## Requirements

Write a program to calculate the linear regression size-estimating parameters for two arrays, each of n numbers. Enhance program 1 to work for the new calculations with minimal duplication.

Given a set of historical data for variables x and y, you want to determine if a likely value yk based on a known or estimated new value xk. An example would be the relationship between the estimated object LOC in a program and the actual new and changed program LOC.

## Conditions

The historical x and y data must demonstrate a relationship.

There must be sufficient data produce a statistically significant result (at least three items and preferably five or more.)

Determine the beta0 and beta 1 parameters that best represent the relationship between these x and y data, and then calculate yk using the following formula and the available data.

## Testing

Thoroughly test the program. At a minimum, use this program to calculate the beta parameters for the three provide data sets.

### TEST DATA

Program Number Estimated Object LOC Estimated New and Changed LOC Actual New and Changed LOC
1 130 163 186
2 650 765 699
3 99 141 132
4 150 166 272
5 128 137 291
6 302 355 331
7 95 136 199
8 945 1206 1890
9 368 433 788
10 961 1130 1601
Sum 3828 4632 6389
Average 382.8 463.2 638.9
1. Use the data in above for estimated object LOC and actual new and changed LOC. The resulting values should be beta_0 = -22.55 and beta_1 = 1.7279.
2. Calculate the beta_0 and beta_1 parameters for the regression fit of estimated new and changed LOC to actual new and changed LOC columns in Table D8. The answer in this case should be beta_0 = -23.92 and beta_1 = 1.4310.
3. Calculate the beta_0 and beta_1 parameters for the estimated new and changed LOC and the actual new and changed LOC for the programs 2A, 3A and 4A that you have developed.

## WORKED EXAMPLE

Program Number Estimated Object LOC Estimated New and Changed LOC XiYi Xi2
1 1 130 186 24180
2 2 650 699 454350
3 3 99 132 13068
4 4 150 272 40800
5 5 128 291 37248
6 6 302 331 99962
7 7 95 199 18905
8 8 945 1890 1786050
9 9 368 788 289984
10 10 961 1601 1538561
Sum 3828 6389 4303108 2540284
Average 382.8 638.9