Udemy Statistics for Data Science and Business Analysis FreeAllCourse
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Udemy Statistics for Data Science and Business Analysis FreeAllCourse
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Udemy Statistics for Data Science and Business Analysis FreeAllCourse
1. What does the course cover.mp4 -
1. What does the course cover.srt -
1.1 Statistics Glossary.xlsx.xlsx -
2. Download all resources.html -
1. The null and the alternative hypothesis.mp4 -
1. The null and the alternative hypothesis.srt -
1.1 Course notes_hypothesis_testing.pdf.pdf -
2. Further reading on null and alternative hypotheses.html -
3. Null vs alternative.html -
4. Establishing a rejection region and a significance level.mp4 -
4. Establishing a rejection region and a significance level.srt -
4.1 Course notes_hypothesis_testing.pdf.pdf -
5. Rejection region and significance level.html -
6. Type I error vs Type II error.mp4 -
6. Type I error vs Type II error.srt -
7. Type I error vs type II error.html -
1. Test for the mean. Population variance known.mp4 -
1. Test for the mean. Population variance known.srt -
1.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx -
10. Test for the mean. Independent samples (Part 1).html -
10.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx -
10.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx -
11. Test for the mean. Independent samples (Part 2).mp4 -
11. Test for the mean. Independent samples (Part 2).srt -
11.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx -
12. Test for the mean. Independent samples (Part 2).html -
13. Test for the mean. Independent samples (Part 2). Exercise.html -
13.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx -
13.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx -
2. Test for the mean. Population variance known. Exercise.html -
2.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx -
2.2 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx -
3. What is the p-value and why is it one of the most useful tools for statisticians.mp4 -
3. What is the p-value and why is it one of the most useful tools for statisticians.srt -
3.1 Online p-value calculator.pdf.pdf -
4. p-value.html -
5. Test for the mean. Population variance unknown.mp4 -
5. Test for the mean. Population variance unknown.srt -
5.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx -
6. Test for the mean. Population variance unknown. Exercise.html -
6.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx -
6.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx -
7. Test for the mean. Dependent samples.mp4 -
7. Test for the mean. Dependent samples.srt -
7.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx -
8. Test for the mean. Dependent samples. Exercise.html -
8.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx -
8.2 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx -
9. Test for the mean. Independent samples (Part 1).mp4 -
9. Test for the mean. Independent samples (Part 1).srt -
9.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx -
1. Practical example hypothesis testing.mp4 -
1. Practical example hypothesis testing.srt -
1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx -
2. Practical example hypothesis testing.html -
2.1 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx -
2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx -
1. Introduction to regression analysis.mp4 -
1. Introduction to regression analysis.srt -
1.1 Course notes_regression_analysis.pdf.pdf -
10. A geometrical representation of the linear regression model.html -
11. A practical example - Reinforced learning.mp4 -
11. A practical example - Reinforced learning.srt -
11.1 5.6. Example_lesson.xlsx.xlsx -
2. Introduction.html -
3. Correlation and causation.mp4 -
3. Correlation and causation.srt -
3.1 Course notes_regression_analysis.pdf.pdf -
3.2 5.2. Correlation and causation_lesson.xlsx.xlsx -
4. Correlation and causation.html -
5. The linear regression model made easy.mp4 -
5. The linear regression model made easy.srt -
6. The linear regression model.html -
7. What is the difference between correlation and regression.mp4 -
7. What is the difference between correlation and regression.srt -
8. Correlation vs regression.html -
9. A geometrical representation of the linear regression model.mp4 -
9. A geometrical representation of the linear regression model.srt -
1. Decomposing the linear regression model - understanding its nuts and bolts.mp4 -
1. Decomposing the linear regression model - understanding its nuts and bolts.srt -
10. The multiple linear regression model.mp4 -
10. The multiple linear regression model.srt -
11. The multiple linear regression model.html -
12. The adjusted R-squared.mp4 -
12. The adjusted R-squared.srt -
12.1 5.12. Adjusted R-squared_lesson.xlsx.xlsx -
13. The adjusted R-squared.html -
14. What does the F-statistic show us and why do we need to understand it.mp4 -
14. What does the F-statistic show us and why do we need to understand it.srt -
2. Decomposition.html -
3. What is R-squared and how does it help us.mp4 -
3. What is R-squared and how does it help us.srt -
4. R-squared.html -
5. The ordinary least squares setting and its practical applications.mp4 -
5. The ordinary least squares setting and its practical applications.srt -
6. The ordinary least squares setting and its practical applications.html -
7. Studying regression tables.mp4 -
7. Studying regression tables.srt -
7.1 5.10.Regression-tables-lesson.xlsx.xlsx -
8. Studying regression tables.html -
9. Regression tables. Exercise.html -
9.1 5.10. Regression tables_exercise_solution.xlsx.xlsx -
9.2 5.10. Regression tables_exercise.xlsx.xlsx -
1. OLS assumptions.mp4 -
1. OLS assumptions.srt -
10. A4. No autocorrelation.html -
11. A5. No multicollinearity.mp4 -
11. A5. No multicollinearity.srt -
12. A5. No multicollinearity.html -
2. OLS assumptions.html -
3. A1. Linearity.mp4 -
3. A1. Linearity.srt -
4. A1. Linearity.html -
5. A2. No endogeneity.mp4 -
5. A2. No endogeneity.srt -
6. A2. No endogeneity.html -
7. A3. Normality and homoscedasticity.mp4 -
7. A3. Normality and homoscedasticity.srt -
8. A3. Normality and homoscedasticity.html -
9. A4. No autocorrelation.mp4 -
9. A4. No autocorrelation.srt -
1. Dummy variables.mp4 -
1. Dummy variables.srt -
1.1 5.20. Dummy variables_lesson.xlsx.xlsx -
1. Practical example regression analysis.mp4 -
1. Practical example regression analysis.srt -
1.1 5.21. Regression_Analysis_practical_example.xlsx.xlsx -
1. Bonus lecture Next steps.html -
1. Understanding the difference between a population and a sample.mp4 -
1. Understanding the difference between a population and a sample.srt -
1.1 Glossary.xlsx.xlsx -
1.2 Course notes_descriptive_statistics.pdf.pdf -
2. Population vs sample.html -
1. The various types of data we can work with.mp4 -
1. The various types of data we can work with.srt -
1.1 Course notes_descriptive_statistics.pdf.pdf -
10. Numerical variables. Using a frequency distribution table. Exercise.html -
10.1 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx -
10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx.xlsx -
10.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx -
10.2 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx.xlsx -
11. Histogram charts.mp4 -
11. Histogram charts.srt -
11.1 2.5. The Histogram_lesson.xlsx.xlsx -
12. Histogram charts.html -
13. Histogram charts. Exercise.html -
13.1 2.5. The Histogram_exercise.xlsx.xlsx -
13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf -
13.2 2.5.The-Histogram-exercise-solution.xlsx.xlsx -
13.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf -
13.3 2.5. The Histogram_exercise.xlsx.xlsx -
13.3 2.5.The-Histogram-exercise-solution.xlsx.xlsx -
14. Cross tables and scatter plots.mp4 -
14. Cross tables and scatter plots.srt -
14.1 2.6. Cross table and scatter plot.xlsx.xlsx -
15. Cross Tables and Scatter Plots.html -
16. Cross tables and scatter plots. Exercise.html -
16.1 2.6. Cross table and scatter plot_exercise.xlsx.xlsx -
16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx -
2. Types of data.html -
3. Levels of measurement.mp4 -
3. Levels of measurement.srt -
4. Levels of measurement.html -
5. Categorical variables. Visualization techniques for categorical variables.mp4 -
5. Categorical variables. Visualization techniques for categorical variables.srt -
5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx -
6. Categorical variables. Visualization Techniques.html -
7. Categorical variables. Visualization techniques. Exercise.html -
7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx -
7.1 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx -
7.2 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx -
7.2 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx -
7.3 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf -
8. Numerical variables. Using a frequency distribution table.mp4 -
8. Numerical variables. Using a frequency distribution table.srt -
8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx -
9. Numerical variables. Using a frequency distribution table.html -
1. The main measures of central tendency mean, median and mode.mp4 -
1. The main measures of central tendency mean, median and mode.srt -
1.1 2.7. Mean, median and mode_lesson.xlsx.xlsx -
10. Standard deviation and coefficient of variation. Exercise.html -
10.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx.xlsx -
10.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx.xlsx -
11. Calculating and understanding covariance.mp4 -
11. Calculating and understanding covariance.srt -
11.1 2.11. Covariance_lesson.xlsx.xlsx -
12. Covariance. Exercise.html -
12.1 2.11. Covariance_exercise_solution.xlsx.xlsx -
12.2 2.11. Covariance_exercise.xlsx.xlsx -
13. The correlation coefficient.mp4 -
13. The correlation coefficient.srt -
13.1 2.12. Correlation_lesson.xlsx.xlsx -
14. Correlation.html -
15. Correlation coefficient.html -
15.1 2.12. Correlation_exercise.xlsx.xlsx -
15.2 2.12. Correlation_exercise_solution.xlsx.xlsx -
2. Mean, median and mode. Exercise.html -
2.1 2.7. Mean, median and mode_exercise.xlsx.xlsx -
2.2 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx -
3. Measuring skewness.mp4 -
3. Measuring skewness.srt -
3.1 2.8. Skewness_lesson.xlsx.xlsx -
4. Skewness.html -
5. Skewness. Exercise.html -
5.1 2.8. Skewness_exercise.xlsx.xlsx -
5.2 2.8. Skewness_exercise_solution.xlsx.xlsx -
6. Measuring how data is spread out calculating variance.mp4 -
6. Measuring how data is spread out calculating variance.srt -
6.1 2.9. Variance_lesson.xlsx.xlsx -
7. Variance. Exercise.html -
7.1 2.9. Variance_exercise.xlsx.xlsx -
7.2 2.9. Variance_exercise_solution.xlsx.xlsx -
8. Standard deviation and coefficient of variation.mp4 -
8. Standard deviation and coefficient of variation.srt -
8.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx -
9. Standard deviation.html -
1. Practical example.mp4 -
1. Practical example.srt -
1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx -
2. Practical example descriptive statistics.html -
2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx -
2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx -
1. Introduction to inferential statistics.mp4 -
1. Introduction to inferential statistics.srt -
1.1 Course notes_inferential statistics.pdf.pdf -
10. The central limit theorem.html -
11. Standard error.mp4 -
11. Standard error.srt -
12. Standard error.html -
2. What is a distribution.mp4 -
2. What is a distribution.srt -
2.1 3.2. What is a distribution_lesson.xlsx.xlsx -
2.2 Course notes_inferential statistics.pdf.pdf -
3. What is a distribution.html -
4. The Normal distribution.mp4 -
4. The Normal distribution.srt -
5. The Normal distribution.html -
6. The standard normal distribution.mp4 -
6. The standard normal distribution.srt -
6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx -
7. The standard normal distribution.html -
8. Standard Normal Distribution. Exercise.html -
8.1 3.4.Standard-normal-distribution-exercise.xlsx.xlsx -
8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx -
9. Understanding the central limit theorem.mp4 -
9. Understanding the central limit theorem.srt -
1. Working with estimators and estimates.mp4 -
1. Working with estimators and estimates.srt -
10. Calculating confidence intervals within a population with an unknown variance.mp4 -
10. Calculating confidence intervals within a population with an unknown variance.srt -
10.1 3.11. The t-table.xlsx.xlsx -
10.2 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx -
11. Population variance unknown. T-score. Exercise.html -
11.1 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx -
11.2 3.11. The t-table.xlsx.xlsx -
11.3 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx -
12. What is a margin of error and why is it important in Statistics.mp4 -
12. What is a margin of error and why is it important in Statistics.srt -
13. Margin of error.html -
2. Estimators and estimates.html -
3. Confidence intervals - an invaluable tool for decision making.mp4 -
3. Confidence intervals - an invaluable tool for decision making.srt -
4. Confidence intervals.html -
5. Calculating confidence intervals within a population with a known variance.mp4 -
5. Calculating confidence intervals within a population with a known variance.srt -
5.1 3.9.The-z-table.xlsx.xlsx -
5.2 3.9. Population variance known, z-score_lesson.xlsx.xlsx -
6. Confidence intervals. Population variance known. Exercise.html -
6.1 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx -
6.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx -
6.3 3.9.The-z-table.xlsx.xlsx -
7. Confidence interval clarifications.mp4 -
7. Confidence interval clarifications.srt -
8. Student's T distribution.mp4 -
8. Student's T distribution.srt -
9. Student's T distribution.html -
1. Calculating confidence intervals for two means with dependent samples.mp4 -
1. Calculating confidence intervals for two means with dependent samples.srt -
1.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx -
2. Confidence intervals. Two means. Dependent samples. Exercise.html -
2.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx -
2.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx -
3. Calculating confidence intervals for two means with independent samples (part 1).mp4 -
3. Calculating confidence intervals for two means with independent samples (part 1).srt -
3.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx -
4. Confidence intervals. Two means. Independent samples (Part 1). Exercise.html -
4.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx -
4.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx -
5. Calculating confidence intervals for two means with independent samples (part 2).mp4 -
5. Calculating confidence intervals for two means with independent samples (part 2).srt -
5.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx -
6. Confidence intervals. Two means. Independent samples (Part 2). Exercise.html -
6.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx -
6.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx -
7. Calculating confidence intervals for two means with independent samples (part 3).mp4 -
7. Calculating confidence intervals for two means with independent samples (part 3).srt -
1. Practical example inferential statistics.mp4 -
1. Practical example inferential statistics.srt -
1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx -
2. Practical example inferential statistics.html -
2.1 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx -
2.2 3.17.Practical-example.Confidence-intervals-exercise.xlsx.xlsx -
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1. What does the course cover.mp4 -
68.6 MB
1. What does the course cover.srt -
5.9 KB
1.1 Statistics Glossary.xlsx.xlsx -
20.3 KB
2. Download all resources.html -
716 bytes
1. The null and the alternative hypothesis.mp4 -
92.1 MB
1. The null and the alternative hypothesis.srt -
7.0 KB
1.1 Course notes_hypothesis_testing.pdf.pdf -
656.4 KB
2. Further reading on null and alternative hypotheses.html -
2.3 KB
3. Null vs alternative.html -
159 bytes
4. Establishing a rejection region and a significance level.mp4 -
82.5 MB
4. Establishing a rejection region and a significance level.srt -
9.4 KB
4.1 Course notes_hypothesis_testing.pdf.pdf -
656.4 KB
5. Rejection region and significance level.html -
159 bytes
6. Type I error vs Type II error.mp4 -
43.9 MB
6. Type I error vs Type II error.srt -
5.4 KB
7. Type I error vs type II error.html -
159 bytes
1. Test for the mean. Population variance known.mp4 -
54.3 MB
1. Test for the mean. Population variance known.srt -
7.5 KB
1.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx -
11.0 KB
10. Test for the mean. Independent samples (Part 1).html -
82 bytes
10.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx -
11.3 KB
10.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx -
10.8 KB
11. Test for the mean. Independent samples (Part 2).mp4 -
36.4 MB
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.xlsx -
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12. Test for the mean. Independent samples (Part 2).html -
160 bytes
13. Test for the mean. Independent samples (Part 2). Exercise.html -
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2. Test for the mean. Population variance known. Exercise.html -
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11.0 KB
3. What is the p-value and why is it one of the most useful tools for statisticians.mp4 -
55.9 MB
3. What is the p-value and why is it one of the most useful tools for statisticians.srt -
55.9 MB
3.1 Online p-value calculator.pdf.pdf -
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4. p-value.html -
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5. Test for the mean. Population variance unknown.mp4 -
40.2 MB
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.xlsx -
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6. Test for the mean. Population variance unknown. Exercise.html -
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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.xlsx -
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8. Test for the mean. Dependent samples. Exercise.html -
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9. Test for the mean. Independent samples (Part 1).mp4 -
30.0 MB
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.xlsx -
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1. Practical example hypothesis testing.mp4 -
69.4 MB
1. Practical example hypothesis testing.srt -
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1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx -
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1. Introduction to regression analysis.mp4 -
19.4 MB
1. Introduction to regression analysis.srt -
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1.1 Course notes_regression_analysis.pdf.pdf -
270.1 KB
10. A geometrical representation of the linear regression model.html -
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11. A practical example - Reinforced learning.mp4 -
45.9 MB
11. A practical example - Reinforced learning.srt -
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11.1 5.6. Example_lesson.xlsx.xlsx -
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2. Introduction.html -
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3. Correlation and causation.mp4 -
25.6 MB
3. Correlation and causation.srt -
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3.1 Course notes_regression_analysis.pdf.pdf -
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3.2 5.2. Correlation and causation_lesson.xlsx.xlsx -
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4. Correlation and causation.html -
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5. The linear regression model made easy.mp4 -
51.0 MB
5. The linear regression model made easy.srt -
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6. The linear regression model.html -
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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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8. Correlation vs regression.html -
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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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11. The multiple linear regression model.html -
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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.xlsx -
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13. The adjusted R-squared.html -
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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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2. Decomposition.html -
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3. What is R-squared and how does it help us.mp4 -
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5. The ordinary least squares setting and its practical applications.mp4 -
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7. Studying regression tables.mp4 -
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7.1 5.10.Regression-tables-lesson.xlsx.xlsx -
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10. A4. No autocorrelation.html -
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3. A1. Linearity.mp4 -
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1. Dummy variables.mp4 -
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1.1 5.20. Dummy variables_lesson.xlsx.xlsx -
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1. Practical example regression analysis.mp4 -
129.3 MB
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1.1 5.21. Regression_Analysis_practical_example.xlsx.xlsx -
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1. Bonus lecture Next steps.html -
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1. Understanding the difference between a population and a sample.mp4 -
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1.1 Glossary.xlsx.xlsx -
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1.2 Course notes_descriptive_statistics.pdf.pdf -
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1. The various types of data we can work with.mp4 -
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1.1 Course notes_descriptive_statistics.pdf.pdf -
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10. Numerical variables. Using a frequency distribution table. Exercise.html -
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11. Histogram charts.mp4 -
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11.1 2.5. The Histogram_lesson.xlsx.xlsx -
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13.1 2.5. The Histogram_exercise.xlsx.xlsx -
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13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf -
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13.3 2.5. The Histogram_exercise.xlsx.xlsx -
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14. Cross tables and scatter plots.mp4 -
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14.1 2.6. Cross table and scatter plot.xlsx.xlsx -
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2. Types of data.html -
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3. Levels of measurement.mp4 -
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5. Categorical variables. Visualization techniques for categorical variables.mp4 -
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7.3 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf -
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8. Numerical variables. Using a frequency distribution table.mp4 -
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1. The main measures of central tendency mean, median and mode.mp4 -
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11. Calculating and understanding covariance.mp4 -
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11.1 2.11. Covariance_lesson.xlsx.xlsx -
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12. Covariance. Exercise.html -
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13. The correlation coefficient.mp4 -
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14. Correlation.html -
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2. Mean, median and mode. Exercise.html -
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2.1 2.7. Mean, median and mode_exercise.xlsx.xlsx -
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3. Measuring skewness.mp4 -
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6. Measuring how data is spread out calculating variance.mp4 -
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8. Standard deviation and coefficient of variation.mp4 -
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1. Practical example.mp4 -
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1. Introduction to inferential statistics.mp4 -
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1.1 Course notes_inferential statistics.pdf.pdf -
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10. The central limit theorem.html -
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11. Standard error.mp4 -
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12. Standard error.html -
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2. What is a distribution.mp4 -
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2.1 3.2. What is a distribution_lesson.xlsx.xlsx -
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4. The Normal distribution.mp4 -
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6. The standard normal distribution.mp4 -
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9. Understanding the central limit theorem.mp4 -
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1. Working with estimators and estimates.mp4 -
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10. Calculating confidence intervals within a population with an unknown variance.mp4 -
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10.1 3.11. The t-table.xlsx.xlsx -
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12. What is a margin of error and why is it important in Statistics.mp4 -
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13. Margin of error.html -
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2. Estimators and estimates.html -
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3. Confidence intervals - an invaluable tool for decision making.mp4 -
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5. Calculating confidence intervals within a population with a known variance.mp4 -
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7. Confidence interval clarifications.mp4 -
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8. Student's T distribution.mp4 -
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1. Calculating confidence intervals for two means with dependent samples.mp4 -
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3. Calculating confidence intervals for two means with independent samples (part 1).mp4 -
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1. Practical example inferential statistics.mp4 -
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Read Me.txt -
1.0 KB
[FreeAllCourse.Com].url -
52 bytes
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