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Title [GigaCourse.com] Udemy - Statistics for Data Science and Business Analysis
Category XXX
Size 2.77GB
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[GigaCourse.com].url 49B
1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx 146.51KB
1.1 2.7. Mean, median and mode_lesson.xlsx 10.49KB
1.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx 10.47KB
1.1 3.17. Practical example. Confidence intervals_lesson.xlsx 1.74MB
1.1 4.10.Hypothesis-testing-section-practical-example.xlsx 51.71KB
1.1 4.4. Test for the mean. Population variance known_lesson.xlsx 10.96KB
1.1 5.20. Dummy variables_lesson.xlsx 25.19KB
1.1 5.21. Regression_Analysis_practical_example.xlsx 1.44MB
1.1 Course notes_descriptive_statistics.pdf 482.21KB
1.1 Course notes_hypothesis_testing.pdf 656.44KB
1.1 Course notes_inferential statistics.pdf 382.32KB
1.1 Course notes_regression_analysis.pdf 270.06KB
1.1 Glossary.xlsx 19.97KB
1.1 Statistics Glossary.xlsx 20.26KB
1.2 Course notes_descriptive_statistics.pdf 482.21KB
1. Bonus lecture Next steps.html 3.52KB
1. Calculating confidence intervals for two means with dependent samples.mp4 70.50MB
1. Calculating confidence intervals for two means with dependent samples.srt 7.88KB
1. Decomposing the linear regression model - understanding its nuts and bolts.mp4 42.22MB
1. Decomposing the linear regression model - understanding its nuts and bolts.srt 4.17KB
1. Dummy variables.mp4 38.19MB
1. Dummy variables.srt 6.14KB
1. Introduction to inferential statistics.mp4 15.47MB
1. Introduction to inferential statistics.srt 1.62KB
1. Introduction to regression analysis.mp4 19.41MB
1. Introduction to regression analysis.srt 1.54KB
1. OLS assumptions.mp4 19.39MB
1. OLS assumptions.srt 3.03KB
1. Practical example.mp4 160.47MB
1. Practical example.srt 19.71KB
1. Practical example hypothesis testing.mp4 69.39MB
1. Practical example hypothesis testing.srt 8.10KB
1. Practical example inferential statistics.mp4 102.59MB
1. Practical example inferential statistics.srt 13.28KB
1. Practical example regression analysis.mp4 129.32MB
1. Practical example regression analysis.srt 17.45KB
1. Test for the mean. Population variance known.mp4 54.30MB
1. Test for the mean. Population variance known.srt 7.55KB
1. The main measures of central tendency mean, median and mode.mp4 37.12MB
1. The main measures of central tendency mean, median and mode.srt 5.58KB
1. The null and the alternative hypothesis.mp4 92.16MB
1. The null and the alternative hypothesis.srt 6.97KB
1. The various types of data we can work with.mp4 72.59MB
1. The various types of data we can work with.srt 5.89KB
1. Understanding the difference between a population and a sample.mp4 58.04MB
1. Understanding the difference between a population and a sample.srt 5.47KB
1. What does the course cover.mp4 68.63MB
1. What does the course cover.srt 5.60KB
1. Working with estimators and estimates.mp4 47.84MB
1. Working with estimators and estimates.srt 3.77KB
10.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.60KB
10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise.xlsx 12.02KB
10.1 3.11. Population variance unknown, t-score_lesson.xlsx 10.78KB
10.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.25KB
10.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.61KB
10.2 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.25KB
10.2 3.11. The t-table.xlsx 15.85KB
10.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 10.77KB
10. A4. No autocorrelation.html 159B
10. A geometrical representation of the linear regression model.html 160B
10. Calculating confidence intervals within a population with an unknown variance.mp4 32.19MB
10. Calculating confidence intervals within a population with an unknown variance.srt 5.14KB
10. Numerical variables. Using a frequency distribution table. Exercise.html 81B
10. Standard deviation and coefficient of variation. Exercise.html 81B
10. Test for the mean. Independent samples (Part 1).html 82B
10. The central limit theorem.html 159B
10. The multiple linear regression model.mp4 19.11MB
10. The multiple linear regression model.srt 3.35KB
11.1 2.11. Covariance_lesson.xlsx 24.92KB
11.1 2.5. The Histogram_lesson.xlsx 18.63KB
11.1 3.11. Population variance unknown, t-score_exercise_solution.xlsx 11.10KB
11.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx 9.31KB
11.1 5.6. Example_lesson.xlsx 23.54KB
11.2 3.11. The t-table.xlsx 15.85KB
11.3 3.11. Population variance unknown, t-score_exercise.xlsx 10.62KB
11. A5. No multicollinearity.mp4 26.59MB
11. A5. No multicollinearity.srt 4.62KB
11. A practical example - Reinforced learning.mp4 45.87MB
11. A practical example - Reinforced learning.srt 7.38KB
11. Calculating and understanding covariance.mp4 27.48MB
11. Calculating and understanding covariance.srt 4.77KB
11. Histogram charts.mp4 13.79MB
11. Histogram charts.srt 3.10KB
11. Population variance unknown. T-score. Exercise.html 81B
11. Standard error.mp4 22.77MB
11. Standard error.srt 1.95KB
11. Test for the mean. Independent samples (Part 2).mp4 36.39MB
11. Test for the mean. Independent samples (Part 2).srt 5.14KB
11. The multiple linear regression model.html 160B
12.1 2.11. Covariance_exercise.xlsx 20.23KB
12.1 5.12. Adjusted R-squared_lesson.xlsx 18.23KB
12.2 2.11. Covariance_exercise_solution.xlsx 29.51KB
12. A5. No multicollinearity.html 159B
12. Covariance. Exercise.html 81B
12. Histogram charts.html 160B
12. Standard error.html 160B
12. Test for the mean. Independent samples (Part 2).html 160B
12. The adjusted R-squared.mp4 43.71MB
12. The adjusted R-squared.srt 6.55KB
12. What is a margin of error and why is it important in Statistics.mp4 47.22MB
12. What is a margin of error and why is it important in Statistics.srt 6.15KB
13.1 2.12. Correlation_lesson.xlsx 24.99KB
13.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.54KB
13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289.12KB
13.2 2.5.The-Histogram-exercise-solution.xlsx 17.10KB
13.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.39KB
13.3 2.5. The Histogram_exercise.xlsx 15.50KB
13. Histogram charts. Exercise.html 81B
13. Margin of error.html 159B
13. Test for the mean. Independent samples (Part 2). Exercise.html 82B
13. The adjusted R-squared.html 159B
13. The correlation coefficient.mp4 29.41MB
13. The correlation coefficient.srt 4.60KB
14.1 2.6. Cross table and scatter plot.xlsx 26.12KB
14. Correlation.html 160B
14. Cross tables and scatter plots.mp4 39.81MB
14. Cross tables and scatter plots.srt 6.59KB
14. What does the F-statistic show us and why do we need to understand it.mp4 13.90MB
14. What does the F-statistic show us and why do we need to understand it.srt 2.55KB
15.1 2.12. Correlation_exercise_solution.xlsx 29.48KB
15.2 2.12. Correlation_exercise.xlsx 29.30KB
15. Correlation coefficient.html 81B
15. Cross Tables and Scatter Plots.html 160B
16.1 2.6. Cross table and scatter plot_exercise_solution.xlsx 40.44KB
16.2 2.6. Cross table and scatter plot_exercise.xlsx 16.28KB
16. Cross tables and scatter plots. Exercise.html 81B
2.1 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 120.28KB
2.1 2.7. Mean, median and mode_exercise_solution.xlsx 11.35KB
2.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx 13.74KB
2.1 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.82MB
2.1 3.2. What is a distribution_lesson.xlsx 19.46KB
2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 44.04KB
2.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx 11.22KB
2.2 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 146.38KB
2.2 2.7. Mean, median and mode_exercise.xlsx 10.87KB
2.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx 14.24KB
2.2 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.73MB
2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx 43.38KB
2.2 4.4. Test for the mean. Population variance known_exercise.xlsx 11.03KB
2.2 Course notes_inferential statistics.pdf 382.32KB
2. Confidence intervals. Two means. Dependent samples. Exercise.html 81B
2. Decomposition.html 159B
2. Download all resources.html 716B
2. Estimators and estimates.html 159B
2. Further reading on null and alternative hypotheses.html 2.29KB
2. Introduction.html 159B
2. Mean, median and mode. Exercise.html 81B
2. OLS assumptions.html 159B
2. Population vs sample.html 159B
2. Practical example descriptive statistics.html 81B
2. Practical example hypothesis testing.html 86B
2. Practical example inferential statistics.html 81B
2. Test for the mean. Population variance known. Exercise.html 86B
2. Types of data.html 159B
2. What is a distribution.mp4 61.62MB
2. What is a distribution.srt 5.76KB
3.1 2.8. Skewness_lesson.xlsx 34.63KB
3.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx 9.83KB
3.1 Course notes_regression_analysis.pdf 270.06KB
3.1 Online p-value calculator.pdf 1.15MB
3.2 5.2. Correlation and causation_lesson.xlsx 10.62KB
3. A1. Linearity.mp4 12.06MB
3. A1. Linearity.srt 2.36KB
3. Calculating confidence intervals for two means with independent samples (part 1).mp4 28.75MB
3. Calculating confidence intervals for two means with independent samples (part 1).srt 5.89KB
3. Confidence intervals - an invaluable tool for decision making.mp4 49.94MB
3. Confidence intervals - an invaluable tool for decision making.srt 3.03KB
3. Correlation and causation.mp4 25.57MB
3. Correlation and causation.srt 5.64KB
3. Levels of measurement.mp4 54.38MB
3. Levels of measurement.srt 4.58KB
3. Measuring skewness.mp4 19.42MB
3. Measuring skewness.srt 3.56KB
3. Null vs alternative.html 159B
3. What is a distribution.html 159B
3. What is R-squared and how does it help us.mp4 36.45MB
3. What is R-squared and how does it help us.srt 6.37KB
3. What is the p-value and why is it one of the most useful tools for statisticians.mp4 55.87MB
3. What is the p-value and why is it one of the most useful tools for statisticians.srt 5.01KB
4.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx 10.12KB
4.1 Course notes_hypothesis_testing.pdf 656.44KB
4.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx 9.83KB
4. A1. Linearity.html 160B
4. Confidence intervals.html 159B
4. Confidence intervals. Two means. Independent samples (Part 1). Exercise.html 81B
4. Correlation and causation.html 160B
4. Establishing a rejection region and a significance level.mp4 82.54MB
4. Establishing a rejection region and a significance level.srt 8.69KB
4. Levels of measurement.html 159B
4. p-value.html 159B
4. R-squared.html 159B
4. Skewness.html 160B
4. The Normal distribution.mp4 49.87MB
4. The Normal distribution.srt 4.98KB
5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 30.77KB
5.1 2.8. Skewness_exercise.xlsx 9.49KB
5.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx 9.52KB
5.1 3.9. Population variance known, z-score_lesson.xlsx 11.21KB
5.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.54KB
5.2 2.8. Skewness_exercise_solution.xlsx 19.78KB
5.2 3.9.The-z-table.xlsx 25.58KB
5. A2. No endogeneity.mp4 32.45MB
5. A2. No endogeneity.srt 5.24KB
5. Calculating confidence intervals for two means with independent samples (part 2).mp4 26.81MB
5. Calculating confidence intervals for two means with independent samples (part 2).srt 4.36KB
5. Calculating confidence intervals within a population with a known variance.mp4 78.22MB
5. Calculating confidence intervals within a population with a known variance.srt 9.08KB
5. Categorical variables. Visualization techniques for categorical variables.mp4 36.66MB
5. Categorical variables. Visualization techniques for categorical variables.srt 6.30KB
5. Rejection region and significance level.html 159B
5. Skewness. Exercise.html 81B
5. Test for the mean. Population variance unknown.mp4 40.26MB
5. Test for the mean. Population variance unknown.srt 5.64KB
5. The linear regression model made easy.mp4 50.99MB
5. The linear regression model made easy.srt 7.06KB
5. The Normal distribution.html 159B
5. The ordinary least squares setting and its practical applications.mp4 20.05MB
5. The ordinary least squares setting and its practical applications.srt 2.82KB
6.1 2.9. Variance_lesson.xlsx 10.08KB
6.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx 9.17KB
6.1 3.4. Standard normal distribution_lesson.xlsx 10.38KB
6.1 3.9.The-z-table.xlsx 25.58KB
6.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.63KB
6.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx 9.79KB
6.2 3.9. Population variance known, z-score_exercise.xlsx 10.83KB
6.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.34KB
6.3 3.9. Population variance known, z-score_exercise_solution.xlsx 11.16KB
6. A2. No endogeneity.html 159B
6. Categorical variables. Visualization Techniques.html 160B
6. Confidence intervals. Population variance known. Exercise.html 81B
6. Confidence intervals. Two means. Independent samples (Part 2). Exercise.html 81B
6. Measuring how data is spread out calculating variance.mp4 50.94MB
6. Measuring how data is spread out calculating variance.srt 7.43KB
6. Test for the mean. Population variance unknown. Exercise.html 86B
6. The linear regression model.html 159B
6. The ordinary least squares setting and its practical applications.html 160B
6. The standard normal distribution.mp4 22.51MB
6. The standard normal distribution.srt 3.87KB
6. Type I error vs Type II error.mp4 43.94MB
6. Type I error vs Type II error.srt 5.37KB
7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx 15.24KB
7.1 2.9. Variance_exercise.xlsx 10.83KB
7.1 4.7. Test for the mean. Dependent samples_lesson.xlsx 9.79KB
7.1 5.10.Regression-tables-lesson.xlsx 12.55KB
7.2 2.9. Variance_exercise_solution.xlsx 11.05KB
7.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289.12KB
7.3 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx 41.11KB
7. A3. Normality and homoscedasticity.mp4 39.97MB
7. A3. Normality and homoscedasticity.srt 6.67KB
7. Calculating confidence intervals for two means with independent samples (part 3).mp4 19.89MB
7. Calculating confidence intervals for two means with independent samples (part 3).srt 1.91KB
7. Categorical variables. Visualization techniques. Exercise.html 81B
7. Confidence interval clarifications.mp4 57.12MB
7. Confidence interval clarifications.srt 5.51KB
7. Studying regression tables.mp4 36.77MB
7. Studying regression tables.srt 6.03KB
7. Test for the mean. Dependent samples.mp4 50.44MB
7. Test for the mean. Dependent samples.srt 6.34KB
7. The standard normal distribution.html 160B
7. Type I error vs type II error.html 159B
7. Variance. Exercise.html 81B
7. What is the difference between correlation and regression.mp4 12.71MB
7. What is the difference between correlation and regression.srt 2.10KB
8.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx 10.97KB
8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx 11.44KB
8.1 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.04KB
8.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx 14.40KB
8.2 3.4.Standard-normal-distribution-exercise.xlsx 11.99KB
8.2 4.7. Test for the mean. Dependent samples_exercise.xlsx 12.80KB
8. A3. Normality and homoscedasticity.html 160B
8. Correlation vs regression.html 159B
8. Numerical variables. Using a frequency distribution table.mp4 25.84MB
8. Numerical variables. Using a frequency distribution table.srt 4.26KB
8. Standard deviation and coefficient of variation.mp4 45.21MB
8. Standard deviation and coefficient of variation.srt 6.01KB
8. Standard Normal Distribution. Exercise.html 81B
8. Student's T distribution.mp4 35.41MB
8. Student's T distribution.srt 4.23KB
8. Studying regression tables.html 160B
8. Test for the mean. Dependent samples. Exercise.html 86B
9.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx 9.63KB
9.1 5.10. Regression tables_exercise.xlsx 12.04KB
9.2 5.10. Regression tables_exercise_solution.xlsx 12.51KB
9. A4. No autocorrelation.mp4 25.88MB
9. A4. No autocorrelation.srt 4.49KB
9. A geometrical representation of the linear regression model.mp4 4.91MB
9. A geometrical representation of the linear regression model.srt 1.64KB
9. Numerical variables. Using a frequency distribution table.html 160B
9. Regression tables. Exercise.html 86B
9. Standard deviation.html 160B
9. Student's T distribution.html 159B
9. Test for the mean. Independent samples (Part 1).mp4 29.97MB
9. Test for the mean. Independent samples (Part 1).srt 5.26KB
9. Understanding the central limit theorem.mp4 62.90MB
9. Understanding the central limit theorem.srt 5.52KB
Readme.txt 962B
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