代写JS程序，进行线性回归分析，计算出最合适的参数。
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
- 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.
- 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.
- 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 | |
