Step #1 - Run a regression analysis - Merchandise purchased is the independent v

Question

Step #1 - Run a regression analysis - Merchandise purchased is the independent variable. Purchasing costs, # of purchase orders and # of suppliers are dependent variables.

Step #2 - Identify outliers

I believe I have to do some sort of scatter graph? But I need the actual excel sheet to submit.

Regression Project-AAA 2010-2 Q Search Sheet InsertPage Layout Formulas Data Review View Cut Copy Format Share ^ AutoSum Wrap Text | Fill Conditional Format Cell Formatting as Table Styles Insert Delete Format Sort & Filter Clear B11 3 Managerial Acccunting Prajects 4 Prachce in Crinca Anne M.A. Sergeant anad Wrtn Col Grand Valley State Un Regression Project Data Daniclle's Bridal Fashions Purchasing Department Cost Data Merchandise Number of Purchased Purchase Number of Costsl(in dolars Orders Suppliers 20 Los Angeles Sheet1 Ready 100% 1

Explanation / Answer

Regression Analysis is a method in statistical analysis used to establish relationship between two variables, namely the independant and dependant variable. The dependant variable is also called endogenous variable while the independant varaible is called explanatory variable. Normally, a model has one or more independant variable and a single dependant variable.

It is given to us that is a single indepenant variable, that is, merchandise purchased. There are three dependant variables. They are : purchasing costs, number of purchasing orders and number of suppliers.

Since there are three dependant variables, we need to run the regression thrice. Once for each dependant variable.

1) Regression for Merchandise purchased and purchasing cocts. When you run regression in excel, you will find out that the coefficeint if correlation, R = 0.02303 while R2 = 0.00057.

Since, the value of R2 is close to 0%, this means that the given model does not explain the variability of the response around the mean. So, basically, it says that the model does not fit the data

2) Regression Analysis for Merchandise purchased and number of purchase orders. When you run the regression for these data on excel, you will find that R here = 0.313511 and R2 = 0.098289. this value is also close to 0%. So again this model does not fit the data.

3) Regression Analysis for Merchandise purchased and number of suppliers. When you run the regression for these variables on excel, you will find that R = 0.096308 and R2 = 0.009275. Here, too the model does not fit the data as the value of R2 is closer to 0.

One outlier that you can see in the data is Seattle which has the highest number of purchase orders of 7586. This indicates that it has a high demand for bridal outifts. This is also indicated at the high pruchasing costs and the amount of merchandise purchased.

Another outlier is Chicago. Here the number of suppliers is 222, however, it has one of the lowest amount of merchandise purchased and a low purchasing costs. This indicates that here, the demand for bridal fashion is lower as compared to some otther places as despite having a large number of suppliers, the merchandise purchased is one of the lowest.

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