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دانلود Udemy Statistics Fundamentals: Bundled

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عنوان اصلی : Statistics Fundamentals: Bundled

این مجموعه آموزش ویدیویی محصول موسسه آموزشی Udemy است که بر روی 1 حلقه دیسک ارائه شده و به مدت زمان 14 ساعت و 33 دقیقه در اختیار علاقه مندان قرار می گیرد.

در ادامه با برخی از سرفصل های درسی این مجموعه آموزش آشنا می شویم :


Introduction :
1-1 What is Statistics?
1-2 Types of Statistics
1-3 What is Data?
1-4 Stevens’ Typology
1-5 How to Distinguish?
1-6 Independent & Dependent Variables
Data

Descriptive Statistics :
2-0 Introduction
2-1 Display Data 1: Frequency Table
2-2 Display Data 2: Create Frequency Table with Python
2-3 Display Data 3: Stem and Leaf Diagram
2-4 Display Data 4: Stem and Leaf Diagram with Python
2-5 Display Data 5: Histogram
2-6 Display Data 6: Create Histograms with Python
2-7 Display Data 7: Dot Plot
2-8 Central Tendency 1: Mean
2-9 Central Tendency 2: Median
2-10 Central Tendency 3: Mode
2-11 Central Tendency 4: Mean Median & Mode with Python
2-12 Central Tendency 5: Geometric Mean
2-13 Central Tendency 6: Harmonic Mean
2-14 Central Tendency 7: Trimmed Mean
2-15 Central Tendency 8: Moving Average
2-16 Central Tendency 9: Expected Value
2-17 Central Tendency 10: Proportions for Binary Data
2-18 Central Tendency 11: Various Means with Python
2-19 Variability 1: What is Variability?
2-20 Variability 2: Range and Residual
2-21 Variability 3: Mean Absolute Deviation
2-22 Variability 4: Variance
2-23 Variability 5: Standard Deviation
2-24 Variability 6: Coefficient of Variation
2-25 Variability 7: Variability with Python
2-26 Relative Position 1: Percentile
2-28 Relative Position 3: The Empirical Rule
2-29 Relative Position 4: Chebyshev's Theorem
2-30 Relative Position 5: Relative Position with Python
2-31 Data Visualization 1: Why Visualization?
2-32 Data Visualization 2: Box Plot
2-33 Data Visualization 3: Box Plot with Python
2-34 Data Visualization 4: Bar Chart
2-35 Data Visualization 5: Bar Plot with Python
2-36 Data Visualization 6: Pie Chart
2-37 Data Visualization 7: Pie Chart with Python
2-38 Data Visualization 8: Line Plot
2-39 Data Visualization 9: Line Plot with Python
2-40 Data Visualization 10: Cross Tabulation Table
2-41 Data Visualization 11: Stacked Bar Chart
2-42 Data Visualization 12: Crosstab and Stacked Bar Chart with Python
2-43 Data Visualization 13: Mosaic Plot with Python
2-44 Data Visualization 14: Ternary Plot
2-45 Data Visualization 15 Ternary Plot with Python
Descriptive Statistics

Probability :
3-0 Introduction
3-1 Permutation & Combination 1: Factorial
3-2 Permutation & Combination 2: Permutation
3-3 Permutation & Combination 3: Combination
Permutation & Combination
3-4 Permutation & Combination 4: Permutation and Combination with Python
3-5 Set Theory 1: Experiment & Event
3-6 Set Theory 2: Set
3-7 Set Theory 3: Event & Element
3-8 Set Theory 4: Venn Diagram
3-9 Set Theory 5: Complementary Event
3-10 Set Theory 6: Intersection
3-11 Set Theory 7: Union
3-12 Set Theory 8: Set Difference
Set Theory
3-13 Set Theory 9: Set in Python
3-14 Probability Theory 1: What is Probability?
3-15 Probability Theory 2: Calculate Probability
3-16 Probability Theory 3: Combination & Probability
3-17 Probability Theory 4: Statistical Independence
3-18 Probability Theory 5: Expected Value
Probability Theory
3-19 Conditional Probability 1: What is Conditional Probability?
3-20 Conditional Probability 2: Statistical Independence
3-21 Conditional Probability 3: Multiplication Theorem
3-22 Conditional Probability 4: Simpson's Paradox
3-23 Conditional Probability 5: Conditional Probability with Python
3-24 Conditional Probability 6: Bayes' Theorem
3-25 Conditional Probability 7: Bayes' Theorem with Python
Conditional Probability

Probability Distribution :
4-0 Introduction
4-1 Random Variable
4-2 Discrete Probability Distribution
4-3 Continuous Probability Distribution
4-4 Probability Density Function
Probability Density Function
4-5 Cumulative Distribution Function
4-6 Expected Value of Random Variables
Expected Values of Random Variables
4-7 Variance of Random Variables
4-8 Find Variance from Expected Value
4-9 Additivity of Variance
Variance of Random Variables
4-10 Normal Distribution
4-11 Standard Normal Distribution
4-12 Standard Normal Distribution Table
4-13 Skewness & Kurtosis
Normal Distribution
4-14 Normal Distribution with Python
4-15 Binomial Distribution
4-16 Expected Value of Binomial Distribution
4-17 Variance of Binomial Distribution
Binomial Distribution
4-18 Binomial Distribution with Python
4-19 Poisson Distribution
4-20 Expected Value of Poisson Distribution
4-21 Variance of Poisson Distribution
4-22 Examples of Poisson Distribution
Poisson Distribution
4-23 Poisson Distribution with Python
4-24 Geometric Distribution
4-25 Expected Value of Geometric Distribution
4-26 Variance of Geometric Distribution
Geometric Distribution
4-27 Geometric Distribution with Python
4-28 Exponential Distribution
4-29 Expected Value of Exponential Distribution
4-30 Variance of Exponential Distribution
4-31 Memorylessness
Exponential Distribution
4-32 Exponential Distribution with Python
4-33 Discrete Uniform Distribution
4-34 Continuous Uniform Distribution
Uniform Distribution
4-35 Uniform Distribution with Python
4-36 Joint Probability Distribution

Sampling :
5-0 Introduction
5-1 Population and Sample
5-2 Complete Survey and Sampling Survey
Sampling Survey
5-3 Probability Sampling and Non-probability Sampling
5-4 Probability Sampling Methods
Sampling
5-5 Random Sampling with Python
5-6 Law of Large Numbers
5-7 Law of Large Numbers with Python
5-8 Central Limit Theorem
Law of Large Numbers & Central Limit Theorem
5-9 Central Limit Theorem with Python
5-10 Experimental and Observational Studies
5-11 Fisher’s Principle
Experiment Design

Estimation :
6-0 Introduction
6-1 What is Point Estimation?
6-2 Point Estimation of Population Mean
6-3 Unbiased Variance
6-4 Standard Error
Point Estimation
6-5 Point Estimation by Python
6-6 What is Interval Estimation?
6-7 Interval Estimation of Population Mean (Population Variance Known)
6-8 What is 95% Confidence Interval?
6-9 Sample Size and Confidence Interval
6-10 When Population Variance is Unknown . . . (t-distribution)
6-11 Interval Estimation of Population Mean (Population Variance Unknown)
Interval Estimation
6-12 Interval Estimation of Population Mean Difference
6-13 Interval Estimation of Population Proportion
6-14 Interval Estimation and Minimum Sample Size
6-15 Chi-Square Distribution
6-16 Properties of Chi-Square Distribution
6-17 Interval Estimation of Population Variance
Interval Estimation Part 2
6-18 Interval Estimation by Python

Hypothesis Testing :
7-0 Introduction
7-1 What is Hypothesis Testing?
7-2 Process of Hypothesis Testing
Null and Alternative Hypotheses
7-3 Significance Level
Significance Level
7-4 Test Statistic
7-5 One- and Two-Tailed Test
7-6 Hypothesis Testing for Population Mean
7-7 Hypothesis Testing for Population Mean with Python
7-8 Exercise Hypothesis Testing for Population Mean
7-9 Two-Sample t-Test
7-10 Two-Sample t-Test Dependent Sample with Python
7-11 Exercise Two-Sample t-Test Dependent Sample
7-12 Two-Sample t-Test Independent Sample
7-13 Two-Sample t-Test Independent Sample with Python
7-14 Exercise Two-Sample t-Test Independent Sample
7-15 Hypothesis Testing for Population Proportion
7-16 Hypothesis Testing for Population Proportion with Python
7-17 Exercise Hypothesis Testing for Population Proportion
7-18 Goodness of Fit Test
7-19 Goodness of Fit Test with Python
7-20 Exercise Goodness of Fit Test
7-21 Test of Independence
7-22 Test of Independence with Python
7-23 Exercise Test of Independence
7-24 Test of Population Proportion Difference
7-25 Test of Population Proportion Difference with Python
7-26 Exercise Test of Population Proportion Difference

Correlation & Regression :
8-0 Introduction
8-1 Scatter Plot
8-2 Correlation
8-3 Correlation Coefficient
8-4 Covariance
8-5 Correlation Coefficient Revisited
8-6 Exercise Correlation Coefficient
8-7 Test of Non-Correlation
8-8 Spurious Correlation
Correlation
8-9 Regression Analysis
8-10 Ordinary Least Squares
8-11 Ordinary Least Squares Math
8-12 The Difference between Correlation and Regression
Regression Analysis
8-13 Multiple Regression Analysis
8-14 Multiple Regression Analysis Math
8-15 Assumptions of Linear Regression
8-16 Hypothesis Testing in Multiple Regression Analysis
8-17 Coefficient of Determination
8-18 Residual Analysis
8-19 Multicollinearity
8-20 Variance Inflation Factor
8-21 F-test
8-22 Dummy Variable
Multiple Regression Analysis
8-23 Effect Size
8-24 Statistical Power
8-25 Correlation Analysis with Python
8-26 Regression Analysis with Python
8-27 Get Dummy Variables with Python

ANOVA :
9-0 Introduction
9-1 What is ANOVA?
9-2 F-Test
9-3 Example F-Test
9-4 One-Way ANOVA
One-Way ANOVA
9-5 Tukey’s HSD test
9-6 Assumptions in ANOVA
Tukey’s HSD and Assumptions in ANOVA
9-7 One-Way ANOVA with Python
9-8 Two-Way ANOVA
9-9 Two-Way ANOVA with Python

Congratulations! :
Congratulations!

مشخصات این مجموعه :
زبان آموزش ها انگلیسی روان و ساده
دارای آموزشهای ویدیویی و دسته بندی شده
ارائه شده بر روی 1 حلقه دیسک
مدت زمان آموزش 14 ساعت و 33 دقیقه !
محصول موسسه آموزشی Udemy