Which variables are numerical Which are binary Which are ordinal Which are nominal 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 Statistics menu and then use an excel function for counting missing values Use XLMiner to plot a histogram for each of the quantitative variables Based on the histogram and summary statistics, answer the following questions Which variable has the largest variability Which variables seem skewed Are there any values that seem extreme (outliers) Compute the correlation table for the quantitative variable (use Excels Data Data Analysis Correlation menu) In addition, use XLMiner to generate a scatter plot matrix for these variables Which pair of variables is most strongly correlated How can we reduce the number of variables based on these correlations How would the correlations change if we normalized the data first Explore the relationship between price and the numerical variables by using scatter plot matrix What does this plot show us Present your insights within 100 words Explore the relationship between price and the categorical variables by using box plots and Pivot Charts Which of the pair among variables seem to be correlated What do these plots and charts show us Present your insights within 100 words Describe how you would handle records with missing values Describe how you would handle records with outliers Describe how you would convert Fuel Type and Color to binary variables Conrm this using XLMiners utility to transform categorical data into dummies 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 Present your insights in a word document Your insights should not exceed 100 words for each above question Modify the worksheets titles and names appropriately to reflect the contents

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Which variables are numerical? Which are binary? Which are ordinal? Which are nominal? Create a table with the average, median, min, max, standard deviation, count

Which variables are numerical? Which are binary? Which are ordinal? Which are nominal?
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 Statistics menu and then use an excel function for counting missing values.
Use XLMiner to plot a histogram for each of the quantitative variables. Based on the histogram and summary statistics, answer the following questions:
1. Which variable has the largest variability?
2. Which variables seem skewed?
3. Are there any values that seem extreme (outliers)?
Compute the correlation table for the quantitative variable (use Excels Data Data Analysis Correlation menu). In addition, use XLMiner to generate a scatter plot matrix for these variables.
- Which pair of variables is most strongly correlated?
- How can we reduce the number of variables based on these correlations?
- How would the correlations change if we normalized the data first?
Explore the relationship between price and the numerical variables by using scatter plot matrix. What does this plot show us? Present your insights within 100 words
Explore the relationship between price and the categorical variables by using box plots and Pivot Charts. Which of the pair among variables seem to be correlated? What do these plots and charts show us? Present your insights within 100 words
Describe how you would handle records with missing values.
Describe how you would handle records with outliers.
Describe how you would convert Fuel Type and Color to binary variables. Conrm this using XLMiners utility to transform categorical data into dummies.
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.
Present your insights in a word document. Your insights should not exceed 100 words for each above question.
Modify the worksheets titles and names appropriately to reflect the contents.