory Multiple Regression Models.
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