Marketing expense	Production expense	Multiplex coverage	Budget	Movie_length	Lead_ Actor_Rating	Lead_Actress_rating	Director_rating	Producer_rating	Critic_rating	Trailer_views	3D_available	Time_taken	Twitter_hastags	Genre	Avg_age_actors	Num_multiplex	Collection
20.1264	59.62	0.462	36524.125	138.7	7.825	8.095	7.91	7.995	7.94	527367	YES	109.6	223.84	Thriller	23	494	48000
20.411	56.48	0.59	35457.565	109.2	5.34	5.535	5.285	5.465	8.54	539589	YES	115.88	303.952	Drama	22	383	49400
20.2864	57.64	0.589	37862.88	114	5.685	5.86	5.815	5.99	8.98	512129	YES	148.64	305.056	Drama	45	426	63200
23.089	65.26	0.547	34135.475	102.7	6.01	6.115	5.965	6.28	7.06	480067	NO		283.728	Comedy	22	438	46600
22.0656	65.26	0.547	32924.485	120.7	6.37	6.66	6.45	6.66	7.06	467075	YES	123.84	283.136	Comedy	20	460	39200
22.9864	65.26	0.547	31891.255	139.7	6.335	6.42	6.235	6.56	7.06	465689	NO		222.992	Thriller	30	439	37400

Use this dataset and solve in R programming

1. detect outliers and remove them for all columns

2. EDA analysis

3. Regression analysis.