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by R. Lyman Ott and Michael Longnecker

Edition: 5TH 01Copyright: 2001

Publisher: Duxbury Press

Published: 2001

International: No

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Statistics is a thought process. In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach: 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.

**Ott, R. Lyman : Global Development Management, Hoechst **

PART I: INTRODUCTION.

1. What Is Statistics?.

PART II: COLLECTING THE DATA.

2. Using Surveys And Scientific Studies To Collect Data.

PART III: SUMMARIZING DATA.

3. Data Description.

PART IV: TOOLS AND CONCEPTS.

4. Probability and Probability Distributions.

PART V: ANALYZING DATA: CENTRAL VALUES, VARIANCES, AND PROPORTIONS.

5. Inferences on A Population Central Value.

6. Comparing Two Population Central Values.

7. Inferences about Population Variances.

8. Inferences About Population Central Values.

9. Multiple Comparisons.

10. Categorical Data.

PART VI: ANALYZING DATA: REGRESSION METHODS, MODEL BUILDING.

11. Simple Linear Regression and Correlation.

12. Inferences Related To Linear Regression and Correlation.

13. Multiple Regression and The General Linear Model.

14. Building Regression Models with Diagnostics.

PART VII: ANALYZING DATA: DESIGN OF EXPERIMENTS AND ANOVA.

15. Design Concepts for Experiments and Studies.

16. Analysis of Variance for Standard Designs.

17. Analysis of Covariance.

18. Analysis of Variance for Some Unbalanced Designs.

19. Analysis of Variance for Some Fixed Effects, Random Effects, And Mixed Effects Models.

20. Split-Plot Designs and Experiments with Repeated Measures.

PART VIII: COMMUNICATING AND DOCUMENTING THE RESULTS OF A STUDY OR EXPERIMENT.

21. Communicating and Documenting the Results of A Study or Experiment.

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Summary

Statistics is a thought process. In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach: 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.

Author Bio

**Ott, R. Lyman : Global Development Management, Hoechst **

Table of Contents

PART I: INTRODUCTION.

1. What Is Statistics?.

PART II: COLLECTING THE DATA.

2. Using Surveys And Scientific Studies To Collect Data.

PART III: SUMMARIZING DATA.

3. Data Description.

PART IV: TOOLS AND CONCEPTS.

4. Probability and Probability Distributions.

PART V: ANALYZING DATA: CENTRAL VALUES, VARIANCES, AND PROPORTIONS.

5. Inferences on A Population Central Value.

6. Comparing Two Population Central Values.

7. Inferences about Population Variances.

8. Inferences About Population Central Values.

9. Multiple Comparisons.

10. Categorical Data.

PART VI: ANALYZING DATA: REGRESSION METHODS, MODEL BUILDING.

11. Simple Linear Regression and Correlation.

12. Inferences Related To Linear Regression and Correlation.

13. Multiple Regression and The General Linear Model.

14. Building Regression Models with Diagnostics.

PART VII: ANALYZING DATA: DESIGN OF EXPERIMENTS AND ANOVA.

15. Design Concepts for Experiments and Studies.

16. Analysis of Variance for Standard Designs.

17. Analysis of Covariance.

18. Analysis of Variance for Some Unbalanced Designs.

19. Analysis of Variance for Some Fixed Effects, Random Effects, And Mixed Effects Models.

20. Split-Plot Designs and Experiments with Repeated Measures.

PART VIII: COMMUNICATING AND DOCUMENTING THE RESULTS OF A STUDY OR EXPERIMENT.

21. Communicating and Documenting the Results of A Study or Experiment.

Publisher Info

Publisher: Duxbury Press

Published: 2001

International: No

Published: 2001

International: No

**Ott, R. Lyman : Global Development Management, Hoechst **

PART I: INTRODUCTION.

1. What Is Statistics?.

PART II: COLLECTING THE DATA.

2. Using Surveys And Scientific Studies To Collect Data.

PART III: SUMMARIZING DATA.

3. Data Description.

PART IV: TOOLS AND CONCEPTS.

4. Probability and Probability Distributions.

PART V: ANALYZING DATA: CENTRAL VALUES, VARIANCES, AND PROPORTIONS.

5. Inferences on A Population Central Value.

6. Comparing Two Population Central Values.

7. Inferences about Population Variances.

8. Inferences About Population Central Values.

9. Multiple Comparisons.

10. Categorical Data.

PART VI: ANALYZING DATA: REGRESSION METHODS, MODEL BUILDING.

11. Simple Linear Regression and Correlation.

12. Inferences Related To Linear Regression and Correlation.

13. Multiple Regression and The General Linear Model.

14. Building Regression Models with Diagnostics.

PART VII: ANALYZING DATA: DESIGN OF EXPERIMENTS AND ANOVA.

15. Design Concepts for Experiments and Studies.

16. Analysis of Variance for Standard Designs.

17. Analysis of Covariance.

18. Analysis of Variance for Some Unbalanced Designs.

19. Analysis of Variance for Some Fixed Effects, Random Effects, And Mixed Effects Models.

20. Split-Plot Designs and Experiments with Repeated Measures.

PART VIII: COMMUNICATING AND DOCUMENTING THE RESULTS OF A STUDY OR EXPERIMENT.

21. Communicating and Documenting the Results of A Study or Experiment.