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. 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. 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. 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.