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Welcome to the course on "Simple Linear Regression Analysis ( A Complete Course )" This course covers running and evaluating linear regression models (simple linear regression) including assessing the overall quality of models and interpreting individual predictors for significance with PDF files and complete exercises that consists of examples and concepts . We also explore RSquare in depth, including how to interpret RSquare for significance. Together with indepth coverage of simple regression, we'll also explore correlation, which is closely related to regression analysis. By the end of this course you will be skilled in running and interpreting your own linear regression analyses, as well as critically evaluating the work of others. Lectures provided in HD video .While you can be confident that you are getting accurate information with Quantitative Specialists,
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Lecture 1 
Introduction to course
Preview

01:47  
Section 1: Basic Concepts of Simple Linear Regression Model with terminologies and Examples  

Lecture 2  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? 

Quiz 1 
Quiz

1 question  
Lecture 3  06:03  


Lecture 4 
Examples with Interpretation

03:38  
Quiz 2 
Quiz

1 question  
Lecture 5  00:24  
Here you can Download PDF Files that consists of Examples and Exercise 

Section 2: Properties of Regression analysis , Prediction , Graphic Explanation , Etc  
Lecture 6 
Graphic Explanation

03:35  
Lecture 7 
Properties of Regression analysis

01:41  
Quiz 3 
Quiz

1 question  
Lecture 8  01:49  
Total Variation, Explained Variation and Unexplained Variation
A measure of the variation of the actual values of the dependent variable from the estimated values of that variable? Symbolically, Sum of squares of residual.......... ? 

Lecture 9  02:41  
Prediction in Regression Analysis Here we will discuss how we may use a regression line to summarize the pattern but we will put less confidence in prediction based on the line. 

Section 3: Intermediate concepts of Simple Linear Regression Model  
Lecture 10 
Standard Error of Estimate

06:44  
Lecture 11 
Coefficient of determination

05:16  
Quiz 4 
Quiz

1 question  
Lecture 12  05:22  


Lecture 13 
Get PDF File ( For Additional Help )

00:24  
Lecture 14  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 ....................? 

Quiz 5 
Quiz

1 question  
Section 4: Properties of coefficient of correlation , Relationship between Coefficient Etc  
Lecture 15 
Properties of coefficient of correlation

02:12  
Lecture 16 
Correlation and Causation

01:02  
Lecture 17  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........? .............? 

Quiz 6 
Quiz

2 questions  
Lecture 18 
Get PDF File ( For More Practice )

00:24  
Lecture 19  01:57  
Interpolation Verses Extrapolation: We will learn how Interpolation is the process of finding the value of the response variable Y for a given value of the explanatory variable X which lies within its given range of values. Whereas extrapolation is the......................................? 

Section 5: Testing of Hypothesis about Regression Coefficient , ANOVA Table and F.Test Etc  
Lecture 20 
Test the significance of coefficient of correlation

05:27  
Lecture 21  02:21  
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 22 
Examples of testing of hypothesis about regreession coefficient
Preview

02:39  
Quiz 7 
Quiz

1 question  
Lecture 23  02:04  


Lecture 24  02:08  
F−test for Slope: The ANOVA F−test can be used to test the null hypothesis H_{0}: β =0 that Y is not linearly related of X in a regression equation......? The variation can be measured by sum of squared deviations . 

Section 6: Confidence interval and prediction interval , Examples of confiddence in......??  
Lecture 25 
Get PDF File ( For More Practice )

00:24  
Lecture 26  01:52  
Able to learn what is the predicted value for any selected X value.? X is any selected value of X. X̅ is the mean of the Xs found by ∑ X/n n is the number of observations? s_{y. x} is the standard error of estimate.? t is the value of t with n−2 degrees of freedom.? and many more 

Lecture 27 
Examples of confiddence interval and prediction interval

02:46  
Quiz 8 
Quiz

1 question  
Lecture 28 
Covariance

02:10  
Lecture 29 
Complete Exercise ( Download PDF file )

00:24 
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