Provide your answers below to each question. Do not delete the questions. Please do not use red font. Blue or green is OK. Part I (35 POINTS) The General Social Survey research department was tasked...

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Answer To: Provide your answers below to each question. Do not delete the questions. Please do not use red...

Vishali answered on Aug 23 2022
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Provide your answers below to each question. Do not delete the questions. Please do not use red font. Blue or green is OK.
Part I (35 POINTS)
The General Social Survey research department was tasked to determine the number of hours per day government employees in the USA spent on emailing. The department reported the data for a sample of 1765 government employees from GSS 2018 data on age (number of years), sex of respondent (1=males, 2=females), total family income (in constant dollars), and hours worked last week. Refer to the SPSS output below. Conduct the full regression analysis.
Regression
    Model Summary
    Model

    R
    R Square
    Adjusted R Square
    Std. Error of the Estimate
    1
    .312a
    .097
    .092
    12.165
    a. Predictors: (Constant), Respondents sex, Age of respondent, Number of hours worked last week, Respondent income in constant dollars
    ANOVAa
    Model
    Sum of Squares
    df
    Mean Square
    F
    Sig.
    1
    Regression
    9764.913
    4
    2441.228
    16.495
    .000b
    
    Residual
    90424.483
    611
    147.994
    
    
    
    Total
    100189.396
    615
    
    
    
    a. Dependent Variable: Email hours per week
    b. Predictors: (Constant), Respondents sex, Age of respondent, Number of hours worked last week, Respondent income in constant dollars
    Coefficientsa
    Model
    Unstandardized Coefficients
    Standardized Coefficients
    t
    Sig.
    
    B
    Std. Error
    Beta
    
    
    1
    (Constant)
    -2.633
    3.044
    
    -.865
    .387
    
    Age of respondent
    -.017
    .036
    -.019
    -.483
    .629
    
    Number of hours worked last week
    .090
    .036
    .102
    2.464
    .014
    
    Respondent income in constant dollars
    8.863E-5
    .000
    .278
    6.659
    .000
    
    Respondents sex
    3.523
    1.025
    .138
    3.438
    .001
    a. Dependent Variable: Email hours per week
Hint: For the coefficient that ends in E-5, move the point 5 decimal places to the left
1. Which variables are independent variables?
Ans-
· Age of respondent
· Number of hours worked last week
· Respondent income in constant dollars
· Respondents sex
2. Which variable is the dependent variable?
Email hours per week
3. What does coefficient of determination (R2) tell you?
R2 measures explanatory power of model. It reflects model accuracy in sense of how much is explanatory power of explanatory(independent) variable. Value of R2 commonly describes how well sample regression line fits observed data. Here, value of R2 is 0.097 which means explanatory variables explains only 9.7% variability in our model.

4. What is the intercept value? What can it indicate here?
Intercept here is -2.633. Here, its p value = 0.387 which means intercept here is non-significant. It indicates that result of the analysis will be zero if all other variables are zero

5. Is the model overall significant?
From Anova table, we can see value of F statistic= 16.495 and p value is 0.001. Model is overall significant at 1%, 5%, 10% level of significance.
6. Determine the multiple regression equation:
Email hours per week= -0.017*Age of respondent + 0.090*Number of hours worked last week + 0.000088*Respondent income in constant dollars + 3.523* Respondents sex - 2.633
Significant regression equation:
Email hours per week= 0.090*Number of hours worked last week + 0.000088*Respondent income in constant dollars + 3.523* Respondents sex
7. Discuss significant regression coefficients.
When null hypothesis (H0) is rejected, question comes which regression coefficients are significant. In Multiple regression model, there may be some regression coefficients which may are non-significant i.e. their contribution to predict y is almost negligible and hence they should not be retained in the final model. Significant regression coefficients are those whose p value is less than 0.05 if level of significance chosen is 5%.
Here, Number of hours worked last week, Respondent income in constant dollars, Respondents sex are significant at 5% level of significance.
8. What variable has the strongest effect on the number of hours US government employees spent emailing?
Respondent income in constant dollars has the strongest effect on the number of hours US government employees spent emailing
9. What is the estimated number of hours emailing per day for a woman who works 40 hours per week and who earns $50.000 per year?
Put Respondents sex=2, Number of hours worked last week=40, Respondent income in constant dollars= 50.000
Reduced Regression Equation at 5% level of significance:
Email hours per week= 0.090*Number of hours worked last week + 0.000088*Respondent income in constant dollars + 3.523* Respondents sex
= -0.90*40 + 0.000088*50.000 + 3.523*2
=43.0504
Hence, the estimated number of hours emailing per day for a woman who works 40 hours per week and who earns $50.000 per year is 43(approx.).
Part II (35 POINTS)
Give the full logistic regression analysis of the SPSS 2014 output provided below. The coding for the dependent variable is presented in the SPSS output. Interpret and include in your regression equation only significant coefficients (use alpha= 0.05).
The question that the respondents were asked is: “Would you favor or
oppose the teaching of sex education in public schools?” (Favor= 1; Oppose=0)
Coding for variables:
polviews:
1. Extremely liberal
2. Liberal
3. Slightly Liberal
4. Moderate
5. Slightly Conservative
6. Conservative
7. Extremely Conservative
pillok: Birth Control to Teenagers 14-16
Question asked: Do you strongly agree, agree, disagree, or strongly disagree that it is acceptable to make birth control devices available to teenagers, age 14-16?
1. Strongly Agree
2. Agree
3. Disagree
4. Strongly disagree
educ: Highest year of school completed
Logistic Regression
    Case Processing Summary
    Unweighted Casesa
    N
    Percent
    Selected Cases
    Included in Analysis
    1579
    62.2
    
    Missing Cases
    959
    37.8
    
    Total
    2538
    100.0
    Unselected Cases
    0
    .0
    Total
    2538
    100.0
    a. If weight is in effect, see classification table for the total number of cases.
    Dependent Variable Encoding
    Original Value
    Internal Value
    Oppose
    0
    Favor
    1
    Categorical Variables Codings
    
    Frequency
    Parameter coding
    
    
    (1)
    (2)
    (3)
    BIRTH CONTROL TO TEENAGERS 14-16
    STRONGLY...
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