• Write the regression (prediction) equation:
• Dep_Var = Intercept + c1 * Ind_Var_1 + c2 * Ind_Var_2 + c3 * Ind_Var_3
• The dependent - price of the home (HP).
• The independent variables square footage (SF), bedroom count (BR), bathroom count (BA) and age of the home (AGE)

1. Define "adjusted R²," what does the value of the adjusted R²reveal about the model?
2. Using the p-value approach, is the null hypothesis for the F-test rejected or not rejected?
3. Using the p-value approach, is the null hypothesis for the t-test rejected or not rejected for each coefficient? Identify the variable with the greatest significance.
4. Generate the correlation matrix. Define multicollinearity. Are any of the independent variables highly correlated with each other?

House	Price	Size	Bedrooms	Baths	Home Age
1	919480	7670	8	5.5	1
2	848420	7190	6	4	5
3	841491	7030	6	4	8
4	793656	6530	7	5	3
5	793084	6800	8	5.5	6
6	706596	6600	7	5	7
7	694430	5900	5	3.5	10
8	667732	5570	5	3.5	4
9	667212	6110	6	4	8
10	558342	6160	6	4	7
11	547596	4920	6	4.5	2
12	546084	5910	6	4	10
13	537900	4890	6	4	7
14	523584	5050	6	4	10
15	496100	4510	6	4.5	17
16	487494	4380	6	4	6
17	478675	4660	5	3.5	9
18	466560	5760	5	3.5	3
19	459950	4680	4	3	8
20	453913	3660	6	4	18
21	448800	5280	6	4	8
22	445740	4370	4	3	5
23	416160	4080	4	3	12
24	416120	4040	5	3.5	3
25	410592	4200	4	3	9
26	404538	3290	5	3.5	14
27	393584	4660	4	3	7
28	393557	3180	4	3	20
29	392904	3400	3	2	8