## Create a crosstab comparing gender of the respondent and type of luxury car the respondent bought. How many women bought a European luxury car?

Data analysis focuses on the process of transforming raw data into information to be used by decision makers. Techniques range from a simple descriptive analysis of the variables to more complex multivariate analysis. The technique selected by a researcher will be determined based on what question they are seeking to answer.

For this homework assignment, you will need to conduct a data analysis.

Using SPSS, the attached data (Luxury Car Data 2019), and the following information on the data, answer the questions listed below:

Information on the Data:

In the last decade, the luxury car segment became one of the most competitive in the automobile market. A large car manufacturer wanted to know more about the consumers, the values, and the attributes that were important in selecting which luxury car to purchase. To collect the data, a survey was mailed to 498 consumers. Consumers were selected because they had either purchases a luxury American, a luxury German, or a luxury Japanese car within the last year. Of the 498 surveys originally sent, 17 were returned by the post office as undeliverable. One hundred fifty-five completed surveys were received, for a response rate of 32.2%.

The survey contained questions on the importance of “car attributes” and the importance of different “values”. Importance was measured with a 7-point scale ranging from “very important” (7) to “very unimportant” (1).

The “car attributes” examined were: Comfort, Safety, Power, Speed, Styling, Durability, Low maintenance cost, Reliability, Warranty, Nonpolluting, High gas mileage, and Speed of repairs.
The “values” examined were: Fun, Sense of belonging, Being well respected, Self-fulfillment, Sense of accomplishment, Warm relationship, Security, and Self-respect.
The survey also contained questions about the respondent’s “demographics”. In the data set, the following codes were used:

Car: 1 = American car; 2 = European Car; 3 = Japanese car
Age: 2 = 35 years and younger; 3 = 36-45 years; 4 = 46-55 years; 5 = 55-65 years; 6 = 65+
Sex: 1 = male; 0 = female
Education: 1 = less than high school; 2 = high school grad; 3 = some college; 4 = college grad; 5 = graduate degree
Income: 1 = less than \$35,000; 2 = \$35-50,000; 3 = \$50-65,000; 4 = \$65,000+
Questions:

Use list-wise deletion to deal with any missing data in the data set. How many completed responses are there?

Create a frequency table for the education of the respondent. How many respondents were high school graduates?

Create a crosstab comparing gender of the respondent and type of luxury car the respondent bought. How many women bought a European luxury car?

Calculate the descriptive statistics (i.e., mean, standard deviation, and range) for the entire list of “values”. Which “value” had the lowest mean?

By selecting the appropriate data and using the proper statistical technique for comparison, answer the following research question: Which of the “values”, if any, do women have an average rating that is statistically greater than the average rating of men?

By selecting the appropriate data and using the proper statistical technique for comparisons, answer the following research hypothesis: Do any of the “car attributes” show statistically significant differences in their average ratings between American, European, and Japanese car owners? For any “car attributes” that are different, identify how they are different.

HOW TO SUBMIT ASSIGNMENT: Upload your data analysis to Blackboard in a word document. Save the document as Your Last Name Data Analysis. Make sure to also have you name on the first page of the document.

NOTES:

You will need to answer the first question before you answer any of the latter questions.
Your answers to the data analysis should be listed by question number. You must show your work to receive credit. Any relevant tables/charts should be copied and pasted into a word document.
This is a difficult assignment and will take significant time and effort. When analyzing data, make sure you take your time as it is very easy to make a mistake.