John, a cost analyst for a manufacturing firm, was asked to estimate the overhead costs at one of the factories. He interviewed the supervisor and several workers there to get a feel of how overhead costs changed. His experience with the cost accounting system pointed to the use of machine hours as the cost driver for overhead items. Over the last 12 months, the total overhead costs were $103,918, out of which John determined $4,620 to be fixed cost per month and the rest variable cost. For the same period of time, 4,519 machine hours were incurred in that factory.

The cost analyst, John, went back to the accounting information system to look for more relevant data. The following breakdown of the overhead costs and the associated machine hours over the same 12-month period was available:

Month	Overhead	Machine Hours
1	$ 7,102	203
2	8,620	319
3	9,120	425
4	7,092	289
5	7,965	388
6	9,641	471
7	10,421	512
8	8,834	443
9	6,935	262
10	8,520	371
11	10,342	494
12	9,326	342
Total	$103,918	4,519

John recently attended a workshop on statistical analysis. He was interested in applying the regression analysis to solve the relation between overhead costs and machine hours.

Required:

1. Determine the cost equation using the regression analysis and interpret the regression results.
2. Predict overhead costs when 400 machine hours are expected to be spent for next month.
3. Indicate the t-statistic and the p-value associated with the estimated coefficient, b.
4. Given a t value of 228, construct the 95 percent confidence interval around the estimated coefficient, b.