This is a group assignment. Download the data file Cereals.xlsx from Blackboard to your computer. This dataset contains the nutritional information, grocery shelf location, and consumer rating of 77 breakfast cereals. Create a new Word document and save it as HW1Answers_X (where X is your team number). Where required, write your answers and/or paste screenshots into this Word document. Your response should not exceed 100 words for each below question. Write every members full name and participation on the first page of the Word document as follows.

Participant	Complete the Assignment before the Meeting (Y/N)	Percentage of Contribution	Justification

Launch Tableau/Excel on your computer. Open the downloaded data file and explore the data.

1. Which variables are quantitative/numerical? Which are ordinal? Which are nominal?
2. Create a table with the average, median, min, max, standard deviation, count blank(the number of records with missing values) for each of the quantitative variables. This can be done through 1) Excels functions or 2)Excels Data Data Analysis Descriptive Statics menu and then use a excel function for counting missing values.
3. Use Tableau to plot a histogram for each of the quantitative variables. Place all visualizations in a Dashboard. Based on the histograms and summary statistics, answer thee following questions:
   1. Which variable have the largest variability?
   2. Which variables seem skewed?
   3. Are there any values that seem extreme?
4. Use Tableau to plot a side-by-side boxplot comparing the calories in hot vs cold cereals. What does this plot show us?
5. Use Tableau to plot a side-by-side boxplot comparing the protein in hot vs cold cereals. What does this plot show us?
6. Use Tableau to plot a side-by-side boxplot of consumer rating as a function of the shelf height. If we were to predict consumer rating from shelf height, does it appear that we need to keep all three categories of shelf height?
7. Compute the correlation table for the quantitative variable (use Excels Data Data Analysis Correlation menu). In addition, use Tableau to generate a scatter plot matrix for these variables.
   1. Which pair of variables is most strongly correlated?
   2. How can we reduce the number of variables based on these correlations?
   3. How would the correlations change if we normalized the data first?
8. Remove all records with missing numerical measurements from the dataset by creating a new worksheet. You may use the Missing Data Handling utility in XLMiner.
9. Conduct a principal components analysis on the cleaned data and comment on the results. Should the data be normalized? Discuss what characterizes the components you consider key. Use the principal components utility in XLMiner.
10. Present your insights in a new document. Your insights should not exceed 100 words for each above question.
11. Modify the worksheets titles and names appropriately to reflect the contents.