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Lecture 11, Python Demo for Distribution
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Data Analytics with Python
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- 1 Data Analytics with Python
- 2 Lec 1, Introduction to Data Analytics
- 3 Lec 2, Python Fundamentals -I
- 4 Lec 3, Python Fundamentals -II
- 5 Lec 4, Central Tendency and Dispersion - I
- 6 Lec 5, Central Tendency and Dispersion - II
- 7 Lec 6, Introduction to Probability-I
- 8 Lec 7, Introduction to Probability-II
- 9 Lec 8, Probability Distribution - I
- 10 Lec 9, Probability Distribution - II
- 11 Lec 10, Probability Distributions - III
- 12 Lecture 11, Python Demo for Distribution
- 13 Lec 12, Sampling and Sampling Distribution
- 14 Lec 13, Distribution of Sample Means, population, and variance
- 15 Lec 14: Confidence interval estimation: Single population - I
- 16 Lec 15, Confidence Interval Estimation: Single Population - II
- 17 Lec 16, Hypothesis Testing- I
- 18 Lec 17, Hypothesis testing- II
- 19 Lec 18, Hypothesis Testing-III
- 20 Lec 19, Errors in Hypothesis Testing
- 21 Lec 20, Hypothesis Testing about the Difference in Two Sample Means
- 22 Lec 21, Hypothesis testing : Two sample test -II
- 23 Lec 22, Hypothesis Testing: Two sample test - III
- 24 Lec 23, ANOVA- I
- 25 Lec 24, ANOVA- II
- 26 Lec 25, Post Hoc Analysis(Tukey’s test)
- 27 Lec 26, Randomize block design (RBD)
- 28 Lec 27, Two Way ANOVA
- 29 Lec 28, Linear Regression - I
- 30 Lec 29, Linear Regression - II
- 31 Lec 30, Linear Regression-III
- 32 Lec 31, Estimation, Prediction of Regression Model Residual Analysis
- 33 Lec 32, Estimation, Prediction of Regression Model Residual Analysis - II
- 34 Lec 33, MULTIPLE REGRESSION MODEL - I
- 35 Lec 34, MULTIPLE REGRESSION MODEL-II
- 36 Lec 35, Categorical variable regression
- 37 Lec 36, Maximum Likelihood Estimation- I
- 38 Lec 37, Maximum Likelihood Estimation-II
- 39 Lec 38, LOGISTIC REGRESSION- I
- 40 Lec 39, LOGISTIC REGRESSION-II
- 41 Lec 40, Linear Regression Model Vs Logistic Regression Model
- 42 Lec 41, Confusion matrix and ROC- I
- 43 Lec 42, Confusion Matrix and ROC-II
- 44 Lec 43, Performance of Logistic Model-III
- 45 Lec 44, Regression Analysis Model Building - I
- 46 Lec 45, Regression Analysis Model Building (Interaction)- II
- 47 Lec 46, Chi - Square Test of Independence - I
- 48 Lec 47, Chi-Square Test of Independence - II
- 49 Lec 48, Chi-Square Goodness of Fit Test
- 50 Lec 49, Cluster analysis: Introduction- I
- 51 Lec 50, Clustering analysis: part II
- 52 Lec 51, Clustering analysis: Part III
- 53 Lec 52, Cluster analysis: Part IV
- 54 Lec 53, Cluster analysis: Part V
- 55 Lec 54, K- Means Clustering
- 56 Lec 55, Hierarchical method of clustering -I
- 57 Lec 56, Hierarchical method of clustering- II
- 58 Lec 57, Classification and Regression Trees (CART : I)
- 59 Lec 58, Measures of attribute selection
- 60 Lec 59, Attribute selection Measures in CART : II
- 61 Lec 60, Classification and Regression Trees (CART) - III
