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El restaurante con mejor ganancia y más estabilidad en las propinas, es…

Restaurante 1
Intervalos de propina	Frecuencia absoluta $(f_{i})$	Marca de Clase $(x_{i})$	Total $(f_{i} \cdot x_{i})$	$x_{i} - µ$	${(x_{i} - µ)}^{2}$	${(x_{i} - µ)}^{2} \cdot f_{i}$	$\frac{{(x_{i} - µ)}^{2} \cdot f_{i}}{N}$
[1.250 , 1.650]	5	1.450	7.250	-1.183	1.399.489	6.997.445	145.780
[1.650 , 2.050]	10	1.850	18.500	-783	613.089	6.130.890	127.727
[2.050 , 2.450]	10	2.250	22.500	-383	146.689	1.466.890	30.560
[2.450 , 2.850]	5	2.650	13.250	17	289	1.445
[2.850 , 3.250]	5	3.050	15.250	417	173.889	869.445	18.113
[3.250 , 3.650]	5	3.450	17.250	817	667.489	3.337.445	69.530
[3.650 , 4.050]	4	3.850	15.400	1.217	1.481.089	5.924.356	123.424
[4.050 , 4.450]	4	4.250	17.000	1.617	2.614.689	10.458.756	217.891
$\sum_{i = 1}^{8} f_{i} = 48 = N$		$\sum_{i = 1}^{8} f_{i} \cdot x_{i} = 126.400$		$\sum_{i = 1}^{8} {(x_{i} - μ)}^{2} \cdot f_{i} = 35.186.672$			$σ^{2} =$

Restaurante 2
Intervalos de propina	Frecuencia absoluta $(f_{i})$	Marca de Clase $(x_{i})$	Total $(f_{i} \cdot x_{i})$	$x_{i} - µ$	${(x_{i} - µ)}^{2}$	${(x_{i} - µ)}^{2} \cdot f_{i}$	$\frac{{(x_{i} - µ)}^{2} \cdot f_{i}}{N}$
[1.700 , 1.800]	5	1.790	8.950	-507	257.049	1.285.245	26.776
[1.880 , 2.010]	5	1.945	9.725	-352	123.904	619.520	12.907
[2.010 , 2.140]	7	2.075	14.525	-222	49.284	344.988	7.187
[2.140 , 2.270]	5	2.205	11.025	-92	8.464	42.320	882
[2.270 , 2.400]	8	2.335	18.680	38	1.444	11.552	241
[2.400 , 2.530]	5	2.465	12.325	168	28.224	141.120	2.940
[2.530 , 2.660]	5	2.595	12.975	298	88.804	444.020	9.250
[2.660 , 2.850]	8	2.755	22.040	458	209.764	1.678.112	34.961
$\sum_{i = 1}^{8} f_{i} = 48 = N$		$\sum_{i = 1}^{8} f_{i} \cdot x_{i} = 110.245$		$\sum_{i = 1}^{8} {(x_{i} - μ)}^{2} \cdot f_{i} = 4.566.877$			$σ^{2} = 95.143$

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