Data Analysis Toolpak

You are the manager of a retail store. You want to investigate how metrics can improve the way you manage your business.

Use the Week 5 Data Set to create and calculate the following in Excel®:

1. Conduct a goodness of fit analysis which assesses orders of a specific item by size and items you received by size.
   • Conduct a hypothesis test with the objective of determining if there is a difference between what you ordered and what you received at the .05 level of significance.
   • Identify the null and alternative hypotheses.
2. Generate a scatter plot, the correlation coefficient, and the linear equation that evaluates whether a relationship exists between the number of times a customer visited the store in the past 6 months and the total amount of money the customer spent.
   • Set up a hypothesis test to evaluate the strength of the relationship between the two variables.
   • Use a level of significance of .05.
3. Use the regression line formula to forecast how much a customer might spend on merchandise if that customer visited the store 13 times in a 6 month period. Consider the average monthly sales of 2014, $1310, as your base to:
   • Calculate indices for each month for the next two years.
   • Graph a time series plot.
4. In the Data Analysis Toolpak, use Excel’s Exponential Smoothing option.
   • Apply a damping factor of .5, to your monthly sales data.
   • Create a new time series graph that compares the original and the revised monthly sales data.

Part 2

Reference your Excel® spreadsheet from Part 1.

You are the manager of a retail store. You want to investigate how metrics can improve the way you manage your business.

Use the Week 5 Data Set to create and calculate the following in Excel®:

1. Conduct a goodness of fit analysis which assesses orders of a specific item by size and items you received by size.
   • Conduct a hypothesis test with the objective of determining if there is a difference between what you ordered and what you received at the .05 level of significance.
   • Identify the null and alternative hypotheses.
2. Generate a scatter plot, the correlation coefficient, and the linear equation that evaluates whether a relationship exists between the number of times a customer visited the store in the past 6 months and the total amount of money the customer spent.
   • Set up a hypothesis test to evaluate the strength of the relationship between the two variables.
   • Use a level of significance of .05.
3. Use the regression line formula to forecast how much a customer might spend on merchandise if that customer visited the store 13 times in a 6 month period. Consider the average monthly sales of 2014, $1310, as your base to:
   • Calculate indices for each month for the next two years.
   • Graph a time series plot.
4. In the Data Analysis Toolpak, use Excel’s Exponential Smoothing option.
   • Apply a damping factor of .5, to your monthly sales data.
   • Create a new time series graph that compares the original and the revised monthly sales data.

Part 2

Reference your Excel® spreadsheet from Part 1.