[FreeCourseSite.com] Udemy - Statistics for Data Science and Business Analysis

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[FreeCourseSite.com] Udemy - Statistics for Data Science and Business Analysis

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种子哈希：530dda77512bf51439686ba7f5f31e06f50f5852
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收录时间：2021-04-15
最近下载：2024-12-11

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文件列表

  • 5. Practical example descriptive statistics/1. Practical example.mp4 168.3 MB
  • 17. Practical example regression analysis/1. Practical example regression analysis.mp4 135.6 MB
  • 9. Practical example inferential statistics/1. Practical example inferential statistics.mp4 107.6 MB
  • 10. Hypothesis testing Introduction/1. The null and the alternative hypothesis.mp4 96.6 MB
  • 10. Hypothesis testing Introduction/4. Establishing a rejection region and a significance level.mp4 86.5 MB
  • 7. Estimators and estimates/5. Calculating confidence intervals within a population with a known variance.mp4 82.0 MB
  • 3. The fundamentals of descriptive statistics/1. The various types of data we can work with.mp4 76.1 MB
  • 8. Confidence intervals advanced topics/1. Calculating confidence intervals for two means with dependent samples.mp4 73.9 MB
  • 12. Practical example hypothesis testing/1. Practical example hypothesis testing.mp4 72.7 MB
  • 1. Introduction/1. What does the course cover.mp4 72.0 MB
  • 6. Distributions/9. Understanding the central limit theorem.mp4 65.9 MB
  • 6. Distributions/2. What is a distribution.mp4 64.6 MB
  • 2. Sample or population data/1. Understanding the difference between a population and a sample.mp4 60.9 MB
  • 7. Estimators and estimates/7. Confidence interval clarifications.mp4 59.9 MB
  • 11. Hypothesis testing Let's start testing!/3. What is the p-value and why is it one of the most useful tools for statisticians.mp4 58.6 MB
  • 3. The fundamentals of descriptive statistics/3. Levels of measurement.mp4 57.0 MB
  • 11. Hypothesis testing Let's start testing!/1. Test for the mean. Population variance known.mp4 56.9 MB
  • 13. The fundamentals of regression analysis/5. The linear regression model made easy.mp4 53.5 MB
  • 4. Measures of central tendency, asymmetry, and variability/6. Measuring how data is spread out calculating variance.mp4 53.4 MB
  • 11. Hypothesis testing Let's start testing!/7. Test for the mean. Dependent samples.mp4 52.9 MB
  • 7. Estimators and estimates/3. Confidence intervals - an invaluable tool for decision making.mp4 52.4 MB
  • 6. Distributions/4. The Normal distribution.mp4 52.3 MB
  • 7. Estimators and estimates/1. Working with estimators and estimates.mp4 50.2 MB
  • 7. Estimators and estimates/12. What is a margin of error and why is it important in Statistics.mp4 49.5 MB
  • 13. The fundamentals of regression analysis/11. A practical example - Reinforced learning.mp4 48.1 MB
  • 4. Measures of central tendency, asymmetry, and variability/8. Standard deviation and coefficient of variation.mp4 47.4 MB
  • 10. Hypothesis testing Introduction/6. Type I error vs Type II error.mp4 46.1 MB
  • 14. Subtleties of regression analysis/12. The adjusted R-squared.mp4 45.8 MB
  • 14. Subtleties of regression analysis/1. Decomposing the linear regression model - understanding its nuts and bolts.mp4 44.3 MB
  • 11. Hypothesis testing Let's start testing!/5. Test for the mean. Population variance unknown.mp4 42.2 MB
  • 15. Assumptions for linear regression analysis/7. A3. Normality and homoscedasticity.mp4 41.9 MB
  • 3. The fundamentals of descriptive statistics/14. Cross tables and scatter plots.mp4 41.7 MB
  • 16. Dealing with categorical data/1. Dummy variables.mp4 40.0 MB
  • 4. Measures of central tendency, asymmetry, and variability/1. The main measures of central tendency mean, median and mode.mp4 38.9 MB
  • 14. Subtleties of regression analysis/7. Studying regression tables.mp4 38.6 MB
  • 3. The fundamentals of descriptive statistics/5. Categorical variables. Visualization techniques for categorical variables.mp4 38.4 MB
  • 14. Subtleties of regression analysis/3. What is R-squared and how does it help us.mp4 38.2 MB
  • 11. Hypothesis testing Let's start testing!/11. Test for the mean. Independent samples (Part 2).mp4 38.1 MB
  • 7. Estimators and estimates/8. Student's T distribution.mp4 37.1 MB
  • 15. Assumptions for linear regression analysis/5. A2. No endogeneity.mp4 34.0 MB
  • 7. Estimators and estimates/10. Calculating confidence intervals within a population with an unknown variance.mp4 33.7 MB
  • 11. Hypothesis testing Let's start testing!/9. Test for the mean. Independent samples (Part 1).mp4 31.4 MB
  • 4. Measures of central tendency, asymmetry, and variability/13. The correlation coefficient.mp4 30.8 MB
  • 8. Confidence intervals advanced topics/3. Calculating confidence intervals for two means with independent samples (part 1).mp4 30.1 MB
  • 4. Measures of central tendency, asymmetry, and variability/11. Calculating and understanding covariance.mp4 28.8 MB
  • 8. Confidence intervals advanced topics/5. Calculating confidence intervals for two means with independent samples (part 2).mp4 28.1 MB
  • 15. Assumptions for linear regression analysis/11. A5. No multicollinearity.mp4 27.9 MB
  • 15. Assumptions for linear regression analysis/9. A4. No autocorrelation.mp4 27.1 MB
  • 3. The fundamentals of descriptive statistics/8. Numerical variables. Using a frequency distribution table.mp4 27.1 MB
  • 13. The fundamentals of regression analysis/3. Correlation and causation.mp4 26.8 MB
  • 6. Distributions/11. Standard error.mp4 23.9 MB
  • 6. Distributions/6. The standard normal distribution.mp4 23.6 MB
  • 14. Subtleties of regression analysis/5. The ordinary least squares setting and its practical applications.mp4 21.0 MB
  • 8. Confidence intervals advanced topics/7. Calculating confidence intervals for two means with independent samples (part 3).mp4 20.8 MB
  • 4. Measures of central tendency, asymmetry, and variability/3. Measuring skewness.mp4 20.4 MB
  • 13. The fundamentals of regression analysis/1. Introduction to regression analysis.mp4 20.3 MB
  • 15. Assumptions for linear regression analysis/1. OLS assumptions.mp4 20.3 MB
  • 14. Subtleties of regression analysis/10. The multiple linear regression model.mp4 20.0 MB
  • 6. Distributions/1. Introduction to inferential statistics.mp4 16.2 MB
  • 14. Subtleties of regression analysis/14. What does the F-statistic show us and why do we need to understand it.mp4 14.6 MB
  • 3. The fundamentals of descriptive statistics/11. Histogram charts.mp4 14.5 MB
  • 13. The fundamentals of regression analysis/7. What is the difference between correlation and regression.mp4 13.3 MB
  • 15. Assumptions for linear regression analysis/3. A1. Linearity.mp4 12.6 MB
  • 13. The fundamentals of regression analysis/9. A geometrical representation of the linear regression model.mp4 5.1 MB
  • 9. Practical example inferential statistics/2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.9 MB
  • 9. Practical example inferential statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx 1.8 MB
  • 9. Practical example inferential statistics/2.1 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.8 MB
  • 17. Practical example regression analysis/1.1 5.21. Regression_Analysis_practical_example.xlsx 1.5 MB
  • 11. Hypothesis testing Let's start testing!/3.1 Online p-value calculator.pdf 1.2 MB
  • 10. Hypothesis testing Introduction/1.1 Course notes_hypothesis_testing.pdf 672.2 kB
  • 10. Hypothesis testing Introduction/4.1 Course notes_hypothesis_testing.pdf 672.2 kB
  • 2. Sample or population data/1.1 Course notes_descriptive_statistics.pdf 493.8 kB
  • 3. The fundamentals of descriptive statistics/1.1 Course notes_descriptive_statistics.pdf 493.8 kB
  • 6. Distributions/1.1 Course notes_inferential statistics.pdf 391.5 kB
  • 6. Distributions/2.1 Course notes_inferential statistics.pdf 391.5 kB
  • 3. The fundamentals of descriptive statistics/13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 296.1 kB
  • 3. The fundamentals of descriptive statistics/7.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 296.1 kB
  • 13. The fundamentals of regression analysis/1.1 Course notes_regression_analysis.pdf 276.5 kB
  • 13. The fundamentals of regression analysis/3.2 Course notes_regression_analysis.pdf 276.5 kB
  • 5. Practical example descriptive statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx 150.0 kB
  • 5. Practical example descriptive statistics/2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 149.9 kB
  • 5. Practical example descriptive statistics/2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 123.2 kB
  • 12. Practical example hypothesis testing/1.1 4.10.Hypothesis-testing-section-practical-example.xlsx 53.0 kB
  • 12. Practical example hypothesis testing/2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 45.1 kB
  • 12. Practical example hypothesis testing/2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx 44.4 kB
  • 3. The fundamentals of descriptive statistics/7.2 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx 42.1 kB
  • 3. The fundamentals of descriptive statistics/16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx 41.4 kB
  • 4. Measures of central tendency, asymmetry, and variability/3.1 2.8. Skewness_lesson.xlsx 35.5 kB
  • 3. The fundamentals of descriptive statistics/5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 31.5 kB
  • 4. Measures of central tendency, asymmetry, and variability/5.1 2.8. Skewness_exercise_solution.xlsx 30.5 kB
  • 4. Measures of central tendency, asymmetry, and variability/12.2 2.11. Covariance_exercise_solution.xlsx 30.2 kB
  • 4. Measures of central tendency, asymmetry, and variability/15.1 2.12. Correlation_exercise_solution.xlsx 30.2 kB
  • 4. Measures of central tendency, asymmetry, and variability/15.2 2.12. Correlation_exercise.xlsx 30.0 kB
  • 3. The fundamentals of descriptive statistics/14.1 2.6. Cross table and scatter plot.xlsx 26.7 kB
  • 7. Estimators and estimates/5.1 3.9.The-z-table.xlsx 26.2 kB
  • 7. Estimators and estimates/6.2 3.9.The-z-table.xlsx 26.2 kB
  • 16. Dealing with categorical data/1.1 5.20. Dummy variables_lesson.xlsx 25.8 kB
  • 4. Measures of central tendency, asymmetry, and variability/13.1 2.12. Correlation_lesson.xlsx 25.6 kB
  • 4. Measures of central tendency, asymmetry, and variability/11.1 2.11. Covariance_lesson.xlsx 25.5 kB
  • 6. Distributions/8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.6 kB
  • 13. The fundamentals of regression analysis/11.1 5.6. Example_lesson.xlsx 24.1 kB
  • 1. Introduction/1.1 Statistics Glossary.xlsx 20.8 kB
  • 4. Measures of central tendency, asymmetry, and variability/12.1 2.11. Covariance_exercise.xlsx 20.7 kB
  • 2. Sample or population data/1.2 Glossary.xlsx 20.4 kB
  • 5. Practical example descriptive statistics/1. Practical example.srt 20.2 kB
  • 6. Distributions/2.2 3.2. What is a distribution_lesson.xlsx 19.9 kB
  • 3. The fundamentals of descriptive statistics/11.1 2.5. The Histogram_lesson.xlsx 19.1 kB
  • 14. Subtleties of regression analysis/12.1 5.12. Adjusted R-squared_lesson.xlsx 18.7 kB
  • 17. Practical example regression analysis/1. Practical example regression analysis.srt 17.9 kB
  • 3. The fundamentals of descriptive statistics/13.2 2.5.The-Histogram-exercise-solution.xlsx 17.5 kB
  • 3. The fundamentals of descriptive statistics/16.1 2.6. Cross table and scatter plot_exercise.xlsx 16.7 kB
  • 7. Estimators and estimates/10.2 3.11. The t-table.xlsx 16.2 kB
  • 7. Estimators and estimates/11.2 3.11. The t-table.xlsx 16.2 kB
  • 3. The fundamentals of descriptive statistics/13.3 2.5. The Histogram_exercise.xlsx 15.9 kB
  • 3. The fundamentals of descriptive statistics/7.3 2.3. Categorical variables. Visualization techniques_exercise.xlsx 15.6 kB
  • 11. Hypothesis testing Let's start testing!/5.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.9 kB
  • 11. Hypothesis testing Let's start testing!/8.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx 14.7 kB
  • 8. Confidence intervals advanced topics/2.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx 14.6 kB
  • 8. Confidence intervals advanced topics/2.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx 14.1 kB
  • 9. Practical example inferential statistics/1. Practical example inferential statistics.srt 13.6 kB
  • 3. The fundamentals of descriptive statistics/10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.6 kB
  • 11. Hypothesis testing Let's start testing!/8.2 4.7. Test for the mean. Dependent samples_exercise.xlsx 13.1 kB
  • 11. Hypothesis testing Let's start testing!/6.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.9 kB
  • 4. Measures of central tendency, asymmetry, and variability/10.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.9 kB
  • 14. Subtleties of regression analysis/7.1 5.10.Regression-tables-lesson.xlsx 12.9 kB
  • 14. Subtleties of regression analysis/9.1 5.10. Regression tables_exercise_solution.xlsx 12.8 kB
  • 14. Subtleties of regression analysis/9.2 5.10. Regression tables_exercise.xlsx 12.3 kB
  • 3. The fundamentals of descriptive statistics/10.2 2.4.Numerical-variables.Frequency-distribution-table-exercise.xlsx 12.3 kB
  • 6. Distributions/8.1 3.4.Standard-normal-distribution-exercise.xlsx 12.3 kB
  • 4. Measures of central tendency, asymmetry, and variability/10.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.9 kB
  • 3. The fundamentals of descriptive statistics/8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx 11.7 kB
  • 11. Hypothesis testing Let's start testing!/13.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.7 kB
  • 4. Measures of central tendency, asymmetry, and variability/2.1 2.7. Mean, median and mode_exercise_solution.xlsx 11.6 kB
  • 11. Hypothesis testing Let's start testing!/6.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.6 kB
  • 11. Hypothesis testing Let's start testing!/10.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.5 kB
  • 11. Hypothesis testing Let's start testing!/2.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx 11.5 kB
  • 7. Estimators and estimates/5.2 3.9. Population variance known, z-score_lesson.xlsx 11.5 kB
  • 7. Estimators and estimates/6.3 3.9. Population variance known, z-score_exercise_solution.xlsx 11.4 kB
  • 7. Estimators and estimates/11.3 3.11. Population variance unknown, t-score_exercise_solution.xlsx 11.4 kB
  • 4. Measures of central tendency, asymmetry, and variability/7.1 2.9. Variance_exercise_solution.xlsx 11.3 kB
  • 11. Hypothesis testing Let's start testing!/2.1 4.4. Test for the mean. Population variance known_exercise.xlsx 11.3 kB
  • 4. Measures of central tendency, asymmetry, and variability/8.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx 11.2 kB
  • 11. Hypothesis testing Let's start testing!/1.1 4.4. Test for the mean. Population variance known_lesson.xlsx 11.2 kB
  • 4. Measures of central tendency, asymmetry, and variability/2.2 2.7. Mean, median and mode_exercise.xlsx 11.1 kB
  • 7. Estimators and estimates/6.1 3.9. Population variance known, z-score_exercise.xlsx 11.1 kB
  • 4. Measures of central tendency, asymmetry, and variability/7.2 2.9. Variance_exercise.xlsx 11.1 kB
  • 7. Estimators and estimates/10.1 3.11. Population variance unknown, t-score_lesson.xlsx 11.0 kB
  • 11. Hypothesis testing Let's start testing!/10.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 11.0 kB
  • 13. The fundamentals of regression analysis/3.1 5.2. Correlation and causation_lesson.xlsx 10.9 kB
  • 7. Estimators and estimates/11.1 3.11. Population variance unknown, t-score_exercise.xlsx 10.9 kB
  • 11. Hypothesis testing Let's start testing!/13.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.8 kB
  • 4. Measures of central tendency, asymmetry, and variability/1.1 2.7. Mean, median and mode_lesson.xlsx 10.7 kB
  • 8. Confidence intervals advanced topics/1.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx 10.7 kB
  • 6. Distributions/6.1 3.4. Standard normal distribution_lesson.xlsx 10.6 kB
  • 8. Confidence intervals advanced topics/4.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx 10.4 kB
  • 4. Measures of central tendency, asymmetry, and variability/6.1 2.9. Variance_lesson.xlsx 10.3 kB
  • 8. Confidence intervals advanced topics/3.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx 10.1 kB
  • 8. Confidence intervals advanced topics/4.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx 10.1 kB
  • 8. Confidence intervals advanced topics/6.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx 10.0 kB
  • 11. Hypothesis testing Let's start testing!/7.1 4.7. Test for the mean. Dependent samples_lesson.xlsx 10.0 kB
  • 11. Hypothesis testing Let's start testing!/9.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx 9.9 kB
  • 8. Confidence intervals advanced topics/5.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx 9.7 kB
  • 4. Measures of central tendency, asymmetry, and variability/5.2 2.8. Skewness_exercise.xlsx 9.7 kB
  • 11. Hypothesis testing Let's start testing!/11.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx 9.5 kB
  • 8. Confidence intervals advanced topics/6.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx 9.4 kB
  • 7. Estimators and estimates/5. Calculating confidence intervals within a population with a known variance.srt 9.3 kB
  • 10. Hypothesis testing Introduction/4. Establishing a rejection region and a significance level.srt 8.9 kB
  • 12. Practical example hypothesis testing/1. Practical example hypothesis testing.srt 8.3 kB
  • 8. Confidence intervals advanced topics/1. Calculating confidence intervals for two means with dependent samples.srt 8.1 kB
  • 11. Hypothesis testing Let's start testing!/1. Test for the mean. Population variance known.srt 7.7 kB
  • 4. Measures of central tendency, asymmetry, and variability/6. Measuring how data is spread out calculating variance.srt 7.6 kB
  • 13. The fundamentals of regression analysis/11. A practical example - Reinforced learning.srt 7.6 kB
  • 13. The fundamentals of regression analysis/5. The linear regression model made easy.srt 7.2 kB
  • 10. Hypothesis testing Introduction/1. The null and the alternative hypothesis.srt 7.1 kB
  • 15. Assumptions for linear regression analysis/7. A3. Normality and homoscedasticity.srt 6.8 kB
  • 3. The fundamentals of descriptive statistics/14. Cross tables and scatter plots.srt 6.7 kB
  • 14. Subtleties of regression analysis/12. The adjusted R-squared.srt 6.7 kB
  • 14. Subtleties of regression analysis/3. What is R-squared and how does it help us.srt 6.5 kB
  • 11. Hypothesis testing Let's start testing!/7. Test for the mean. Dependent samples.srt 6.5 kB
  • 3. The fundamentals of descriptive statistics/5. Categorical variables. Visualization techniques for categorical variables.srt 6.5 kB
  • 7. Estimators and estimates/12. What is a margin of error and why is it important in Statistics.srt 6.3 kB
  • 16. Dealing with categorical data/1. Dummy variables.srt 6.3 kB
  • 14. Subtleties of regression analysis/7. Studying regression tables.srt 6.2 kB
  • 4. Measures of central tendency, asymmetry, and variability/8. Standard deviation and coefficient of variation.srt 6.2 kB
  • 3. The fundamentals of descriptive statistics/1. The various types of data we can work with.srt 6.0 kB
  • 8. Confidence intervals advanced topics/3. Calculating confidence intervals for two means with independent samples (part 1).srt 6.0 kB
  • 6. Distributions/2. What is a distribution.srt 5.9 kB
  • 13. The fundamentals of regression analysis/3. Correlation and causation.srt 5.8 kB
  • 11. Hypothesis testing Let's start testing!/5. Test for the mean. Population variance unknown.srt 5.8 kB
  • 1. Introduction/1. What does the course cover.srt 5.7 kB
  • 4. Measures of central tendency, asymmetry, and variability/1. The main measures of central tendency mean, median and mode.srt 5.7 kB
  • 6. Distributions/9. Understanding the central limit theorem.srt 5.7 kB
  • 7. Estimators and estimates/7. Confidence interval clarifications.srt 5.6 kB
  • 2. Sample or population data/1. Understanding the difference between a population and a sample.srt 5.6 kB
  • 10. Hypothesis testing Introduction/6. Type I error vs Type II error.srt 5.5 kB
  • 11. Hypothesis testing Let's start testing!/9. Test for the mean. Independent samples (Part 1).srt 5.4 kB
  • 15. Assumptions for linear regression analysis/5. A2. No endogeneity.srt 5.4 kB
  • 11. Hypothesis testing Let's start testing!/11. Test for the mean. Independent samples (Part 2).srt 5.3 kB
  • 7. Estimators and estimates/10. Calculating confidence intervals within a population with an unknown variance.srt 5.3 kB
  • 11. Hypothesis testing Let's start testing!/3. What is the p-value and why is it one of the most useful tools for statisticians.srt 5.1 kB
  • 6. Distributions/4. The Normal distribution.srt 5.1 kB
  • 4. Measures of central tendency, asymmetry, and variability/11. Calculating and understanding covariance.srt 4.9 kB
  • 15. Assumptions for linear regression analysis/11. A5. No multicollinearity.srt 4.7 kB
  • 4. Measures of central tendency, asymmetry, and variability/13. The correlation coefficient.srt 4.7 kB
  • 3. The fundamentals of descriptive statistics/3. Levels of measurement.srt 4.7 kB
  • 15. Assumptions for linear regression analysis/9. A4. No autocorrelation.srt 4.6 kB
  • 8. Confidence intervals advanced topics/5. Calculating confidence intervals for two means with independent samples (part 2).srt 4.5 kB
  • 3. The fundamentals of descriptive statistics/8. Numerical variables. Using a frequency distribution table.srt 4.4 kB
  • 7. Estimators and estimates/8. Student's T distribution.srt 4.3 kB
  • 14. Subtleties of regression analysis/1. Decomposing the linear regression model - understanding its nuts and bolts.srt 4.3 kB
  • 6. Distributions/6. The standard normal distribution.srt 4.0 kB
  • 7. Estimators and estimates/1. Working with estimators and estimates.srt 3.9 kB
  • 4. Measures of central tendency, asymmetry, and variability/3. Measuring skewness.srt 3.6 kB
  • 14. Subtleties of regression analysis/10. The multiple linear regression model.srt 3.4 kB
  • 3. The fundamentals of descriptive statistics/11. Histogram charts.srt 3.2 kB
  • 7. Estimators and estimates/3. Confidence intervals - an invaluable tool for decision making.srt 3.1 kB
  • 15. Assumptions for linear regression analysis/1. OLS assumptions.srt 3.1 kB
  • 14. Subtleties of regression analysis/5. The ordinary least squares setting and its practical applications.srt 2.9 kB
  • 14. Subtleties of regression analysis/14. What does the F-statistic show us and why do we need to understand it.srt 2.6 kB
  • 15. Assumptions for linear regression analysis/3. A1. Linearity.srt 2.4 kB
  • 10. Hypothesis testing Introduction/2. Further reading on null and alternative hypotheses.html 2.3 kB
  • 13. The fundamentals of regression analysis/7. What is the difference between correlation and regression.srt 2.1 kB
  • 6. Distributions/11. Standard error.srt 2.0 kB
  • 8. Confidence intervals advanced topics/7. Calculating confidence intervals for two means with independent samples (part 3).srt 2.0 kB
  • 18. Bonus lecture/1. Bonus lecture Next steps.html 1.9 kB
  • 13. The fundamentals of regression analysis/9. A geometrical representation of the linear regression model.srt 1.7 kB
  • 6. Distributions/1. Introduction to inferential statistics.srt 1.7 kB
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  • 1. Introduction/2. Download all resources.html 716 Bytes
  • 11. Hypothesis testing Let's start testing!/12. Test for the mean. Independent samples (Part 2).html 160 Bytes
  • 13. The fundamentals of regression analysis/10. A geometrical representation of the linear regression model.html 160 Bytes
  • 13. The fundamentals of regression analysis/4. Correlation and causation.html 160 Bytes
  • 14. Subtleties of regression analysis/11. The multiple linear regression model.html 160 Bytes
  • 14. Subtleties of regression analysis/6. The ordinary least squares setting and its practical applications.html 160 Bytes
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  • 15. Assumptions for linear regression analysis/4. A1. Linearity.html 160 Bytes
  • 15. Assumptions for linear regression analysis/8. A3. Normality and homoscedasticity.html 160 Bytes
  • 3. The fundamentals of descriptive statistics/12. Histogram charts.html 160 Bytes
  • 3. The fundamentals of descriptive statistics/15. Cross Tables and Scatter Plots.html 160 Bytes
  • 3. The fundamentals of descriptive statistics/6. Categorical variables. Visualization Techniques.html 160 Bytes
  • 3. The fundamentals of descriptive statistics/9. Numerical variables. Using a frequency distribution table.html 160 Bytes
  • 4. Measures of central tendency, asymmetry, and variability/14. Correlation.html 160 Bytes
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  • 4. Measures of central tendency, asymmetry, and variability/9. Standard deviation.html 160 Bytes
  • 6. Distributions/12. Standard error.html 160 Bytes
  • 6. Distributions/7. The standard normal distribution.html 160 Bytes
  • 10. Hypothesis testing Introduction/3. Null vs alternative.html 159 Bytes
  • 10. Hypothesis testing Introduction/5. Rejection region and significance level.html 159 Bytes
  • 10. Hypothesis testing Introduction/7. Type I error vs type II error.html 159 Bytes
  • 11. Hypothesis testing Let's start testing!/4. p-value.html 159 Bytes
  • 13. The fundamentals of regression analysis/2. Introduction.html 159 Bytes
  • 13. The fundamentals of regression analysis/6. The linear regression model.html 159 Bytes
  • 13. The fundamentals of regression analysis/8. Correlation vs regression.html 159 Bytes
  • 14. Subtleties of regression analysis/13. The adjusted R-squared.html 159 Bytes
  • 14. Subtleties of regression analysis/2. Decomposition.html 159 Bytes
  • 14. Subtleties of regression analysis/4. R-squared.html 159 Bytes
  • 15. Assumptions for linear regression analysis/10. A4. No autocorrelation.html 159 Bytes
  • 15. Assumptions for linear regression analysis/12. A5. No multicollinearity.html 159 Bytes
  • 15. Assumptions for linear regression analysis/2. OLS assumptions.html 159 Bytes
  • 15. Assumptions for linear regression analysis/6. A2. No endogeneity.html 159 Bytes
  • 2. Sample or population data/2. Population vs sample.html 159 Bytes
  • 3. The fundamentals of descriptive statistics/2. Types of data.html 159 Bytes
  • 3. The fundamentals of descriptive statistics/4. Levels of measurement.html 159 Bytes
  • 6. Distributions/10. The central limit theorem.html 159 Bytes
  • 6. Distributions/3. What is a distribution.html 159 Bytes
  • 6. Distributions/5. The Normal distribution.html 159 Bytes
  • 7. Estimators and estimates/13. Margin of error.html 159 Bytes
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  • 7. Estimators and estimates/4. Confidence intervals.html 159 Bytes
  • 7. Estimators and estimates/9. Student's T distribution.html 159 Bytes
  • 11. Hypothesis testing Let's start testing!/2. Test for the mean. Population variance known. Exercise.html 86 Bytes
  • 11. Hypothesis testing Let's start testing!/6. Test for the mean. Population variance unknown. Exercise.html 86 Bytes
  • 11. Hypothesis testing Let's start testing!/8. Test for the mean. Dependent samples. Exercise.html 86 Bytes
  • 12. Practical example hypothesis testing/2. Practical example hypothesis testing.html 86 Bytes
  • 14. Subtleties of regression analysis/9. Regression tables. Exercise.html 86 Bytes
  • 11. Hypothesis testing Let's start testing!/10. Test for the mean. Independent samples (Part 1).html 82 Bytes
  • 11. Hypothesis testing Let's start testing!/13. Test for the mean. Independent samples (Part 2). Exercise.html 82 Bytes
  • 3. The fundamentals of descriptive statistics/10. Numerical variables. Using a frequency distribution table. Exercise.html 81 Bytes
  • 3. The fundamentals of descriptive statistics/13. Histogram charts. Exercise.html 81 Bytes
  • 3. The fundamentals of descriptive statistics/16. Cross tables and scatter plots. Exercise.html 81 Bytes
  • 3. The fundamentals of descriptive statistics/7. Categorical variables. Visualization techniques. Exercise.html 81 Bytes
  • 4. Measures of central tendency, asymmetry, and variability/10. Standard deviation and coefficient of variation. Exercise.html 81 Bytes
  • 4. Measures of central tendency, asymmetry, and variability/12. Covariance. Exercise.html 81 Bytes
  • 4. Measures of central tendency, asymmetry, and variability/15. Correlation coefficient.html 81 Bytes
  • 4. Measures of central tendency, asymmetry, and variability/2. Mean, median and mode. Exercise.html 81 Bytes
  • 4. Measures of central tendency, asymmetry, and variability/5. Skewness. Exercise.html 81 Bytes
  • 4. Measures of central tendency, asymmetry, and variability/7. Variance. Exercise.html 81 Bytes
  • 5. Practical example descriptive statistics/2. Practical example descriptive statistics.html 81 Bytes
  • 6. Distributions/8. Standard Normal Distribution. Exercise.html 81 Bytes
  • 7. Estimators and estimates/11. Population variance unknown. T-score. Exercise.html 81 Bytes
  • 7. Estimators and estimates/6. Confidence intervals. Population variance known. Exercise.html 81 Bytes
  • 8. Confidence intervals advanced topics/2. Confidence intervals. Two means. Dependent samples. Exercise.html 81 Bytes
  • 8. Confidence intervals advanced topics/4. Confidence intervals. Two means. Independent samples (Part 1). Exercise.html 81 Bytes
  • 8. Confidence intervals advanced topics/6. Confidence intervals. Two means. Independent samples (Part 2). Exercise.html 81 Bytes
  • 9. Practical example inferential statistics/2. Practical example inferential statistics.html 81 Bytes

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