Statistic assignment
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Review resources and media programs related to multiple regression.
Multiple regression is an advancement of the simple linear regression. It is applied when predicting the values or variables based on the values of two variables or more. The variable to be predicted is known as a target variable, and the other variables used in predicting the dependent variable is called the independent variables.
If you are using the HS Long Survey Dataset, report the mean of X1Par1Edu.
The mean of the variable X1Par1Edu is -0.21
What is your research question?
The research question for this study is: can sex be used to predict the socio-economic status?
What is the null hypothesis for your question?
There is no relationship between sex and socio-economic status of the student.
What would the research design align with this question? Use 1-2 references
Quantitative research design is suitable for these variables are expressed in numerical form (Salkind, 2010). A descriptive research design is appropriate for finding the mean values for the socio-economic values (Bartolucci et al., n.d.). Analysis needs to be done on the values ascertain their relationship.
What dependent variable was used, and how is it measured?
Socio-economic status of the student.
What independent variable is used, and how is it measured?
Sex. Sex is used as an independent variable to predict the changes in the socio-economic status.
What other variables were added to the multiple regression models as controls?
Race
What is the justification for adding the variables?
Race of a person cannot be changed, therefore, it is an independent variable, it is also a nominal variable like sex there is no intrinsic order for these variables
If you found significance, what is the strength of the effect?
the significance level between the variables is minimal since the value of R2=0.538
Explain your results for a lay audience, explain what the answer to your research question is using 1-2 references.
Since the level of significance between the variable is low, it means the two variables are not related to each other. The change in the predictor variable does not influence the independent variable (Lee, 2012). The similarity between the two occurred by random chance, and it is statistically insignificant. (Montgomery et al., 2007). When the significance interval is very low, there is less chance the two variables are statistically relation, and therefore we can conclude there are not related.
References
Bartolucci, A., Singh, K., & Bae, S. Introduction to statistical analysis of laboratory data (pp. 1-2).
Lee, A. (2012). Linear Regression Analysis (pp. 19-20). Wiley.
Salkind, N. (2010). Encyclopedia of research design (pp. 9-10). Sage.
Montgomery, D., Peck, E., & Vining, G. (2007). Student solutions manual to accompany introduction to linear regression analysis. Wiley-Interscience.