Multiple Linear Regression - Revision history

Strungadi: /* Multiple Linear Regression interpreting results example */

2019-12-04T17:11:04Z

‎Multiple Linear Regression interpreting results example

Strungadi: /* Multiple Linear Regression interpreting results example */

2019-12-04T17:09:58Z

‎Multiple Linear Regression interpreting results example

Strungadi at 17:08, 4 December 2019

2019-12-04T17:08:32Z

Admin at 15:06, 2 December 2019

2019-12-02T15:06:20Z

Admin at 15:05, 2 December 2019

2019-12-02T15:05:52Z

Admin at 22:25, 19 November 2019

2019-11-19T22:25:28Z

Sheri.prendergast at 21:15, 7 November 2019

2019-11-07T21:15:04Z

Foxthom4: Created page with "Multiple linear regression (multiple regression) is a type of correlational test in which the research is interested in finding the strength of a correlation between multiple..."

2019-10-09T16:06:52Z

Created page with "Multiple linear regression (multiple regression) is a type of correlational test in which the research is interested in finding the strength of a correlation between multiple..."

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Multiple linear regression (multiple regression) is a type of correlational test in which the research is interested in finding the strength of a correlation between multiple variables. In multiple linear regression, multiple variables are used as ''predictors. '''Here, the researcher is interested in the relationship between the predicted variables (dependent) and predictor variables (also known as the independent variables).
''

Independent variables in multiple regression are usually quantitatively measured variables using summative response, interval, or ratio scales (Lawrence, Meyer, & Guarino, 2017)

Multiple Linear Regression uses the same general equation as linear regression, but accommodates for multiple IV's.

Contribution by: Thomas Fox, WCSU Cohort 8

Reference

Lawrence, S., Meyer, G, & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications

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 Sample report interpreting results:
-Multiple linear regression was conducted with college readiness as the criterion variable and self regulation, engagement in reading, household income and population density as predictor variables.  The model was significant F(4,45) = 17.88, p<.001.  Together, the variables in the model explained 61.4% of the variation in parent income, f2=[.614/.386], 1.59. Household income contributed significantly to the prediction of college readiness p<.001 while self regulation, engagement, and population density did not.
+Multiple linear regression was conducted with college readiness as the criterion variable and self regulation, engagement in reading, household income and population density as predictor variables.  The model was significant F(4,45) = 17.88, p<.001.  Together, the variables in the model explained 61.4% of the variation in parent income, f^2=[.614/.386], 1.59. Household income contributed significantly to the prediction of college readiness p<.001 while self regulation, engagement, and population density did not.
 ''contributed by Scott Trungadi, WCSU Cohort 8''

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 ==Multiple Linear Regression interpreting results example==
 Research Question:
 To what extent and in what manner can variation in college readiness be explained by self regulation, engagement in reading, household income, and population density?

← Older revision		Revision as of 17:08, 4 December 2019
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	Lawrence, S., Meyer, G, & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications		Lawrence, S., Meyer, G, & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications

		+	==Multiple Linear Regression interpreting results example==
		+	Research Question:
		+	To what extent and in what manner can variation in college readiness be explained by self regulation, engagement in reading, household income, and population density?

		+	Independent Variables: Self regulation, Engagement in reading, Household income, population density
		+	Dependent Variable: College Readiness
		+
		+	Sample report interpreting results:
		+	Multiple linear regression was conducted with college readiness as the criterion variable and self regulation, engagement in reading, household income and population density as predictor variables. The model was significant F(4,45) = 17.88, p<.001. Together, the variables in the model explained 61.4% of the variation in parent income, f2=[.614/.386], 1.59. Household income contributed significantly to the prediction of college readiness p<.001 while self regulation, engagement, and population density did not.
		+
		+	''contributed by Scott Trungadi, WCSU Cohort 8''

	==Collinearity==		==Collinearity==

← Older revision		Revision as of 15:06, 2 December 2019
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−	~~Contribution~~ by: Thomas Fox, WCSU Cohort 8	+	''contributed by Thomas Fox, WCSU Cohort 8''

	Reference		Reference

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----------
-Collinearity
+==Collinearity==
 When conducting a multiple linear regression, you to see if the data meets the assumption of collinearity.  Therefore, you need to locate the Coefficients table in your results under the heading Collinearity Statistics, under which are two subheadings, Tolerance and VIF.

@@ Line 17: / Line 17: @@
 Collinearity
-When conducting a multiple linear regressions, you to see if the data meets the assumption of collinearity.  Therefore, you need to locate the Coefficients table in your results under the heading Collinearity Statistics, under which are two subheadings, Tolerance and VIF.
+When conducting a multiple linear regression, you to see if the data meets the assumption of collinearity.  Therefore, you need to locate the Coefficients table in your results under the heading Collinearity Statistics, under which are two subheadings, Tolerance and VIF.
 If the VIF value is greater than 10, or the Tolerance is less than 0.1, then you have concerns over multicollinearity. Otherwise, your data has met the assumption of collinearity and can be written up something like this:
-Contribution by: Sheri Prendergast, WCSU Cohort 8
+''contributed by Sheri Prendergast, WCSU Cohort 8''
 Dart, A., (2013).  Reporting Multiple Regressions in APA format-Part One. Retrieved from:  http://www.adart.myzen.co.uk/reporting-multiple-regressions-in-apa-format-part-one/

← Older revision		Revision as of 21:15, 7 November 2019
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	Lawrence, S., Meyer, G, & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications		Lawrence, S., Meyer, G, & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications
		+
		+
		+	---------
		+	Collinearity
		+
		+	When conducting a multiple linear regressions, you to see if the data meets the assumption of collinearity. Therefore, you need to locate the Coefficients table in your results under the heading Collinearity Statistics, under which are two subheadings, Tolerance and VIF.
		+
		+	If the VIF value is greater than 10, or the Tolerance is less than 0.1, then you have concerns over multicollinearity. Otherwise, your data has met the assumption of collinearity and can be written up something like this:
		+
		+	Contribution by: Sheri Prendergast, WCSU Cohort 8
		+
		+	Dart, A., (2013). Reporting Multiple Regressions in APA format-Part One. Retrieved from: http://www.adart.myzen.co.uk/reporting-multiple-regressions-in-apa-format-part-one/