Explore BrainMass

Explore BrainMass

    Brief Lab Summary

    This content was COPIED from BrainMass.com - View the original, and get the already-completed solution here!

    Question 1
    Group Statistics
    HIGHEST YEAR OF SCHOOL COMPLETED N Mean Std. Deviation Std. Error Mean
    RESPONDENTS INCOME 12 283 12.24 18.415 1.095
    16 161 17.57 21.854 1.722

    Independent Samples Test
    Levene's Test for Equality of Variances t-test for Equality of Means
    F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference
    Lower Upper
    RESPONDENTS INCOME Equal variances assumed 1.340 .248 -2.734 442 .007 -5.325 1.948 -9.153 -1.497
    Equal variances not assumed -2.609 288.642 .010 -5.325 2.041 -9.342 -1.308

    Question 2
    NUMBER OF CHILDREN Pearson Correlation 1 -.111**
    Sig. (2-tailed) .001
    N 955 955
    RS HIGHEST DEGREE Pearson Correlation -.111** 1
    Sig. (2-tailed) .001
    N 955 955
    **. Correlation is significant at the 0.01 level (2-tailed).
    Question 3

    Variables Entered/Removeda
    Model Variables Entered Variables Removed Method
    1 RS HIGHEST DEGREEb . Enter
    a. Dependent Variable: NUMBER OF CHILDREN
    b. All requested variables entered.

    Model Summary
    Model R R Square Adjusted R Square Std. Error of the Estimate
    1 .111a .012 .011 1.666
    a. Predictors: (Constant), RS HIGHEST DEGREE

    Model Sum of Squares df Mean Square F Sig.
    1 Regression 32.852 1 32.852 11.837 .001b
    Residual 2645.043 953 2.775
    Total 2677.895 954
    a. Dependent Variable: NUMBER OF CHILDREN
    b. Predictors: (Constant), RS HIGHEST DEGREE
    Model Unstandardized Coefficients Standardized Coefficients t Sig.
    B Std. Error Beta
    1 (Constant) 2.232 .089 25.152 .000
    RS HIGHEST DEGREE -.153 .045 -.111 -3.440 .001
    a. Dependent Variable: NUMBER OF CHILDREN
    Talk about constant, slope and R2

    © BrainMass Inc. brainmass.com June 4, 2020, 3:27 am ad1c9bdddf


    Solution Preview

    Let's first see what the ordinary least squared regression line is all about. The regression line determines a linear equation that best fits the values of x (predictor) and y (dependent).
    In our case this line is 2.232 - 0.153x = y.
    So what is the interpretation of this line?
    2.232 (the constant) is called the y intercept and it's the value of y when the value of x is zero. i.e. it is the number of children when there is no RS Higher Degree.
    -0.153 (the slope or gradient) is the rate of change of y with respect to a unit change in x. In ...

    Solution Summary

    A step by step explanation of all the statistical parameters of a simple linear regression analysis based on the least square estimate.