project no	total score	work effort	ufp rating	more than one computer	elapsed time	effort test	funcion points	team age_average	defects delivered
1	2844	17710	A	yes	192	136	110	19	25
2	2954	18920	B	yes	192	136	110	19	25
3	3380	41315	A	yes	193	209	182	16	22
4	2128	7957	A	no	157	98	102	24	30
5	1989	6692	A	yes	157	90	68	31	38
6	2191	8558	C	no	157	98	102	24	30
7	1713	6479	A	yes	144	92	58	49	54
8	1940	6529	B	yes	150	92	76	30	34
9	2024	7295	C	yes	157	92	76	30	34
10	2465	12945	C	yes	175	110	101	24	28
11	1874	15250	A	yes	155	90	70	38	43
12	1909	5195	A	yes	155	90	70	38	43
13	4066	35550	C	yes	199	258	176	15	19
14	1890	5195	A	yes	159	91	68	30	31
15	2380	11845	B	yes	169	70	101	17	23

The table given above consisting of one dependent (Total score of projects) and eight independent variables (work effort, ufp rating, whether more than one computer is used, elapsed time, effort test, function points, average team age and defects delivered). For the given dataset answer the following questions (for =0,10): (PLEASE USE MINITAB)

state your study steps while you are solving questions in minitab. For Example STAT-REGRESSION-FIT REGRESSION - .... -

- Please estimate the total score of projects by using a single independent variable. Please explain the criteria you use to determine the best independent variable.

- Interpret your results.

- Construct the ANOVA table for the simple linear regression model.

- Test the significance of the simple linear regression model

- Estimate the ^2 of your simple linear model

- Calculate R^2 of your simple linear model and interpret your result

- Test lack of fit of your simple linear model

MULTIPLE LINEAR REGRESSION PART ( Now use all independent variables (for =0,10) )

- Estimate the parameters of the multiple regression model

- Construct the ANOVA table

- Test the significance of the model

- Calculate R^2 of your model

- Check the multicollinearity

- Check the model adequacies