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The data in the table that follows was collected by a large car manufacturer concerning a new prototype car called the CIELO. Thirty carefully selected respondents were shown the car and fully briefed about its capabilities. Here is the data, which includes age (intervally scaled), sex (nominal), social status (interval scale ranging from 10, low status, to 30, high status), attitude toward CIELO (interval scale ranging from 6, negative attitude, to 30, positive attitude) and intention to purchase CIELO (nominal, yes or no).
1. Is there any difference in male and female attitudes toward the CIELO?
2. What is the relationship between intention to purchase and gender?
3. Is there anything unusual about this data?

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The solution discusses the data in the table that follows was collected by a large car manufacturer concerning a new prototype car called the CIELO. Thirty carefully selected respondents were shown the car and fully briefed about its capabilities. Here is the data, which includes age (intervally scaled), sex (nominal), social status (interval scale ranging from 10, low status, to 30, high status), attitude toward CIELO (interval scale ranging from 6, negative attitude, to 30, positive attitude) and intention to purchase CIELO (nominal, yes or no).
1. Is there any difference in male and female attitudes toward the CIELO?
2. What is the relationship between intention to purchase and gender?
3. Is there anything unusual about this data?

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first, let's setup dummy variables for gender and intention to purchase:
<br>so, Male=1 for males and =0 for females
<br>Buying=1 for "yes to purchase" and =0 for "No"
<br>You can see the new data in the attachment.
<br>1. difference in male and female attitudes toward the CIELO
<br>we can regress attitudes function with independent variable of Male,
<br>Coefficients
<br>Model Coefficients Std. Error t Sig.
<br> B
<br>(Constant) 18.133 1.450 12.508 .000
<br>MALE .800 2.050 .390 .699
<br>* Dependent Variable: ATTITUDE SCORE, R square=0.005
<br>we can find that both t-stat and R square value are very low, which means the regression estimate is insignificant. Therefore, we have to say that there's no relationship between ...

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