A large mail-order house believes that there is a linear relationship between the weight of the mail it receives and the number of orders to be filled. It would like to investigate this relationship in order to predict the number of orders to be filled based on the weight of the mail. From an operational perspective, knowledge of the number of orders to be filled will help in the planning of the order-fulfillment process. A sample of 15 mail shipments is selected within a range of 200 to 700 pounds. The results are as follows. Weight of Mail (Pounds) Orders to be Filled (In Thousands) 240 6.5 290 9.0 237 7.2 380 11.5 342 10.3 384 10.6 482 14.5 430 13.6 409 12.8 553 16.5 572 17.1 501 15.8 628 19.0 670 19.6 620 19.2 a. Construct a scatter plot using Excel. b. Assuming a linear relationship, determine the regression results by using Excel. What are the values for the regression coefficients b0 and b1 for this problem? c. Interpret the meaning of the slope, b1, in this problem.

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Hypothesis Testing

In statistics, there are two ways to calculate the sample data. One is descriptive statistics and the other is inferential statistics.  In descriptive statistics, all of the information is defined through graphs and charts. In inferential statistics, it allows one to test the hypothesis and it is always used to predict the data. Inferential statistics has two main areas: estimating parameters and hypothesis testing. A hypothesis is a statement about the unknown parameter of the population which we want to verify from the information available from the sample. The premise may or may not be true. The estimation of the whole population would be a reasonable way of deciding if the statistical hypothesis is valid. There are two types of hypotheses. They are null hypothesis and alternative hypothesis.