{"id":89,"date":"2025-09-20T01:58:46","date_gmt":"2025-09-20T01:58:46","guid":{"rendered":"https:\/\/itisallmath.com\/statistics\/"},"modified":"2025-12-31T14:54:56","modified_gmt":"2025-12-31T14:54:56","slug":"statistics","status":"publish","type":"page","link":"https:\/\/itisallmath.com\/es\/mathematics\/statistics\/","title":{"rendered":"Statistics"},"content":{"rendered":"
<\/div><\/div>\n\n\n\n
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What are you going to learn?<\/h1><\/div>\n\n\n\n

Recognize important differences between descriptive and inferential statistics; distinguish between different types of variables and data; summarize, organize, tabulate and graph statistical data; read and understand statistical data present in various forms of the media; find and analyze measures of center and variation for quantitative data. Apply normal distributions in solving real-world problems involving percentages and percentiles; understand the importance of sampling distributions and the Central Limit Theorem. Determine if a correlation exists between two variables and interpret the strength of the correlation; use regression equations in order to predict the value of one variable given the value of another. Apply the basic concepts of probability theory.<\/p>\n\n\n\n

Content<\/h2><\/div>\n<\/div><\/div>\n\n\n\n
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Chapter 1. Statistics and the Scientific Method<\/strong><\/h3><\/div>\n\n\n\n
    \n
  • Introduction<\/li>\n\n\n\n
  • Why to Study Statistics?<\/li>\n<\/ul>\n<\/div>\n\n\n\n
    <\/div>\n<\/div><\/div>\n\n\n\n
    \n
    \n

    Chapter 2. Data Collection<\/strong><\/strong><\/h3><\/div>\n\n\n\n
      \n
    • Introduction<\/li>\n\n\n\n
    • Observational Studies, Sampling Design<\/li>\n\n\n\n
    • Experimental Studies, Design of Experiments<\/li>\n\n\n\n
    • Elements of Statistics<\/li>\n<\/ul>\n<\/div>\n\n\n\n
      <\/div>\n<\/div><\/div>\n\n\n\n
      \n
      \n

      Chapter 3. Data Description<\/strong><\/h3><\/div>\n\n\n\n
        \n
      • Introduction<\/li>\n\n\n\n
      • Graphical Methods<\/li>\n\n\n\n
      • Measures of Central Tendency<\/li>\n\n\n\n
      • Measures of Variability<\/li>\n\n\n\n
      • The Boxplot<\/li>\n\n\n\n
      • Sample Statistics and Population Parameters<\/li>\n\n\n\n
      • Software Systems<\/li>\n<\/ul>\n<\/div>\n\n\n\n
        <\/div>\n<\/div><\/div>\n\n\n\n
        \n
        \n

        Chapter 4. Probability Distributions<\/strong><\/h3><\/div>\n\n\n\n
          \n
        • Introduction<\/li>\n\n\n\n
        • Discrete and Continuous Probability Distributions<\/li>\n\n\n\n
        • The Normal Probability Distribution<\/li>\n\n\n\n
        • Sampling Distribution of Sample Means<\/li>\n<\/ul>\n<\/div>\n\n\n\n
          <\/div>\n<\/div><\/div>\n\n\n\n
          \n
          \n

          Chapter 5. Statistical Inference: Estimation<\/strong><\/h3><\/div>\n\n\n\n
            \n
          • Point and Interval Estimation<\/li>\n\n\n\n
          • Confidence Interval for a Proportion<\/li>\n\n\n\n
          • Confidence Interval for a Mean<\/li>\n\n\n\n
          • Choice of Sample Size<\/li>\n\n\n\n
          • Confidence Intervals for Median and Other Parameters<\/li>\n<\/ul>\n<\/div>\n\n\n\n
            <\/div>\n<\/div><\/div>\n\n\n\n
            \n
            \n

            Chapter 6. Statistical Inference: Significance <\/strong><\/strong>Tests<\/h3><\/div>\n\n\n\n
              \n
            • The Five Parts of Significance Test<\/li>\n\n\n\n
            • Significance Test for a Mean<\/li>\n\n\n\n
            • Significance Test for a Proportion<\/li>\n\n\n\n
            • Decisions and Types of Errors in Tests<\/li>\n\n\n\n
            • Limitations of Significance Tests<\/li>\n\n\n\n
            • Calculating P (Type II Error)<\/li>\n\n\n\n
            • Small-Sample Test for a Proportion – The Binomial Distribution<\/li>\n<\/ul>\n<\/div>\n\n\n\n
              <\/div>\n<\/div><\/div>\n\n\n\n
              \n
              \n

              Chapter 7. Comparison of Two Groups<\/strong><\/h3><\/div>\n\n\n\n
                \n
              • Introduction<\/li>\n\n\n\n
              • Categorical Data: Comparing Two Proportions<\/li>\n\n\n\n
              • Quantitative Data: Comparing Two Means<\/li>\n\n\n\n
              • Conditional Distribution Functions<\/li>\n<\/ul>\n<\/div>\n\n\n\n
                <\/div>\n<\/div><\/div>\n\n\n\n
                \n
                \n

                Chapter 8. Linear Regression and Correlation<\/strong><\/h3><\/div>\n\n\n\n
                  \n
                • Linear Relationships<\/li>\n\n\n\n
                • Least Squares Prediction Equation<\/li>\n\n\n\n
                • The Linear Regression Model<\/li>\n\n\n\n
                • Measuring Linear Association: The Correlation<\/li>\n\n\n\n
                • Inferences for the Sloope and Correlation<\/li>\n\n\n\n
                • Model Assumptions and Violations<\/li>\n<\/ul>\n<\/div>\n\n\n\n
                  <\/div>\n<\/div><\/div>\n\n\n\n
                  \n
                  \n

                  Chapter 9. Comparing Groups: Analysis of Variance (ANOVA)<\/strong><\/strong><\/h3><\/div>\n\n\n\n
                    \n
                  • Compairing Several Means: The Analysis of Variance F Test<\/li>\n\n\n\n
                  • Multiple Comparisons of Means<\/li>\n\n\n\n
                  • Performing ANOVA by Regression Modeling<\/li>\n\n\n\n
                  • Two Way ANOVA<\/li>\n\n\n\n
                  • Two Way ANOVA and Regression<\/li>\n<\/ul>\n<\/div>\n\n\n\n
                    <\/div>\n<\/div><\/div>\n\n\n\n
                    \n
                    \n

                    Chapter 10. Simulations<\/strong><\/h3><\/div>\n\n\n\n
                      \n
                    • Introduction<\/li>\n\n\n\n
                    • General Techniques<\/li>\n\n\n\n
                    • Simulating Discrete Distributions<\/li>\n\n\n\n
                    • Variance Reduction Techniques<\/li>\n<\/ul>\n<\/div>\n\n\n\n
                      <\/div>\n<\/div><\/div>\n\n\n\n

                      Bibliography<\/h2><\/div>\n\n\n\n
                        \n
                      • Agresti, A. Kateri, M. Foundations of Statistics for Data Scientists with R and Python<\/em><\/strong>. CRC Press, Boca Raton, 2021.<\/li>\n\n\n\n
                      • Agresti, A. Franklin, C. Statistics: The Art and Science of Learnng from Data<\/em><\/strong>. Pearson, Essex, 2018.<\/li>\n\n\n\n
                      • Agresti, A. Finlay, B. Statistical Methods for Social Sciences<\/em><\/strong>. 4th ed., Pearson, New York, 2009.<\/li>\n\n\n\n
                      • Lyman, O. Longnecker, M. <\/em>An Introduction to Statistical Methods & Data Analysis<\/em><\/strong>. 7th ed., Cengage Learning, Boston, 2016.<\/li>\n<\/ul>\n\n\n\n

                        Webgraphy<\/h2><\/div>\n\n\n\n
                          \n
                        • <\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"

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