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 R-Square in depth, including how to interpret R-Square for significance. Together with in-depth 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,
You will Learn
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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?
Here you can Download PDF Files that consists of Examples and Exercise
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?
Sum of squares of residual.......... ?
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.
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 ....................?
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........?
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......................................?
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
F−test for Slope:
The ANOVA F−test can be used to test the null hypothesis H0: β =0 that Y is not linearly related of X in a regression equation......?
The variation can be measured by sum of squared deviations .
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?
sy. x is the standard error of estimate.?
t is the value of t with n−2 degrees of freedom.? and many more
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