Please give me your personal comments per video and no handwritten responses ple
ID: 3238328 • Letter: P
Question
Please give me your personal comments per video and no handwritten responses please. Thank you.
1) Multiple Regression Analysis (Four videos)
Intro to Multiple Regression - Intro to Inferential Statistics (2:58)
https://www.youtube.com/watch?v=sFdKnpfQBW8
by Udacity
2) Multiple Regression Interpretation in Excel (6:32)
https://www.youtube.com/watch?v=tlbdkgYz7FM
by TheWoundedDoctor
3) Multiple Regression Analysis (6:32)
St. Cloud State University uploaded on Aug 20, 2011
This video shows you how run a multivariate linear regression in Excel. It shows how to write a regression equation.
Identifying and analyzing more than one independent variables (IV) can lead to better explained variation (R Squared).
Notice. Significance F shows that there is statistical significance. As a result, Ho would be rejected. Now, observe the P-value for Price and Income. Price P-value = .96. This IV would not reject Ho. In practice, we would run the regression analysis again but leave out Price.
https://www.youtube.com/watch?v=TkiB1xBnjn4
4) Multiple Regression (7:27)
https://www.coursera.org/learn/wharton-quantitative-modeling/lecture/9u0z4/4-6-multiple-regression
by Richard Waterman, University of Pennsylvania, Produced by Coursera
Explanation / Answer
Solution:
In this video they have showed one example as, student effort in math class depends on how they value math, how much they enjoy math and how it is related to teachers.
So multiple regression equation is ,
Student effort in Math class = value * x1 + enjoy* x2 + teachers * x3
In any multiple regression analysis output we also get two terms as R and R^2.
R is multiple regression coefficient and R^2 is coefficient of determination.
The Correlation Coefficient R tells you whether a relationship exists between the variables
s, how strong that relationship is and whether the relationship is positive or negative.
And R^2 tells us that, proportion of variability in dependent variable y explained by independent variables.