Statistical Analysis
Statistics with Ms Excel
Simple Statistics with Excel and Minitab
Elementary Concepts in Statistics
Multiple Regression
ANOVA
Elementary Concepts in Statistics
Overview of Elementary Concepts in Statistics. In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of one's general understanding of the "quantitative nature" of reality (Nisbett, et al., 1987). Because of space limitations, we will focus mostly on the functional aspects of the concepts discussed and the presentation will be very short. Further information on each of those concepts can be found in the Introductory Overview and Examples sections of this manual and in statistical textbooks. Recommended introductory textbooks are:
Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of elementary theory and assumptions of statistics, see the classic books by Hays (1988), and Kendall and Stuart (1979).
• What are variables?
• Correlational vs. experimental research
• Dependent vs. independent variables • Measurement scales
• Relations between variables
• Why relations between variables are important
• Two basic features of every relation between variables
• What is "statistical significance" (p-value)
• How to determine that a result is "really" significant
• Statistical significance and the number of analyses performed • Strength vs. reliability of a
• Why significance of a relation between variables depends on the size of the sample
• Example: "Baby boys to baby girls ratio"
• Why small relations can be proven significant only in large samples
• Can "no relation" be a significant result?
• How to measure the magnitude (strength) of
relations
Simple Statistics with Excel and Minitab
Elementary Concepts in Statistics
Multiple Regression
ANOVA
Elementary Concepts in Statistics
Overview of Elementary Concepts in Statistics. In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of one's general understanding of the "quantitative nature" of reality (Nisbett, et al., 1987). Because of space limitations, we will focus mostly on the functional aspects of the concepts discussed and the presentation will be very short. Further information on each of those concepts can be found in the Introductory Overview and Examples sections of this manual and in statistical textbooks. Recommended introductory textbooks are:
Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of elementary theory and assumptions of statistics, see the classic books by Hays (1988), and Kendall and Stuart (1979).
• What are variables?
• Correlational vs. experimental research
• Dependent vs. independent variables • Measurement scales
• Relations between variables
• Why relations between variables are important
• Two basic features of every relation between variables
• What is "statistical significance" (p-value)
• How to determine that a result is "really" significant
• Statistical significance and the number of analyses performed • Strength vs. reliability of a
• Why significance of a relation between variables depends on the size of the sample
• Example: "Baby boys to baby girls ratio"
• Why small relations can be proven significant only in large samples
• Can "no relation" be a significant result?
• How to measure the magnitude (strength) of
relations