perform a basic linear regression analysis using your dependent variable and sex/gender and race as the two independent variables (IV). You will follow the instructions in the text in chapter 17 to first create a dummy variable for both of your IVs. You will then follow the steps covered in the book:
Analyze - Regression - Linear
Explain your findings to your reader. You can use the example explained on page 311 for help.
MY DEPENDENT VARIABLE CAME FROM A GSS DATASET AND I USED NATDRUG...
HERE IS A CLASS MATES POST AS AN EXAMPLE OF WHAT MINE SHOULD LOOK LIKE BUT WITH NATDRUG. MY IV WAS HELPOTH IN COMPARED TO THE ASSIGNMENT. IM CONFUSED. SOMEONE PLEASE EXPLAIN THIS TO ME.
The coefficients table lets us know the relationship between our independent variables and dependent variable.
Hypothesis: The independent variable helps to predict the dependent variable.
Null Hypothesis: The independent variable does not help to predict the dependent variable.
The constant: 2.847
For “male” the p-value= .000, standardized coefficient beta is -.141, unstandardized coefficient beta is -.232.
For “white” the p-value= .63, standardized coefficient beta is .051, unstandardized coefficient beta is .098.
For the independent variable “male”, the p-value is .000 so p<.05. In other words the p-value is less than the level of significance at .05 (and also less than .01) therefore we reject the null hypothesis. The independent variable “male” helps to predict the dependent variable “fefam”. Knowing maleness helps to predict support for women to work outside the home.
For the independent variable “white”, the p-value is .63 so p>.05. In other words the p-value is greater than the level of significance at .05 (and also .01 obviously) therefore we accept the null hypothesis. The independent variable “white” does not help to predict the dependent variable “fefam”. Knowing “whiteness” (if one is white or not) does not help to predict support for women to work outside the home.
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