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Welcome to the course on "Workshop in Probability and Statistics from Start to Finish" This workshop is designed to help you make sense of basic probability and statistics with easytounderstand explanations of all the subject's most important concepts. Whether you are starting from scratch or if you are in a statistics class and struggling with your assigned textbook or lecture material, this workshop was built with you in mind. In this workshop you will learn about Probability , Hypothesis Testing , Confidence Interval , Simple and Linear Regression Models , Sampling or survey sampling with complete Exercises , and PDF materials also provided for additional help .
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Lecture 1 
Introduction to course
Preview

09:49  
Section 1: Basic Probability and Statistics  

Lecture 2 
What is Set , Subset , Infinite and Finite Set , Universal and Null Set Etc Etc
Preview

05:27  
Lecture 3 
Venn Diagram , Union of two sets , Intersection of two sets , Disjoint Set

02:56  
Lecture 4 
Overlaping sets , Complement of a Set , Difference of two sets

02:06  
Lecture 5 
What is Experiment , Trial ,Outcome , Random Experiment Sample Space?

03:39  
Lecture 6 
Event , Simple Event , Composite or Compound Event and Equally Likely Events

03:23  
Lecture 7 
Mutually Exclusive Events , Collectively Events , Independent & Dependent Events

04:01  
Lecture 8 
Define Probability with Examples?

04:18  
Lecture 9 
Find the Probability when Even or Odd numbers are rolled

03:48  
Lecture 10 
Rules of Probability , Rules of Multiplication and Addition

03:16  
Lecture 11 
Conditional Probability

03:03  
Lecture 12 
Rules of Multiplication with Examples

01:57  
Section 2: Hypothesis testing  
Lecture 13 
What is Hypothesis , Statistical Hypothesis and Testing of Hypothesis?
Preview

01:58  
Lecture 14 
Null Hypothesis and Alternative Hypothesis

01:58  
Lecture 15 
Degree of Freedom , General Procedure for Testing of Hypothesis

01:05  
Lecture 16 
Example 1

05:15  
Lecture 17 
Example II  Is this a one or two tailed test.....??
Preview

03:09  
Lecture 18 
Example of Testing Hypothesis about Population Proportion (Sample Size is Large)

03:15  
Lecture 19 
Examples of Population Mean: Small Sample, Population Standard Deviation.....?

07:47  
Lecture 20 
Test Statistic for the Difference between Two Means

04:26  
Lecture 21 
Two Sample Tests about Proportions
Preview

07:14  
Lecture 22 
Exercise

00:24  
Section 3: Confidence Interval  
Lecture 23 
What is Estimate , Estimator and Estimation?

03:47  
Lecture 24 
Confidence interval and prediction interval

04:13  
Lecture 25 
Normal population with sigma known

06:23  
Lecture 26  03:21  
A random sample of size ,,,,,,,,, is taken from a normal population with a known variance. If the mean of the sample is ....... find 95% confidence limits for the population mean. ? 

Lecture 27 
What is Student TDistribution?

02:16  
Lecture 28 
Confidence interval for difference of means

03:22  
Lecture 29 
Examples of confidence interval for difference of means

05:18  
Lecture 30 
Any population variance known/unknown large samples

05:17  
Lecture 31 
Confidence Interval for population proportion

03:55  
Lecture 32  02:56  
Confidence Interval for Difference between Two Population Proportions, π_{1}−π_{2} 

Lecture 33 
Exercise

00:24  
Section 4: Simple Linear Regression Analysis  
Lecture 34  03:01  
The least squares principle is a criterion for fitting a specified model to observed data such that the sum of squares of the residuals (difference between observed and estimate value) is minimized? in detail. The estimated model can be written? 

Lecture 35  06:03  


Lecture 36 
Examples with Interpretation

03:38  
Lecture 37 
Standard Error of Estimate

06:44  
Lecture 38 
Coefficient of determination

05:16  
Lecture 39  05:22  


Lecture 40  04:27  
Correlation Analysis Pearson’s Product Moment Correlation Coefficient: Pearson’s product moment correlation coefficient, usually denoted by r, is one example of a correlation coefficient. It is a measure of the linear association ....................? 

Lecture 41  04:21  
The Relationship among the Coefficient of Correlation, the Coefficient of Determination and the S.E of Estimate: Sum of square of regression =S.S.R =Regression=Explained variation........? .............? 

Lecture 42 
Test the significance of coefficient of correlation

05:27  
Lecture 43  02:20  
Inferences about the Slope Coefficient: The inferences about the regression coefficient can be made by t−test for the slope and F−test for the slope. Testing Hypotheses about Regression Coefficient 

Lecture 44 
Exercise

00:24  
Section 5: Multiple Regression Model  
Lecture 45 
What is Multiple Regression?

03:18  
Lecture 46 
Estimation of Multiple Linear Regression Model with Two Explanatory Variables ..

02:29  
Lecture 47 
Introducing the formulas of Multiple Regression in deviation form

03:25  
Lecture 48 
Solving the model of Multiple Regression model with example

10:06  
Lecture 49 
What is Multiple Standard Error of Estimate?

03:14  
Lecture 50 
How to find the values of Multiple standard error of estimate?

04:26  
Lecture 51 
What is Coefficient of Multiple Determination?

02:18  
Lecture 52 
How to find the values of coefficient of Multiple determination?

03:58  
Lecture 53 
What is Adjusted Coefficient of Multiple Determinations?

03:16  
Lecture 54 
How to find Adjusted Coefficient of Multiple Determination?

02:11  
Lecture 55 
Exercise

00:24  
Section 6: Sampling and Sample Survey  
Lecture 56 
What is population? Sample and Sampling

01:36  
Lecture 57 
Advantages of Sampling

02:09  
Lecture 58 
Sampling design and sampling Survey

03:46  
Lecture 59 
Sampling Distribution of the Sample Mean

05:54  
Lecture 60 
Sampling Distribution when sample size is 3

03:53  
Lecture 61 
What is Standard Error? Explanation with Examples

03:25  
Lecture 62 
Sampling Distribution with Replacement

06:15  
Lecture 63 
Sampling Distribution of the Difference between Means

01:58  
Lecture 64 
Example of Sampling Distribution of the Difference between Means Part II

09:29  
Lecture 65 
Example of Sampling Distribution of the Difference between Means Part II

03:24  
Lecture 66 
Exercise

00:24  
Section 7: Analysis of Variance  
Lecture 67 
The F Distribution and Characteristics of the F Distribution

02:08  
Lecture 68 
Example one

03:07  
Lecture 69 
ANOVA Test and Assumptions

05:12  
Lecture 70 
Example Four

07:57  
Lecture 71 
Inferences about Pairs of Treatment Means

02:10  
Lecture 72 
Example of Inferences about Pairs of Treatment Means & difference of means

02:10  
Lecture 73 
Two−Way Analysis of Variance

01:15  
Lecture 74 
Example of Two−Way Analysis of Variance (part I)

07:28  
Lecture 75 
Example of Two−Way Analysis of Variance (part II)

04:20  
Lecture 76 
Exercise

00:24 
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