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[Tutorialsplanet.NET] Udemy - Statistics for Data Science and Business Analysis
Size 2.77 GB
Files 226
Info Hash: EFCBA0F41496728864047D090DB714AFC5CD34CD
Indexed 2020-08-05 09:39:59
Updated 2020-08-05 09:39:59
📂 File List (226)
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1. What does the course cover.mp4
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1. What does the course cover.srt
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1.1 Statistics Glossary.xlsx
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1. The null and the alternative hypothesis.mp4
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1. The null and the alternative hypothesis.srt
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1.1 Course notes_hypothesis_testing.pdf
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4. Establishing a rejection region and a significance level.mp4
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4. Establishing a rejection region and a significance level.srt
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4.1 Course notes_hypothesis_testing.pdf
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6. Type I error vs Type II error.mp4
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6. Type I error vs Type II error.srt
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1. Test for the mean. Population variance known.mp4
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1. Test for the mean. Population variance known.srt
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1.1 4.4. Test for the mean. Population variance known_lesson.xlsx
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10.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx
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10.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx
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11. Test for the mean. Independent samples (Part 2).mp4
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11. Test for the mean. Independent samples (Part 2).srt
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11.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx
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13.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx
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13.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx
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2.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx
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2.2 4.4. Test for the mean. Population variance known_exercise.xlsx
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3. What is the p-value and why is it one of the most useful tools for statisticians.mp4
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3. What is the p-value and why is it one of the most useful tools for statisticians.srt
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3.1 Online p-value calculator.pdf
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5. Test for the mean. Population variance unknown.mp4
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5. Test for the mean. Population variance unknown.srt
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5.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx
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6.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx
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6.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx
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7. Test for the mean. Dependent samples.mp4
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7. Test for the mean. Dependent samples.srt
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7.1 4.7. Test for the mean. Dependent samples_lesson.xlsx
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8.1 4.7. Test for the mean. Dependent samples_exercise.xlsx
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8.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx
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9. Test for the mean. Independent samples (Part 1).mp4
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9. Test for the mean. Independent samples (Part 1).srt
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9.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx
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1. Practical example hypothesis testing.mp4
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1. Practical example hypothesis testing.srt
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1.1 4.10.Hypothesis-testing-section-practical-example.xlsx
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2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx
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2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx
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1. Introduction to regression analysis.mp4
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1. Introduction to regression analysis.srt
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1.1 Course notes_regression_analysis.pdf
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11. A practical example - Reinforced learning.mp4
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11. A practical example - Reinforced learning.srt
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11.1 5.6. Example_lesson.xlsx
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3. Correlation and causation.mp4
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3. Correlation and causation.srt
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3.1 Course notes_regression_analysis.pdf
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3.2 5.2. Correlation and causation_lesson.xlsx
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5. The linear regression model made easy.mp4
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5. The linear regression model made easy.srt
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7. What is the difference between correlation and regression.mp4
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7. What is the difference between correlation and regression.srt
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9. A geometrical representation of the linear regression model.mp4
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9. A geometrical representation of the linear regression model.srt
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1. Decomposing the linear regression model - understanding its nuts and bolts.mp4
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1. Decomposing the linear regression model - understanding its nuts and bolts.srt
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10. The multiple linear regression model.mp4
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10. The multiple linear regression model.srt
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12. The adjusted R-squared.mp4
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12. The adjusted R-squared.srt
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12.1 5.12. Adjusted R-squared_lesson.xlsx
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14. What does the F-statistic show us and why do we need to understand it.mp4
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14. What does the F-statistic show us and why do we need to understand it.srt
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3. What is R-squared and how does it help us.mp4
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3. What is R-squared and how does it help us.srt
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5. The ordinary least squares setting and its practical applications.mp4
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5. The ordinary least squares setting and its practical applications.srt
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7. Studying regression tables.mp4
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7. Studying regression tables.srt
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7.1 5.10.Regression-tables-lesson.xlsx
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9.1 5.10. Regression tables_exercise_solution.xlsx
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9.2 5.10. Regression tables_exercise.xlsx
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1. OLS assumptions.mp4
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1. OLS assumptions.srt
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11. A5. No multicollinearity.mp4
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11. A5. No multicollinearity.srt
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3. A1. Linearity.mp4
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3. A1. Linearity.srt
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5. A2. No endogeneity.mp4
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5. A2. No endogeneity.srt
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7. A3. Normality and homoscedasticity.mp4
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7. A3. Normality and homoscedasticity.srt
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9. A4. No autocorrelation.mp4
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9. A4. No autocorrelation.srt
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1. Dummy variables.mp4
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1. Dummy variables.srt
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1.1 5.20. Dummy variables_lesson.xlsx
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1. Practical example regression analysis.mp4
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1. Practical example regression analysis.srt
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1.1 5.21. Regression_Analysis_practical_example.xlsx
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1. Understanding the difference between a population and a sample.mp4
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1. Understanding the difference between a population and a sample.srt
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1.1 Glossary.xlsx
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1.2 Course notes_descriptive_statistics.pdf
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1. The various types of data we can work with.mp4
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1. The various types of data we can work with.srt
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1.1 Course notes_descriptive_statistics.pdf
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10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise.xlsx
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10.2 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx
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11. Histogram charts.mp4
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11. Histogram charts.srt
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11.1 2.5. The Histogram_lesson.xlsx
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13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf
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13.2 2.5.The-Histogram-exercise-solution.xlsx
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13.3 2.5. The Histogram_exercise.xlsx
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14. Cross tables and scatter plots.mp4
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14. Cross tables and scatter plots.srt
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14.1 2.6. Cross table and scatter plot.xlsx
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16.1 2.6. Cross table and scatter plot_exercise.xlsx
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16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx
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3. Levels of measurement.mp4
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3. Levels of measurement.srt
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5. Categorical variables. Visualization techniques for categorical variables.mp4
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5. Categorical variables. Visualization techniques for categorical variables.srt
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5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx
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7.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf
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7.2 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx
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7.3 2.3. Categorical variables. Visualization techniques_exercise.xlsx
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8. Numerical variables. Using a frequency distribution table.mp4
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8. Numerical variables. Using a frequency distribution table.srt
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8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx
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1. The main measures of central tendency mean, median and mode.mp4
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1. The main measures of central tendency mean, median and mode.srt
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1.1 2.7. Mean, median and mode_lesson.xlsx
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10.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx
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10.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx
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11. Calculating and understanding covariance.mp4
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11. Calculating and understanding covariance.srt
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11.1 2.11. Covariance_lesson.xlsx
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12.1 2.11. Covariance_exercise.xlsx
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12.2 2.11. Covariance_exercise_solution.xlsx
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13. The correlation coefficient.mp4
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13. The correlation coefficient.srt
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13.1 2.12. Correlation_lesson.xlsx
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15.1 2.12. Correlation_exercise_solution.xlsx
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15.2 2.12. Correlation_exercise.xlsx
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2.1 2.7. Mean, median and mode_exercise_solution.xlsx
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2.2 2.7. Mean, median and mode_exercise.xlsx
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3. Measuring skewness.mp4
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3. Measuring skewness.srt
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3.1 2.8. Skewness_lesson.xlsx
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5.1 2.8. Skewness_exercise.xlsx
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5.2 2.8. Skewness_exercise_solution.xlsx
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6. Measuring how data is spread out calculating variance.mp4
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6. Measuring how data is spread out calculating variance.srt
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6.1 2.9. Variance_lesson.xlsx
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7.1 2.9. Variance_exercise.xlsx
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7.2 2.9. Variance_exercise_solution.xlsx
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8. Standard deviation and coefficient of variation.mp4
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8. Standard deviation and coefficient of variation.srt
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8.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx
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1. Practical example.mp4
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1. Practical example.srt
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1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx
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2.1 2.13.Practical-example.Descriptive-statistics-exercise.xlsx
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2.2 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx
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1. Introduction to inferential statistics.mp4
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1. Introduction to inferential statistics.srt
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1.1 Course notes_inferential statistics.pdf
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11. Standard error.mp4
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11. Standard error.srt
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2. What is a distribution.mp4
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2. What is a distribution.srt
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2.1 3.2. What is a distribution_lesson.xlsx
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2.2 Course notes_inferential statistics.pdf
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4. The Normal distribution.mp4
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4. The Normal distribution.srt
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6. The standard normal distribution.mp4
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6. The standard normal distribution.srt
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6.1 3.4. Standard normal distribution_lesson.xlsx
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8.1 3.4.Standard-normal-distribution-exercise-solution.xlsx
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8.2 3.4.Standard-normal-distribution-exercise.xlsx
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9. Understanding the central limit theorem.mp4
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9. Understanding the central limit theorem.srt
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1. Working with estimators and estimates.mp4
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1. Working with estimators and estimates.srt
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10. Calculating confidence intervals within a population with an unknown variance.mp4
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10. Calculating confidence intervals within a population with an unknown variance.srt
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10.1 3.11. Population variance unknown, t-score_lesson.xlsx
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10.2 3.11. The t-table.xlsx
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11.1 3.11. Population variance unknown, t-score_exercise_solution.xlsx
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11.2 3.11. The t-table.xlsx
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11.3 3.11. Population variance unknown, t-score_exercise.xlsx
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12. What is a margin of error and why is it important in Statistics.mp4
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12. What is a margin of error and why is it important in Statistics.srt
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3. Confidence intervals - an invaluable tool for decision making.mp4
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3. Confidence intervals - an invaluable tool for decision making.srt
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5. Calculating confidence intervals within a population with a known variance.mp4
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5. Calculating confidence intervals within a population with a known variance.srt
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5.1 3.9.The-z-table.xlsx
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5.2 3.9. Population variance known, z-score_lesson.xlsx
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6.1 3.9. Population variance known, z-score_exercise.xlsx
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6.2 3.9.The-z-table.xlsx
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6.3 3.9. Population variance known, z-score_exercise_solution.xlsx
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7. Confidence interval clarifications.mp4
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7. Confidence interval clarifications.srt
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8. Student's T distribution.mp4
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8. Student's T distribution.srt
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1. Calculating confidence intervals for two means with dependent samples.mp4
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1. Calculating confidence intervals for two means with dependent samples.srt
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1.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx
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2.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx
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2.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx
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3. Calculating confidence intervals for two means with independent samples (part 1).mp4
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3. Calculating confidence intervals for two means with independent samples (part 1).srt
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3.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx
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4.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx
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4.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx
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5. Calculating confidence intervals for two means with independent samples (part 2).mp4
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5. Calculating confidence intervals for two means with independent samples (part 2).srt
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5.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx
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6.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx
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6.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx
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7. Calculating confidence intervals for two means with independent samples (part 3).mp4
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7. Calculating confidence intervals for two means with independent samples (part 3).srt
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1. Practical example inferential statistics.mp4
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1. Practical example inferential statistics.srt
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1.1 3.17. Practical example. Confidence intervals_lesson.xlsx
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2.1 3.17.Practical-example.Confidence-intervals-exercise.xlsx
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2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx
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