FreeAllCourse Com Udemy Statistics for Data Science and Business Analysis

/1. Introduction/

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

/10. Hypothesis testing Introduction/

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

/11. Hypothesis testing Let's start testing!/

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

/12. Practical example hypothesis testing/

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

/13. The fundamentals of regression analysis/

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

/14. Subtleties of regression analysis/

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

/15. Assumptions for linear regression analysis/

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

/16. Dealing with categorical data/

1. Dummy variables.mp4

1. Dummy variables.srt

1.1 5.20. Dummy variables_lesson.xlsx.xlsx

/17. Practical example regression analysis/

1. Practical example regression analysis.mp4

1. Practical example regression analysis.srt

1.1 5.21. Regression_Analysis_practical_example.xlsx.xlsx

/18. Bonus lecture/

1. Bonus lecture Next steps.html

/2. Sample or population data/

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

/3. The fundamentals of descriptive statistics/

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

/4. Measures of central tendency, asymmetry, and variability/

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

/5. Practical example descriptive statistics/

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

/6. Distributions/

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

/7. Estimators and estimates/

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

/8. Confidence intervals advanced topics/

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

/9. Practical example inferential statistics/

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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