Use the Marital Happy data on Canvas data to examine if there is a relationship between marital status and happiness, answering the following questions. (Note: This is summary data) What type of test...

1. 
   

   Use the Marital Happy data on Canvas data to examine if there is a relationship between marital status and happiness, answering the following questions. (Note: This is summary data)

   
   




1. 
   

   What type of test is needed?

   
   

Chi-Square Test of Independence

2. 
   

   What are the hypotheses?

   
   

3. 
   

   Is the test valid? Explain

   
   

4. 
   

   What is the decision and conclusion? Provide supporting evidence.

   
   

5. 
   

   Copy/paste all SPSS results used for this question.

   
   

2. 
   

   Use the Titanic data on Canvas to examine if class was related to passengers’ country of Residence. (Note: This is NOT summary data)

   
   

1. 
   

   What are the hypotheses?

   
   

2. 
   

   What is the decision and conclusion? Provide supporting evidence.

   
   

3. 
   

   Provide a cluster bar chart. Include a title.

   
   

4. 
   

   How does the bar chart support your decision in part (b)?

   
   

5. 
   

   Copy/paste all SPSS results used for this question.

   
   

3. 
   

   Use the Birth Weight data on Canvas to answer the questions below:

   
   

1. 
   

   Which of the following variables has the strongest linear relationship with gestational weeks: birthweight, head circumference, or length? Report the correlation coefficients and indicate which is the strongest.

   
   

r (gest./birthweight): 0.706

r (gest./head circ.): 0.444

r (gest./length): 0.651

2. 
   

   Create a scatterplot with gestational weeks as the explanatory variable and birthweight as the response variable.

   
   

3. 
   

   Run the linear regression for gestational weeks predicting birth weight.

   
   




1. 
   

   What is the linear regression equation?

   
   

y=6.66+0.36x

2. 
   

   Interpret the slope

   
   

3. 
   

   Does it make sense to interpret the y-intercept? If so, interpret, if not then explain why.

   
   

4. 
   

   What is the predicted birth weight of a baby born at 37 gestation weeks?

   
   

5. 
   

   Would it be appropriate to use this model to predict a birthweight for 30 gestational weeks? Explain.

   
   

4. 
   

   Copy/paste all SPSS results used for this question.

   
   

4. 
   

   Use the Presidential Height & Age data on Canvas to exam the relationship between the heights of U.S. Presidents and the age at which they became President.

   
   




1. 
   

   What types of variables are height and age?

   
   

Height: Quantitative continuous; explanatory variable

Age: Quantitative continuous; response variable

2. 
   

   Create a scatterplot using height as the predictor, paste below.

   
   

3. 
   

   Using the scatterplot and correlation coefficient, describe the linear relationship between the variables (report r).

   
   

With an correlation coefficient of 0.74, there is a high positive correlation between president height (in inches) and the age at which they were each elected.

4. 
   

   Would you recommend using linear regression to exam this relationship? Why or why not?

   
   

5. 
   

   Report the descriptives for both of the variables (mean, standard deviation, min, and max.). Explain whether these values are statistics or parameters.

   
   

6. 
   

   Create a histogram for age and paste below. How would you describe the distribution?

   
   

For the next two questions, going to Data-Sort Cases-Moving over the variable to sort will help you locate the particular subject you are looking for.

7. 
   

   Who was the shortest U.S. President? How tall was he?

   
   

James Madison, 64 inches (5’4)

8. 
   

   Who was the youngest to serve as President? What was his age?

   
   

Theodore Roosevelt, 42 years