Considering only X6 'How satisfied are you with stadium fees', which of the following statements are true? (Tick as many as apply).

Considering only X2 'How satisfied are you with the interior design of the stadium'.

Which of the following statements are true? (Tick as many as apply)

Create a new variable called Loyalty_THREE.

Into this variable, I want you to recode the variable X7_loyalty as follows:

1 - 4 recode to 1 (Not loyal)

5 - 6 recode to 2 (Loyal)

6 recode to 3 (Very Loyal)

The club wants to determine if there are any differences between the loyalty levels and their attitudes towards the stadium, the team players and the entry fees. Run an appropriate test (using this new variable) to address this and answer the following questions.

Which items of the 6 items (X1 - X6) show a significant difference? (Tick as many as apply)

Multicollinearity has occurred in the regression. Run a procedure to overcome this problem using variable X1 - X5. Save the output and rerun the regression using the factors that have been produced. Then answer the following questions.

In an effort to simplify your output - if you have added a respondent in row 802 (for your regression calculation), delete it. 

How many factors are created?

Rerun the model excluding the NON-significant items. Assume a respondent gives 6 out of 7 for each of the remain significant variables, what score will that respondent have for loyalty (X7)

What is the R-square on the regression using the factors created in the previous question.

Multicollinearity has occurred in this model.

The club wants to determine how 'Age' (X9) might impact on levels of loyalty (X7) towards the club as whole.

Run the appropriate tests to answer this question.  (You will need to run MORE than one tabulation to get to answer this question fully).

There is NO association between age and levels of loyalty.

There is a significant difference between the genders (X8) and their loyalty to the football club (x7)?