Hey, Can I please get an answer to this assignment? thank you.

this is done in R studio :

Please follow the R code to

1. Please read data and create training data (70%) and validation data (30%)
2. Build a multiple regression model-- the median house price (MEDV) as target variable and CRIM, CHAS, and RM as input variables (the explanations of the columns are available at page. 169 or Problem 1 at the end of the chapter)
3. Test the model on the validation data set.
4. In a word file, write the equation of the model and compare the model's performance on the training data and validation data (accuracy function)
5. Include all variables (except CAT..MEDV) as predictors to predict MEDV. Please use stepwise regression (both). Test the final model on the validation data
6. Write the equation of the model in the word file.

For the data, i can't attach the data set but here is a sample that has 506 lines total. and for training sample we can use 300 spiceman

CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	LSTAT	MEDV	CAT. MEDV
0.00632	18	2.31	0	0.538	6.575	65.2	4.09	1	296	15.3	4.98	24	0
0.02731	0	7.07	0	0.469	6.421	78.9	4.9671	2	242	17.8	9.14	21.6	0
0.02729	0	7.07	0	0.469	7.185	61.1	4.9671	2	242	17.8	4.03	34.7	1
0.03237	0	2.18	0	0.458	6.998	45.8	6.0622	3	222	18.7	2.94	33.4	1
0.06905	0	2.18	0	0.458	7.147	54.2	6.0622	3	222	18.7	5.33	36.2	1
0.02985	0	2.18	0	0.458	6.43	58.7	6.0622	3	222	18.7	5.21	28.7	0
0.08829	12.5	7.87	0	0.524	6.012	66.6	5.5605	5	311	15.2	12.43	22.9	0
0.14455	12.5	7.87	0	0.524	6.172	96.1	5.9505	5	311	15.2	19.15	27.1	0
0.21124	12.5	7.87	0	0.524	5.631	100	6.0821	5	311	15.2	29.93	16.5	0
0.17004	12.5	7.87	0	0.524	6.004	85.9	6.5921	5	311	15.2	17.1	18.9	0
0.22489	12.5	7.87	0	0.524	6.377	94.3	6.3467	5	311	15.2	20.45	15	0
0.11747	12.5	7.87	0	0.524	6.009	82.9	6.2267	5	311	15.2	13.27	18.9	0
0.09378	12.5	7.87	0	0.524	5.889	39	5.4509	5	311	15.2	15.71	21.7	0
0.62976	0	8.14	0	0.538	5.949	61.8	4.7075	4	307	21	8.26	20.4	0
0.63796	0	8.14	0	0.538	6.096	84.5	4.4619	4	307	21	10.26	18.2	0
0.62739	0	8.14	0	0.538	5.834	56.5	4.4986	4	307	21	8.47	19.9	0
1.05393	0	8.14	0	0.538	5.935	29.3	4.4986	4	307	21	6.58	23.1	0
0.7842	0	8.14	0	0.538	5.99	81.7	4.2579	4	307	21	14.67	17.5	0
0.80271	0	8.14	0	0.538	5.456	36.6	3.7965	4	307	21	11.69	20.2	0
0.7258	0	8.14	0	0.538	5.727	69.5	3.7965	4	307	21	11.28	18.2	0
1.25179	0	8.14	0	0.538	5.57	98.1	3.7979	4	307	21	21.02	13.6	0
0.85204	0	8.14	0	0.538	5.965	89.2	4.0123	4	307	21	13.83	19.6	0
1.23247	0	8.14	0	0.538	6.142	91.7	3.9769	4	307	21	18.72	15.2	0
0.98843	0	8.14	0	0.538	5.813	100	4.0952	4	307	21	19.88	14.5	0
0.75026	0	8.14	0	0.538	5.924	94.1	4.3996	4	307	21	16.3	15.6	0
0.84054	0	8.14	0	0.538	5.599	85.7	4.4546	4	307	21	16.51	13.9	0
0.67191	0	8.14	0	0.538	5.813	90.3	4.682	4	307	21	14.81	16.6	0
0.95577	0	8.14	0	0.538	6.047	88.8	4.4534	4	307	21	17.28	14.8	0
0.77299	0	8.14	0	0.538	6.495	94.4	4.4547	4	307	21	12.8	18.4	0
1.00245	0	8.14	0	0.538	6.674	87.3	4.239	4	307	21	11.98	21	0
1.13081	0	8.14	0	0.538	5.713	94.1	4.233	4	307	21	22.6	12.7	0
1.35472	0	8.14	0	0.538	6.072	100	4.175	4	307	21	13.04	14.5	0
1.38799	0	8.14	0	0.538	5.95	82	3.99	4	307	21	27.71	13.2	0
1.15172	0	8.14	0	0.538	5.701	95	3.7872	4	307	21	18.35	13.1	0
1.61282	0	8.14	0	0.538	6.096	96.9	3.7598	4	307	21	20.34	13.5	0
0.06417	0	5.96	0	0.499	5.933	68.2	3.3603	5	279	19.2	9.68	18.9	0
0.09744	0	5.96	0	0.499	5.841	61.4	3.3779	5	279	19.2	11.41	20	0