Statistical Tables F Distribution Table entry for p is the critical value F* with probability p lying to its right. F critical values Degrees of freedom in the numerator Degrees of freedom in the denominator p 1 2 3 4 5 6 7 8 9 1 .100 39.86 49.50 53.59 55.83 57.24 58.20 58.91 59.44 59.86 .050 161.45 199.50 215.71 224.58 230.16 233.99 236.77 238.88 240.54 .025 647.79 799.50 864.16 899.58 921.85 937.11 948.22 956.66 963.28 .010 4052.2 4999.5 5403.4 5624.6 5763.6 5859.0 5928.4 5981.1 6022.5 .001 405284 500000 540379 562500 576405 585937 592873 598144 602284 2 .100 8.53 9.00 9.16 9.24 9.29 9.33 9.35 9.37 9.38 .050 18.51 19.00 19.16 19.25 19.30 19.33 19.35 19.37 19.38 .025 38.51 39.00 39.17 39.25 39.30 39.33 39.36 39.37 39.39 .010 98.50 99.00 99.17 99.25 99.30 99.33 99.36 99.37 99.39 .001 998.50 999.00 999.17 999.25 999.30 999.33 999.36 999.37 999.39 3 .100 5.54 5.46 5.39 5.34 5.31 5.28 5.27 5.25 5.24 .050 10.13 9.55 9.28 9.12 9.01 8.94 8.89 8.85 8.81 .025 17.44 16.04 15.44 15.10 14.88 14.73 14.62 14.54 14.47 .010 34.12 30.82 29.46 28.71 28.24 27.91 27.67 27.49 27.35 .001 167.03 148.50 141.11 137.10 134.58 132.85 131.58 130.62 129.86 4 .100 4.54 4.32 4.19 4.11 4.05 4.01 3.98 3.95 3.94 .050 7.71 6.94 6.59 6.39 6.26 6.16 6.09 6.04 6.00 .025 12.22 10.65 9.98 9.60 9.36 9.20 9.07 8.98 8.90 .010 21.20 18.00 16.69 15.98 15.52 15.21 14.98 14.80 14.66 .001 74.14 61.25 56.18 53.44 51.71 50.53 49.66 49.00 48.47 5 .100 4.06 3.78 3.62 3.52 3.45 3.40 3.37 3.34 3.32 .050 6.61 5.79 5.41 5.19 5.05 4.95 4.88 4.82 4.77 .025 10.01 8.43 7.76 7.39 7.15 6.98 6.85 6.76 6.68 .010 16.26 13.27 12.06 11.39 10.97 10.67 10.46 10.29 10.16 .001 47.18 37.12 33.20 31.09 29.75 28.83 28.16 27.65 27.24 6 .100 3.78 3.46 3.29 3.18 3.11 3.05 3.01 2.98 2.96 .050 5.99 5.14 4.76 4.53 4.39 4.28 4.21 4.15 4.10 .025 8.81 7.26 6.60 6.23 5.99 5.82 5.70 5.60 5.52 .010 13.75 10.92 9.78 9.15 8.75 8.47 8.26 8.10 7.98 .001 35.51 27.00 23.70 21.92 20.80 20.03 19.46 19.03 18.69 7 .100 3.59 3.26 3.07 2.96 2.88 2.83 2.78 2.75 2.72 .050 5.59 4.74 4.35 4.12 3.97 3.87 3.79 3.73 3.68 .025 8.07 6.54 5.89 5.52 5.29 5.12 4.99 4.90 4.82 .010 12.25 9.55 8.45 7.85 7.46 7.19 6.99 6.84 6.72 .001 29.25 21.69 18.77 17.20 16.21 15.52 15.02 14.63 14.33 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 10 12 15 20 25 30 1 .100 60.19 60.71 61.22 61.74 62.05 62.26 .050 241.88 243.91 245.95 248.01 249.26 250.10 .025 968.63 976.71 984.87 993.10 998.08 1001.4 .010 6055.8 6106.3 6157.3 6208.7 6239.8 6260.6 .001 605621 610668 615764 620908 624017 626099 2 .100 9.39 9.41 9.42 9.44 9.45 9.46 .050 19.40 19.41 19.43 19.45 19.46 19.46 .025 39.40 39.41 39.43 39.45 39.46 39.46 .010 99.40 99.42 99.43 99.45 99.46 99.47 .001 999.40 999.42 999.43 999.45 999.46 999.47 3 .100 5.23 5.22 5.20 5.18 5.17 5.17 .050 8.79 8.74 8.70 8.66 8.63 8.62 .025 14.42 14.34 14.25 14.17 14.12 14.08 .010 27.23 27.05 26.87 26.69 26.58 26.50 .001 129.25 128.32 127.37 126.42 125.84 125.45 4 .100 3.92 3.90 3.87 3.84 3.83 3.82 .050 5.96 5.91 5.86 5.80 5.77 5.75 .025 8.84 8.75 8.66 8.56 8.50 8.46 .010 14.55 14.37 14.20 14.02 13.91 13.84 .001 48.05 47.41 46.76 46.10 45.70 45.43 5 .100 3.30 3.27 3.24 3.21 3.19 3.17 .050 4.74 4.68 4.62 4.56 4.52 4.50 .025 6.62 6.52 6.43 6.33 6.27 6.23 .010 10.05 9.89 9.72 9.55 9.45 9.38 .001 26.92 26.42 25.91 25.39 25.08 24.87 6 .100 2.94 2.90 2.87 2.84 2.81 2.80 .050 4.06 4.00 3.94 3.87 3.83 3.81 .025 5.46 5.37 5.27 5.17 5.11 5.07 .010 7.87 7.72 7.56 7.40 7.30 7.23 .001 18.41 17.99 17.56 17.12 16.85 16.67 7 .100 2.70 2.67 2.63 2.59 2.57 2.56 .050 3.64 3.57 3.51 3.44 3.40 3.38 .025 4.76 4.67 4.57 4.47 4.40 4.36 .010 6.62 6.47 6.31 6.16 6.06 5.99 .001 14.08 13.71 13.32 12.93 12.69 12.53 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 40 50 60 120 1000 1 .100 62.53 62.69 62.79 63.06 63.30 .050 251.14 251.77 252.20 253.25 254.19 .025 1005.6 1008.1 1009.8 1014.0 1017.7 .010 6286.8 6302.5 6313.0 6339.4 6362.7 .001 628712 630285 631337 633972 636301 2 .100 9.47 9.47 9.47 9.48 9.49 .050 19.47 19.48 19.48 19.49 19.49 .025 39.47 39.48 39.48 39.49 39.50 .010 99.47 99.48 99.48 99.49 99.50 .001 999.47 999.48 999.48 999.49 999.50 3 .100 5.16 5.15 5.15 5.14 5.13 .050 8.59 8.58 8.57 8.55 8.53 .025 14.04 14.01 13.99 13.95 13.91 .010 26.41 26.35 26.32 26.22 26.14 .001 124.96 124.66 124.47 123.97 123.53 4 .100 3.80 3.80 3.79 3.78 3.76 .050 5.72 5.70 5.69 5.66 5.63 .025 8.41 8.38 8.36 8.31 8.26 .010 13.75 13.69 13.65 13.56 13.47 .001 45.09 44.88 44.75 44.40 44.09 5 .100 3.16 3.15 3.14 3.12 3.11 .050 4.46 4.44 4.43 4.40 4.37 .025 6.18 6.14 6.12 6.07 6.02 .010 9.29 9.24 9.20 9.11 9.03 .001 24.60 24.44 24.33 24.06 23.82 6 .100 2.78 2.77 2.76 2.74 2.72 .050 3.77 3.75 3.74 3.70 3.67 .025 5.01 4.98 4.96 4.90 4.86 .010 7.14 7.09 7.06 6.97 6.89 .001 16.44 16.31 16.21 15.98 15.77 7 .100 2.54 2.52 2.51 2.49 2.47 .050 3.34 3.32 3.30 3.27 3.23 .025 4.31 4.28 4.25 4.20 4.15 .010 5.91 5.86 5.82 5.74 5.66 .001 12.33 12.20 12.12 11.91 11.72 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 1 2 3 4 5 6 7 8 9 8 .100 3.46 3.11 2.92 2.81 2.73 2.67 2.62 2.59 2.56 .050 5.32 4.46 4.07 3.84 3.69 3.58 3.50 3.44 3.39 .025 7.57 6.06 5.42 5.05 4.82 4.65 4.53 4.43 4.36 .010 11.26 8.65 7.59 7.01 6.63 6.37 6.18 6.03 5.91 .001 25.41 18.49 15.83 14.39 13.48 12.86 12.40 12.05 11.77 9 .100 3.36 3.01 2.81 2.69 2.61 2.55 2.51 2.47 2.44 .050 5.12 4.26 3.86 3.63 3.48 3.37 3.29 3.23 3.18 .025 7.21 5.71 5.08 4.72 4.48 4.32 4.20 4.10 4.03 .010 10.56 8.02 6.99 6.42 6.06 5.80 5.61 5.47 5.35 .001 22.86 16.39 13.90 12.56 11.71 11.13 10.70 10.37 10.11 10 .100 3.29 2.92 2.73 2.61 2.52 2.46 2.41 2.38 2.35 .050 4.96 4.10 3.71 3.48 3.33 3.22 3.14 3.07 3.02 .025 6.94 5.46 4.83 4.47 4.24 4.07 3.95 3.85 3.78 .010 10.04 7.56 6.55 5.99 5.64 5.39 5.20 5.06 4.94 .001 21.04 14.91 12.55 11.28 10.48 9.93 9.52 9.20 8.96 11 .100 3.23 2.86 2.66 2.54 2.45 2.39 2.34 2.30 2.27 .050 4.84 3.98 3.59 3.36 3.20 3.09 3.01 2.95 2.90 .025 6.72 5.26 4.63 4.28 4.04 3.88 3.76 3.66 3.59 .010 9.65 7.21 6.22 5.67 5.32 5.07 4.89 4.74 4.63 .001 19.69 13.81 11.56 10.35 9.58 9.05 8.66 8.35 8.12 12 .100 3.18 2.81 2.61 2.48 2.39 2.33 2.28 2.24 2.21 .050 4.75 3.89 3.49 3.26 3.11 3.00 2.91 2.85 2.80 .025 6.55 5.10 4.47 4.12 3.89 3.73 3.61 3.51 3.44 .010 9.33 6.93 5.95 5.41 5.06 4.82 4.64 4.50 4.39 .001 18.64 12.97 10.80 9.63 8.89 8.38 8.00 7.71 7.48 13 .100 3.14 2.76 2.56 2.43 2.35 2.28 2.23 2.20 2.16 .050 4.67 3.81 3.41 3.18 3.03 2.92 2.83 2.77 2.71 .025 6.41 4.97 4.35 4.00 3.77 3.60 3.48 3.39 3.31 .010 9.07 6.70 5.74 5.21 4.86 4.62 4.44 4.30 4.19 .001 17.82 12.31 10.21 9.07 8.35 7.86 7.49 7.21 6.98 14 .100 3.10 2.73 2.52 2.39 2.31 2.24 2.19 2.15 2.12 .050 4.60 3.74 3.34 3.11 2.96 2.85 2.76 2.70 2.65 .025 6.30 4.86 4.24 3.89 3.66 3.50 3.38 3.29 3.21 .010 8.86 6.51 5.56 5.04 4.69 4.46 4.28 4.14 4.03 .001 17.14 11.78 9.73 8.62 7.92 7.44 7.08 6.80 6.58 15 .100 3.07 2.70 2.49 2.36 2.27 2.21 2.16 2.12 2.09 .050 4.54 3.68 3.29 3.06 2.90 2.79 2.71 2.64 2.59 .025 6.20 4.77 4.15 3.80 3.58 3.41 3.29 3.20 3.12 .010 8.68 6.36 5.42 4.89 4.56 4.32 4.14 4.00 3.89 .001 16.59 11.34 9.34 8.25 7.57 7.09 6.74 6.47 6.26 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 10 12 15 20 25 30 40 50 60 120 1000 8 .100 2.54 2.50 2.46 2.42 2.40 2.38 2.36 2.35 2.34 2.32 2.30 .050 3.35 3.28 3.22 3.15 3.11 3.08 3.04 3.02 3.01 2.97 2.93 .025 4.30 4.20 4.10 4.00 3.94 3.89 3.84 3.81 3.78 3.73 3.68 .010 5.81 5.67 5.52 5.36 5.26 5.20 5.12 5.07 5.03 4.95 4.87 .001 11.54 11.19 10.84 10.48 10.26 10.11 9.92 9.80 9.73 9.53 9.36 9 .100 2.42 2.38 2.34 2.30 2.27 2.25 2.23 2.22 2.21 2.18 2.16 .050 3.14 3.07 3.01 2.94 2.89 2.86 2.83 2.80 2.79 2.75 2.71 .025 3.96 3.87 3.77 3.67 3.60 3.56 3.51 3.47 3.45 3.39 3.34 .010 5.26 5.11 4.96 4.81 4.71 4.65 4.57 4.52 4.48 4.40 4.32 .001 9.89 9.57 9.24 8.90 8.69 8.55 8.37 8.26 8.19 8.00 7.84 10 .100 2.32 2.28 2.24 2.20 2.17 2.16 2.13 2.12 2.11 2.08 2.06 .050 2.98 2.91 2.85 2.77 2.73 2.70 2.66 2.64 2.62 2.58 2.54 .025 3.72 3.62 3.52 3.42 3.35 3.31 3.26 3.22 3.20 3.14 3.09 .010 4.85 4.71 4.56 4.41 4.31 4.25 4.17 4.12 4.08 4.00 3.92 .001 8.75 8.45 8.13 7.80 7.60 7.47 7.30 7.19 7.12 6.94 6.78 11 .100 2.25 2.21 2.17 2.12 2.10 2.08 2.05 2.04 2.03 2.00 1.98 .050 2.85 2.79 2.72 2.65 2.60 2.57 2.53 2.51 2.49 2.45 2.41 .025 3.53 3.43 3.33 3.23 3.16 3.12 3.06 3.03 3.00 2.94 2.89 .010 4.54 4.40 4.25 4.10 4.01 3.94 3.86 3.81 3.78 3.69 3.61 .001 7.92 7.63 7.32 7.01 6.81 6.68 6.52 6.42 6.35 6.18 6.02 12 .100 2.19 2.15 2.10 2.06 2.03 2.01 1.99 1.97 1.96 1.93 1.91 .050 2.75 2.69 2.62 2.54 2.50 2.47 2.43 2.40 2.38 2.34 2.30 .025 3.37 3.28 3.18 3.07 3.01 2.96 2.91 2.87 2.85 2.79 2.73 .010 4.30 4.16 4.01 3.86 3.76 3.70 3.62 3.57 3.54 3.45 3.37 .001 7.29 7.00 6.71 6.40 6.22 6.09 5.93 5.83 5.76 5.59 5.44 13 .100 2.14 2.10 2.05 2.01 1.98 1.96 1.93 1.92 1.90 1.88 1.85 .050 2.67 2.60 2.53 2.46 2.41 2.38 2.34 2.31 2.30 2.25 2.21 .025 3.25 3.15 3.05 2.95 2.88 2.84 2.78 2.74 2.72 2.66 2.60 .010 4.10 3.96 3.82 3.66 3.57 3.51 3.43 3.38 3.34 3.25 3.18 .001 6.80 6.52 6.23 5.93 5.75 5.63 5.47 5.37 5.30 5.14 4.99 14 .100 2.10 2.05 2.01 1.96 1.93 1.91 1.89 1.87 1.86 1.83 1.80 .050 2.60 2.53 2.46 2.39 2.34 2.31 2.27 2.24 2.22 2.18 2.14 .025 3.15 3.05 2.95 2.84 2.78 2.73 2.67 2.64 2.61 2.55 2.50 .010 3.94 3.80 3.66 3.51 3.41 3.35 3.27 3.22 3.18 3.09 3.02 .001 6.40 6.13 5.85 5.56 5.38 5.25 5.10 5.00 4.94 4.77 4.62 15 .100 2.06 2.02 1.97 1.92 1.89 1.87 1.85 1.83 1.82 1.79 1.76 .050 2.54 2.48 2.40 2.33 2.28 2.25 2.20 2.18 2.16 2.11 2.07 .025 3.06 2.96 2.86 2.76 2.69 2.64 2.59 2.55 2.52 2.46 2.40 .010 3.80 3.67 3.52 3.37 3.28 3.21 3.13 3.08 3.05 2.96 2.88 .001 6.08 5.81 5.54 5.25 5.07 4.95 4.80 4.70 4.64 4.47 4.33 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 1 2 3 4 5 6 7 8 9 16 .100 3.05 2.67 2.46 2.33 2.24 2.18 2.13 2.09 2.06 .050 4.49 3.63 3.24 3.01 2.85 2.74 2.66 2.59 2.54 .025 6.12 4.69 4.08 3.73 3.50 3.34 3.22 3.12 3.05 .010 8.53 6.23 5.29 4.77 4.44 4.20 4.03 3.89 3.78 .001 16.12 10.97 9.01 7.94 7.27 6.80 6.46 6.19 5.98 17 .100 3.03 2.64 2.44 2.31 2.22 2.15 2.10 2.06 2.03 .050 4.45 3.59 3.20 2.96 2.81 2.70 2.61 2.55 2.49 .025 6.04 4.62 4.01 3.66 3.44 3.28 3.16 3.06 2.98 .010 8.40 6.11 5.19 4.67 4.34 4.10 3.93 3.79 3.68 .001 15.72 10.66 8.73 7.68 7.02 6.56 6.22 5.96 5.75 18 .100 3.01 2.62 2.42 2.29 2.20 2.13 2.08 2.04 2.00 .050 4.41 3.55 3.16 2.93 2.77 2.66 2.58 2.51 2.46 .025 5.98 4.56 3.95 3.61 3.38 3.22 3.10 3.01 2.93 .010 8.29 6.01 5.09 4.58 4.25 4.01 3.84 3.71 3.60 .001 15.38 10.39 8.49 7.46 6.81 6.35 6.02 5.76 5.56 19 .100 3.36 3.01 2.81 2.69 2.61 2.55 2.51 2.47 2.44 .050 5.12 4.26 3.86 3.63 3.48 3.37 3.29 3.23 3.18 .025 7.21 5.71 5.08 4.72 4.48 4.32 4.20 4.10 4.03 .010 10.56 8.02 6.99 6.42 6.06 5.80 5.61 5.47 5.35 .001 22.86 16.39 13.90 12.56 11.71 11.13 10.70 10.37 10.11 20 .100 2.97 2.59 2.38 2.25 2.16 2.09 2.04 2.00 1.96 .050 4.35 3.49 3.10 2.87 2.71 2.60 2.51 2.45 2.39 .025 5.87 4.46 3.86 3.51 3.29 3.13 3.01 2.91 2.84 .010 8.10 5.85 4.94 4.43 4.10 3.87 3.70 3.56 3.46 .001 14.82 9.95 8.10 7.10 6.46 6.02 5.69 5.44 5.24 21 .100 2.96 2.57 2.36 2.23 2.14 2.08 2.02 1.98 1.95 .050 4.32 3.47 3.07 2.84 2.68 2.57 2.49 2.42 2.37 .025 5.83 4.42 3.82 3.48 3.25 3.09 2.97 2.87 2.80 .010 8.02 5.78 4.87 4.37 4.04 3.81 3.64 3.51 3.40 .001 14.59 9.77 7.94 6.95 6.32 5.88 5.56 5.31 5.11 22 .100 2.95 2.56 2.35 2.22 2.13 2.06 2.01 1.97 1.93 .050 4.30 3.44 3.05 2.82 2.66 2.55 2.46 2.40 2.34 .025 5.79 4.38 3.78 3.44 3.22 3.05 2.93 2.84 2.76 .010 7.95 5.72 4.82 4.31 3.99 3.76 3.59 3.45 3.35 .001 14.38 9.61 7.80 6.81 6.19 5.76 5.44 5.19 4.99 23 .100 2.94 2.55 2.34 2.21 2.11 2.05 1.99 1.95 1.92 .050 4.28 3.42 3.03 2.80 2.64 2.53 2.44 2.37 2.32 .025 5.75 4.35 3.75 3.41 3.18 3.02 2.90 2.81 2.73 .010 7.88 5.66 4.76 4.26 3.94 3.71 3.54 3.41 3.30 .001 14.20 9.47 7.67 6.70 6.08 5.65 5.33 5.09 4.89 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 10 12 15 20 25 30 40 50 60 120 1000 16 .100 2.03 1.99 1.94 1.89 1.86 1.84 1.81 1.79 1.78 1.75 1.72 .050 2.49 2.42 2.35 2.28 2.23 2.19 2.15 2.12 2.11 2.06 2.02 .025 2.99 2.89 2.79 2.68 2.61 2.57 2.51 2.47 2.45 2.38 2.32 .010 3.69 3.55 3.41 3.26 3.16 3.10 3.02 2.97 2.93 2.84 2.76 .001 5.81 5.55 5.27 4.99 4.82 4.70 4.54 4.45 4.39 4.23 4.08 17 .100 2.00 1.96 1.91 1.86 1.83 1.81 1.78 1.76 1.75 1.72 1.69 .050 2.45 2.38 2.31 2.23 2.18 2.15 2.10 2.08 2.06 2.01 1.97 .025 2.92 2.82 2.72 2.62 2.55 2.50 2.44 2.41 2.38 2.32 2.26 .010 3.59 3.46 3.31 3.16 3.07 3.00 2.92 2.87 2.83 2.75 2.66 .001 5.58 5.32 5.05 4.78 4.60 4.48 4.33 4.24 4.18 4.02 3.87 18 .100 1.98 1.93 1.89 1.84 1.80 1.78 1.75 1.74 1.72 1.69 1.66 .050 2.41 2.34 2.27 2.19 2.14 2.11 2.06 2.04 2.02 1.97 1.92 .025 2.87 2.77 2.67 2.56 2.49 2.44 2.38 2.35 2.32 2.26 2.20 .010 3.51 3.37 3.23 3.08 2.98 2.92 2.84 2.78 2.75 2.66 2.58 .001 5.39 5.13 4.87 4.59 4.42 4.30 4.15 4.06 4.00 3.84 3.69 19 .100 1.96 1.91 1.86 1.81 1.78 1.76 1.73 1.71 1.70 1.67 1.64 .050 2.38 2.31 2.23 2.16 2.11 2.07 2.03 2.00 1.98 1.93 1.88 .025 2.82 2.72 2.62 2.51 2.44 2.39 2.33 2.30 2.27 2.20 2.14 .010 3.43 3.30 3.15 3.00 2.91 2.84 2.76 2.71 2.67 2.58 2.50 .001 5.22 4.97 4.70 4.43 4.26 4.14 3.99 3.90 3.84 3.68 3.53 20 .100 1.94 1.89 1.84 1.79 1.76 1.74 1.71 1.69 1.68 1.64 1.61 .050 2.35 2.28 2.20 2.12 2.07 2.04 1.99 1.97 1.95 1.90 1.85 .025 2.77 2.68 2.57 2.46 2.40 2.35 2.29 2.25 2.22 2.16 2.09 .010 3.37 3.23 3.09 2.94 2.84 2.78 2.69 2.64 2.61 2.52 2.43 .001 5.08 4.82 4.56 4.29 4.12 4.00 3.86 3.77 3.70 3.54 3.40 21 .100 1.92 1.87 1.83 1.78 1.74 1.72 1.69 1.67 1.66 1.62 1.59 .050 2.32 2.25 2.18 2.10 2.05 2.01 1.96 1.94 1.92 1.87 1.82 .025 2.73 2.64 2.53 2.42 2.36 2.31 2.25 2.21 2.18 2.11 2.05 .010 3.31 3.17 3.03 2.88 2.79 2.72 2.64 2.58 2.55 2.46 2.37 .001 4.95 4.70 4.44 4.17 4.00 3.88 3.74 3.64 3.58 3.42 3.28 22 .100 1.90 1.86 1.81 1.76 1.73 1.70 1.67 1.65 1.64 1.60 1.57 .050 2.30 2.23 2.15 2.07 2.02 1.98 1.94 1.91 1.89 1.84 1.79 .025 2.70 2.60 2.50 2.39 2.32 2.27 2.21 2.17 2.14 2.08 2.01 .010 3.26 3.12 2.98 2.83 2.73 2.67 2.58 2.53 2.50 2.40 2.32 .001 4.83 4.58 4.33 4.06 3.89 3.78 3.63 3.54 3.48 3.32 3.17 23 .100 1.89 1.84 1.80 1.74 1.71 1.69 1.66 1.64 1.62 1.59 1.55 .050 2.27 2.20 2.13 2.05 2.00 1.96 1.91 1.88 1.86 1.81 1.76 .025 2.67 2.57 2.47 2.36 2.29 2.24 2.18 2.14 2.11 2.04 1.98 .010 3.21 3.07 2.93 2.78 2.69 2.62 2.54 2.48 2.45 2.35 2.27 .001 4.73 4.48 4.23 3.96 3.79 3.68 3.53 3.44 3.38 3.22 3.08 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 1 2 3 4 5 6 7 8 9 24 .100 2.93 2.54 2.33 2.19 2.10 2.04 1.98 1.94 1.91 .050 4.26 3.40 3.01 2.78 2.62 2.51 2.42 2.36 2.30 .025 5.72 4.32 3.72 3.38 3.15 2.99 2.87 2.78 2.70 .010 7.82 5.61 4.72 4.22 3.90 3.67 3.50 3.36 3.26 .001 14.03 9.34 7.55 6.59 5.98 5.55 5.23 4.99 4.80 25 .100 2.92 2.53 2.32 2.18 2.09 2.02 1.97 1.93 1.89 .050 4.24 3.39 2.99 2.76 2.60 2.49 2.40 2.34 2.28 .025 5.69 4.29 3.69 3.35 3.13 2.97 2.85 2.75 2.68 .010 7.77 5.57 4.68 4.18 3.85 3.63 3.46 3.32 3.22 .001 13.88 9.22 7.45 6.49 5.89 5.46 5.15 4.91 4.71 26 .100 2.91 2.52 2.31 2.17 2.08 2.01 1.96 1.92 1.88 .050 4.23 3.37 2.98 2.74 2.59 2.47 2.39 2.32 2.27 .025 5.66 4.27 3.67 3.33 3.10 2.94 2.82 2.73 2.65 .010 7.72 5.53 4.64 4.14 3.82 3.59 3.42 3.29 3.18 .001 13.74 9.12 7.36 6.41 5.80 5.38 5.07 4.83 4.64 27 .100 2.90 2.51 2.30 2.17 2.07 2.00 1.95 1.91 1.87 .050 4.21 3.35 2.96 2.73 2.57 2.46 2.37 2.31 2.25 .025 5.63 4.24 3.65 3.31 3.08 2.92 2.80 2.71 2.63 .010 7.68 5.49 4.60 4.11 3.78 3.56 3.39 3.26 3.15 .001 13.61 9.02 7.27 6.33 5.73 5.31 5.00 4.76 4.57 28 .100 2.89 2.50 2.29 2.16 2.06 2.00 1.94 1.90 1.87 .050 4.20 3.34 2.95 2.71 2.56 2.45 2.36 2.29 2.24 .025 5.61 4.22 3.63 3.29 3.06 2.90 2.78 2.69 2.61 .010 7.64 5.45 4.57 4.07 3.75 3.53 3.36 3.23 3.12 .001 13.50 8.93 7.19 6.25 5.66 5.24 4.93 4.69 4.50 29 .100 2.89 2.50 2.28 2.15 2.06 1.99 1.93 1.89 1.86 .050 4.18 3.33 2.93 2.70 2.55 2.43 2.35 2.28 2.22 .025 5.59 4.20 3.61 3.27 3.04 2.88 2.76 2.67 2.59 .010 7.60 5.42 4.54 4.04 3.73 3.50 3.33 3.20 3.09 .001 13.39 8.85 7.12 6.19 5.59 5.18 4.87 4.64 4.45 30 .100 2.88 2.49 2.28 2.14 2.05 1.98 1.93 1.88 1.85 .050 4.17 3.32 2.92 2.69 2.53 2.42 2.33 2.27 2.21 .025 5.57 4.18 3.59 3.25 3.03 2.87 2.75 2.65 2.57 .010 7.56 5.39 4.51 4.02 3.70 3.47 3.30 3.17 3.07 .001 13.29 8.77 7.05 6.12 5.53 5.12 4.82 4.58 4.39 40 .100 2.84 2.44 2.23 2.09 2.00 1.93 1.87 1.83 1.79 .050 4.08 3.23 2.84 2.61 2.45 2.34 2.25 2.18 2.12 .025 5.42 4.05 3.46 3.13 2.90 2.74 2.62 2.53 2.45 .010 7.31 5.18 4.31 3.83 3.51 3.29 3.12 2.99 2.89 .001 12.61 8.25 6.59 5.70 5.13 4.73 4.44 4.21 4.02 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 10 12 15 20 25 30 40 50 60 120 1000 24 .100 1.88 1.83 1.78 1.73 1.70 1.67 1.64 1.62 1.61 1.57 1.54 .050 2.25 2.18 2.11 2.03 1.97 1.94 1.89 1.86 1.84 1.79 1.74 .025 2.64 2.54 2.44 2.33 2.26 2.21 2.15 2.11 2.08 2.01 1.94 .010 3.17 3.03 2.89 2.74 2.64 2.58 2.49 2.44 2.40 2.31 2.22 .001 4.64 4.39 4.14 3.87 3.71 3.59 3.45 3.36 3.29 3.14 2.99 25 .100 1.87 1.82 1.77 1.72 1.68 1.66 1.63 1.61 1.59 1.56 1.52 .050 2.24 2.16 2.09 2.01 1.96 1.92 1.87 1.84 1.82 1.77 1.72 .025 2.61 2.51 2.41 2.30 2.23 2.18 2.12 2.08 2.05 1.98 1.91 .010 3.13 2.99 2.85 2.70 2.60 2.54 2.45 2.40 2.36 2.27 2.18 .001 4.56 4.31 4.06 3.79 3.63 3.52 3.37 3.28 3.22 3.06 2.91 26 .100 1.86 1.81 1.76 1.71 1.67 1.65 1.61 1.59 1.58 1.54 1.51 .050 2.22 2.15 2.07 1.99 1.94 1.90 1.85 1.82 1.80 1.75 1.70 .025 2.59 2.49 2.39 2.28 2.21 2.16 2.09 2.05 2.03 1.95 1.89 .010 3.09 2.96 2.81 2.66 2.57 2.50 2.42 2.36 2.33 2.23 2.14 .001 4.48 4.24 3.99 3.72 3.56 3.44 3.30 3.21 3.15 2.99 2.84 27 .100 1.85 1.80 1.75 1.70 1.66 1.64 1.60 1.58 1.57 1.53 1.50 .050 2.20 2.13 2.06 1.97 1.92 1.88 1.84 1.81 1.79 1.73 1.68 .025 2.57 2.47 2.36 2.25 2.18 2.13 2.07 2.03 2.00 1.93 1.86 .010 3.06 2.93 2.78 2.63 2.54 2.47 2.38 2.33 2.29 2.20 2.11 .001 4.41 4.17 3.92 3.66 3.49 3.38 3.23 3.14 3.08 2.92 2.78 28 .100 1.84 1.79 1.74 1.69 1.65 1.63 1.59 1.57 1.56 1.52 1.48 .050 2.19 2.12 2.04 1.96 1.91 1.87 1.82 1.79 1.77 1.71 1.66 .025 2.55 2.45 2.34 2.23 2.16 2.11 2.05 2.01 1.98 1.91 1.84 .010 3.03 2.90 2.75 2.60 2.51 2.44 2.35 2.30 2.26 2.17 2.08 .001 4.35 4.11 3.86 3.60 3.43 3.32 3.18 3.09 3.02 2.86 2.72 29 .100 1.83 1.78 1.73 1.68 1.64 1.62 1.58 1.56 1.55 1.51 1.47 .050 2.18 2.10 2.03 1.94 1.89 1.85 1.81 1.77 1.75 1.70 1.65 .025 2.53 2.43 2.32 2.21 2.14 2.09 2.03 1.99 1.96 1.89 1.82 .010 3.00 2.87 2.73 2.57 2.48 2.41 2.33 2.27 2.23 2.14 2.05 .001 4.29 4.05 3.80 3.54 3.38 3.27 3.12 3.03 2.97 2.81 2.66 30 .100 1.82 1.77 1.72 1.67 1.63 1.61 1.57 1.55 1.54 1.50 1.46 .050 2.16 2.09 2.01 1.93 1.88 1.84 1.79 1.76 1.74 1.68 1.63 .025 2.51 2.41 2.31 2.20 2.12 2.07 2.01 1.97 1.94 1.87 1.80 .010 2.98 2.84 2.70 2.55 2.45 2.39 2.30 2.25 2.21 2.11 2.02 .001 4.24 4.00 3.75 3.49 3.33 3.22 3.07 2.98 2.92 2.76 2.61 40 .100 1.76 1.71 1.66 1.61 1.57 1.54 1.51 1.48 1.47 1.42 1.38 .050 2.08 2.00 1.92 1.84 1.78 1.74 1.69 1.66 1.64 1.58 1.52 .025 2.39 2.29 2.18 2.07 1.99 1.94 1.88 1.83 1.80 1.72 1.65 .010 2.80 2.66 2.52 2.37 2.27 2.20 2.11 2.06 2.02 1.92 1.82 .001 3.87 3.64 3.40 3.14 2.98 2.87 2.73 2.64 2.57 2.41 2.25 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 1 2 3 4 5 6 7 8 9 50 .100 2.81 2.41 2.20 2.06 1.97 1.90 1.84 1.80 1.76 .050 4.03 3.18 2.79 2.56 2.40 2.29 2.20 2.13 2.07 .025 5.34 3.97 3.39 3.05 2.83 2.67 2.55 2.46 2.38 .010 7.17 5.06 4.20 3.72 3.41 3.19 3.02 2.89 2.78 .001 12.22 7.96 6.34 5.46 4.90 4.51 4.22 4.00 3.82 60 .100 2.79 2.39 2.18 2.04 1.95 1.87 1.82 1.77 1.74 .050 4.00 3.15 2.76 2.53 2.37 2.25 2.17 2.10 2.04 .025 5.29 3.93 3.34 3.01 2.79 2.63 2.51 2.41 2.33 .010 7.08 4.98 4.13 3.65 3.34 3.12 2.95 2.82 2.72 .001 11.97 7.77 6.17 5.31 4.76 4.37 4.09 3.86 3.69 100 .100 2.76 2.36 2.14 2.00 1.91 1.83 1.78 1.73 1.69 .050 3.94 3.09 2.70 2.46 2.31 2.19 2.10 2.03 1.97 .025 5.18 3.83 3.25 2.92 2.70 2.54 2.42 2.32 2.24 .010 6.90 4.82 3.98 3.51 3.21 2.99 2.82 2.69 2.59 .001 11.50 7.41 5.86 5.02 4.48 4.11 3.83 3.61 3.44 200 .100 2.73 2.33 2.11 1.97 1.88 1.80 1.75 1.70 1.66 .050 3.89 3.04 2.65 2.42 2.26 2.14 2.06 1.98 1.93 .025 5.10 3.76 3.18 2.85 2.63 2.47 2.35 2.26 2.18 .010 6.76 4.71 3.88 3.41 3.11 2.89 2.73 2.60 2.50 .001 11.15 7.15 5.63 4.81 4.29 3.92 3.65 3.43 3.26 1000 .100 2.71 2.31 2.09 1.95 1.85 1.78 1.72 1.68 1.64 .050 3.85 3.00 2.61 2.38 2.22 2.11 2.02 1.95 1.89 .025 5.04 3.70 3.13 2.80 2.58 2.42 2.30 2.20 2.13 .010 6.66 4.63 3.80 3.34 3.04 2.82 2.66 2.53 2.43 .001 10.89 6.96 5.46 4.65 4.14 3.78 3.51 3.30 3.13 F critical values (continued) Degrees of freedom in the numerator Degrees of freedom in the denominator p 10 12 15 20 25 30 40 50 60 120 1000 50 .100 1.73 1.68 1.63 1.57 1.53 1.50 1.46 1.44 1.42 1.38 1.33 .050 2.03 1.95 1.87 1.78 1.73 1.69 1.63 1.60 1.58 1.51 1.45 .025 2.32 2.22 2.11 1.99 1.92 1.87 1.80 1.75 1.72 1.64 1.56 .010 2.70 2.56 2.42 2.27 2.17 2.10 2.01 1.95 1.91 1.80 1.70 .001 3.67 3.44 3.20 2.95 2.79 2.68 2.53 2.44 2.38 2.21 2.05 60 .100 1.71 1.66 1.60 1.54 1.50 1.48 1.44 1.41 1.40 1.35 1.30 .050 1.99 1.92 1.84 1.75 1.69 1.65 1.59 1.56 1.53 1.47 1.40 .025 2.27 2.17 2.06 1.94 1.87 1.82 1.74 1.70 1.67 1.58 1.49 .010 2.63 2.50 2.35 2.20 2.10 2.03 1.94 1.88 1.84 1.73 1.62 .001 3.54 3.32 3.08 2.83 2.67 2.55 2.41 2.32 2.25 2.08 1.92 100 .100 1.66 1.61 1.56 1.49 1.45 1.42 1.38 1.35 1.34 1.28 1.22 .050 1.93 1.85 1.77 1.68 1.62 1.57 1.52 1.48 1.45 1.38 1.30 .025 2.18 2.08 1.97 1.85 1.77 1.71 1.64 1.59 1.56 1.46 1.36 .010 2.50 2.37 2.22 2.07 1.97 1.89 1.80 1.74 1.69 1.57 1.45 .001 3.30 3.07 2.84 2.59 2.43 2.32 2.17 2.08 2.01 1.83 1.64 200 .100 1.63 1.58 1.52 1.46 1.41 1.38 1.34 1.31 1.29 1.23 1.16 .050 1.88 1.80 1.72 1.62 1.56 1.52 1.46 1.41 1.39 1.30 1.21 .025 2.11 2.01 1.90 1.78 1.70 1.64 1.56 1.51 1.47 1.37 1.25 .010 2.41 2.27 2.13 1.97 1.87 1.79 1.69 1.63 1.58 1.45 1.30 .001 3.12 2.90 2.67 2.42 2.26 2.15 2.00 1.90 1.83 1.64 1.43 1000 .100 1.61 1.55 1.49 1.43 1.38 1.35 1.30 1.27 1.25 1.18 1.08 .050 1.84 1.76 1.68 1.58 1.52 1.47 1.41 1.36 1.33 1.24 1.11 .025 2.06 1.96 1.85 1.72 1.64 1.58 1.50 1.45 1.41 1.29 1.13 .010 2.34 2.20 2.06 1.90 1.79 1.72 1.61 1.54 1.50 1.35 1.16 .001 2.99 2.77 2.54 2.30 2.14 2.02 1.87 1.77 1.69 1.49 1.22 Numerical entries represent the probability that a standard normal random variable is between 0 and z where z = x − μ σ . Standard Normal Probability Distribution: Z Table Standard Normal Distribution z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.0 0.0000 0.0040 0.0080 0.0120 0.0160 0.0199 0.0239 0.0279 0.0319 0.0359 0.1 0.0398 0.0438 0.0478 0.0517 0.0557 0.0596 0.0636 0.0675 0.0714 0.0753 0.2 0.0793 0.0832 0.0871 0.0910 0.0948 0.0987 0.1026 0.1064 0.1103 0.1141 0.3 0.1179 0.1217 0.1255 0.1293 0.1331 0.1368 0.1406 0.1443 0.1480 0.1517 0.4 0.1554 0.1591 0.1628 0.1664 0.1700 0.1736 0.1772 0.1808 0.1844 0.1879 0.5 0.1915 0.1950 0.1985 0.2019 0.2054 0.2088 0.2123 0.2157 0.2190 0.2224 0.6 0.2257 0.2291 0.2324 0.2357 0.2389 0.2422 0.2454 0.2486 0.2517 0.2549 0.7 0.2580 0.2611 0.2642 0.2673 0.2704 0.2734 0.2764 0.2794 0.2823 0.2852 0.8 0.2881 0.2910 0.2939 0.2967 0.2995 0.3023 0.3051 0.3078 0.3106 0.3133 0.9 0.3159 0.3186 0.3212 0.3238 0.3264 0.3289 0.3315 0.3340 0.3365 0.3389 1.0 0.3413 0.3438 0.3461 0.3485 0.3508 0.3531 0.3554 0.3577 0.3599 0.3621 1.1 0.3643 0.3665 0.3686 0.3708 0.3729 0.3749 0.3770 0.3790 0.3810 0.3830 1.2 0.3849 0.3869 0.3888 0.3907 0.3925 0.3944 0.3962 0.3980 0.3997 0.4015 1.3 0.4032 0.4049 0.4066 0.4082 0.4099 0.4115 0.4131 0.4147 0.4162 0.4177 1.4 0.4192 0.4207 0.4222 0.4236 0.4251 0.4265 0.4279 0.4292 0.4306 0.4319 1.5 0.4332 0.4345 0.4357 0.4370 0.4382 0.4394 0.4406 0.4418 0.4429 0.4441 1.6 0.4452 0.4463 0.4474 0.4484 0.4495 0.4505 0.4515 0.4525 0.4535 0.4545 1.7 0.4554 0.4564 0.4573 0.4582 0.4591 0.4599 0.4608 0.4616 0.4625 0.4633 1.8 0.4641 0.4649 0.4656 0.4664 0.4671 0.4678 0.4686 0.4693 0.4699 0.4706 1.9 0.4713 0.4719 0.4726 0.4732 0.4738 0.4744 0.4750 0.4756 0.4761 0.4767 2.0 0.4772 0.4778 0.4783 0.4788 0.4793 0.4798 0.4803 0.4808 0.4812 0.4817 2.1 0.4821 0.4826 0.4830 0.4834 0.4838 0.4842 0.4846 0.4850 0.4854 0.4857 2.2 0.4861 0.4864 0.4868 0.4871 0.4875 0.4878 0.4881 0.4884 0.4887 0.4890 2.3 0.4893 0.4896 0.4898 0.4901 0.4904 0.4906 0.4909 0.4911 0.4913 0.4916 2.4 0.4918 0.4920 0.4922 0.4925 0.4927 0.4929 0.4931 0.4932 0.4934 0.4936 2.5 0.4938 0.4940 0.4941 0.4943 0.4945 0.4946 0.4948 0.4949 0.4951 0.4952 2.6 0.4953 0.4955 0.4956 0.4957 0.4959 0.4960 0.4961 0.4962 0.4963 0.4964 2.7 0.4965 0.4966 0.4967 0.4968 0.4969 0.4970 0.4971 0.4972 0.4973 0.4974 2.8 0.4974 0.4975 0.4976 0.4977 0.4977 0.4978 0.4979 0.4979 0.4980 0.4981 2.9 0.4981 0.4982 0.4982 0.4983 0.4984 0.4984 0.4985 0.4985 0.4986 0.4986 3.0 0.4987 0.4987 0.4987 0.4988 0.4988 0.4989 0.4989 0.4989 0.4990 0.4990 3.1 0.4990 0.4991 0.4991 0.4991 0.4992 0.4992 0.4992 0.4992 0.4993 0.4993 3.2 0.4993 0.4993 0.4994 0.4994 0.4994 0.4994 0.4994 0.4995 0.4995 0.4995 3.3 0.4995 0.4995 0.4995 0.4996 0.4996 0.4996 0.4996 0.4996 0.4996 0.4997 3.4 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4998 Student's t Distribution Upper critical values of Student's t Distribution with v Degrees of Freedom For selected probabilities, a , the table shows the values t v,a such that P ( t v > t v,a ) = a , where t v is a Student’s t random variable with v degrees of freedom. For example, the probability is .10 that a Student’s t random variable with 10 degrees of freedom exceeds 1.372. Probability of Exceeding the Critical Value v 0.10 0.05 0.025 0.01 0.005 0.001 1 3.078 6.314 12.706 31.821 63.657 318.313 2 1.886 2.920 4.303 6.965 9.925 22.327 3 1.638 2.353 3.182 4.541 5.841 10.215 4 1.533 2.132 2.776 3.747 4.604 7.173 5 1.476 2.015 2.571 3.365 4.032 5.893 6 1.440 1.943 2.447 3.143 3.707 5.208 7 1.415 1.895 2.365 2.998 3.499 4.782 8 1.397 1.860 2.306 2.896 3.355 4.499 9 1.383 1.833 2.262 2.821 3.250 4.296 10 1.372 1.812 2.228 2.764 3.169 4.143 11 1.363 1.796 2.201 2.718 3.106 4.024 12 1.356 1.782 2.179 2.681 3.055 3.929 13 1.350 1.771 2.160 2.650 3.012 3.852 14 1.345 1.761 2.145 2.624 2.977 3.787 15 1.341 1.753 2.131 2.602 2.947 3.733 16 1.337 1.746 2.120 2.583 2.921 3.686 17 1.333 1.740 2.110 2.567 2.898 3.646 18 1.330 1.734 2.101 2.552 2.878 3.610 19 1.328 1.729 2.093 2.539 2.861 3.579 20 1.325 1.725 2.086 2.528 2.845 3.552 21 1.323 1.721 2.080 2.518 2.831 3.527 22 1.321 1.717 2.074 2.508 2.819 3.505 23 1.319 1.714 2.069 2.500 2.807 3.485 24 1.318 1.711 2.064 2.492 2.797 3.467 25 1.316 1.708 2.060 2.485 2.787 3.450 26 1.315 1.706* 2.056 2.479 2.779 3.435 27 1.314 1.703 2.052 2.473 2.771 3.421 28 1.313 1.701 2.048 2.467 2.763 3.408 29 1.311 1.699 2.045 2.462 2.756 3.396 30 1.310 1.697 2.042 2.457 2.750 3.385 40 1.303 1.684 2.021 2.423 2.704 3.307 60 1.296 1.671 2.000 2.390 2.660 3.232 100 1.290 1.660 1.984 2.364 2.626 3.174 ∞ 1.282 1.645 1.960 2.326 2.576 3.090 NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/, September 2011. χ 2 Probability Distribution Area to the Right of the Critical Value of χ 2 df 0.995 0.990 0.975 0.950 0.900 0.100 0.050 0.025 0.010 0.005 1 0.000 0.000 0.001 0.004 0.016 2.706 3.841 5.024 6.635 7.879 2 0.010 0.020 0.051 0.103 0.211 4.605 5.991 7.378 9.210 10.597 3 0.072 0.115 0.216 0.352 0.584 6.251 7.815 9.348 11.345 12.838 4 0.207 0.297 0.484 0.711 1.064 7.779 9.488 11.143 13.277 14.860 5 0.412 0.554 0.831 1.145 1.610 9.236 11.070 12.833 15.086 16.750 6 0.676 0.872 1.237 1.635 2.204 10.645 12.592 14.449 16.812 18.548 7 0.989 1.239 1.690 2.167 2.833 12.017 14.067 16.013 18.475 20.278 8 1.344 1.646 2.180 2.733 3.490 13.362 15.507 17.535 20.090 21.955 9 1.735 2.088 2.700 3.325 4.168 14.684 16.919 19.023 21.666 23.589 10 2.156 2.558 3.247 3.940 4.865 15.987 18.307 20.483 23.209 25.188 11 2.603 3.053 3.816 4.575 5.578 17.275 19.675 21.920 24.725 26.757 12 3.074 3.571 4.404 5.226 6.304 18.549 21.026 23.337 26.217 28.300 13 3.565 4.107 5.009 5.892 7.042 19.812 22.362 24.736 27.688 29.819 14 4.075 4.660 5.629 6.571 7.790 21.064 23.685 26.119 29.141 31.319 15 4.601 5.229 6.262 7.261 8.547 22.307 24.996 27.488 30.578 32.801 16 5.142 5.812 6.908 7.962 9.312 23.542 26.296 28.845 32.000 34.267 17 5.697 6.408 7.564 8.672 10.085 24.769 27.587 30.191 33.409 35.718 18 6.265 7.015 8.231 9.390 10.865 25.989 28.869 31.526 34.805 37.156 19 6.844 7.633 8.907 10.117 11.651 27.204 30.144 32.852 36.191 38.582 20 7.434 8.260 9.591 10.851 12.443 28.412 31.410 34.170 37.566 39.997 21 8.034 8.897 10.283 11.591 13.240 29.615 32.671 35.479 38.932 41.401 22 8.643 9.542 10.982 12.338 14.041 30.813 33.924 36.781 40.289 42.796 23 9.260 10.196 11.689 13.091 14.848 32.007 35.172 38.076 41.638 44.181 24 9.886 10.856 12.401 13.848 15.659 33.196 36.415 39.364 42.980 45.559 25 10.520 11.524 13.120 14.611 16.473 34.382 37.652 40.646 44.314 46.928 26 11.160 12.198 13.844 15.379 17.292 35.563 38.885 41.923 45.642 48.290 27 11.808 12.879 14.573 16.151 18.114 36.741 40.113 43.195 46.963 49.645 28 12.461 13.565 15.308 16.928 18.939 37.916 41.337 44.461 48.278 50.993 29 13.121 14.256 16.047 17.708 19.768 39.087 42.557 45.722 49.588 52.336 30 13.787 14.953 16.791 18.493 20.599 40.256 43.773 46.979 50.892 53.672 40 20.707 22.164 24.433 26.509 29.051 51.805 55.758 59.342 63.691 66.766 50 27.991 29.707 32.357 34.764 37.689 63.167 67.505 71.420 76.154 79.490 60 35.534 37.485 40.482 43.188 46.459 74.397 79.082 83.298 88.379 91.952 70 43.275 45.442 48.758 51.739 55.329 85.527 90.531 95.023 100.425 104.215 80 51.172 53.540 57.153 60.391 64.278 96.578 101.879 106.629 112.329 116.321 90 59.196 61.754 65.647 69.126 73.291 107.565 113.145 118.136 124.116 128.299 100 67.328 70.065 74.222 77.929 82.358 118.498 124.342 129.561 135.807 140.169 Data Sets Lap Times The following tables provide lap times from Terri Vogel's log book. Times are recorded in seconds for 2.5-mile laps completed in a series of races and practice runs. Race Lap Times (in seconds) Lap 1 Lap 2 Lap 3 Lap 4 Lap 5 Lap 6 Lap 7 Race 1 135 130 131 132 130 131 133 Race 2 134 131 131 129 128 128 129 Race 3 129 128 127 127 130 127 129 Race 4 125 125 126 125 124 125 125 Race 5 133 132 132 132 131 130 132 Race 6 130 130 130 129 129 130 129 Race 7 132 131 133 131 134 134 131 Race 8 127 128 127 130 128 126 128 Race 9 132 130 127 128 126 127 124 Race 10 135 131 131 132 130 131 130 Race 11 132 131 132 131 130 129 129 Race 12 134 130 130 130 131 130 130 Race 13 128 127 128 128 128 129 128 Race 14 132 131 131 131 132 130 130 Race 15 136 129 129 129 129 129 129 Race 16 129 129 129 128 128 129 129 Race 17 134 131 132 131 132 132 132 Race 18 129 129 130 130 133 133 127 Race 19 130 129 129 129 129 129 128 Race 20 131 128 130 128 129 130 130 Practice Lap Times (in seconds) Lap 1 Lap 2 Lap 3 Lap 4 Lap 5 Lap 6 Lap 7 Practice 1 142 143 180 137 134 134 172 Practice 2 140 135 134 133 128 128 131 Practice 3 130 133 130 128 135 133 133 Practice 4 141 136 137 136 136 136 145 Practice 5 140 138 136 137 135 134 134 Practice 6 142 142 139 138 129 129 127 Practice 7 139 137 135 135 137 134 135 Practice 8 143 136 134 133 134 133 132 Practice 9 135 134 133 133 132 132 133 Practice 10 131 130 128 129 127 128 127 Practice 11 143 139 139 138 138 137 138 Practice 12 132 133 131 129 128 127 126 Practice 13 149 144 144 139 138 138 137 Practice 14 133 132 137 133 134 130 131 Practice 15 138 136 133 133 132 131 131 Stock Prices The following table lists initial public offering (IPO) stock prices for all 1999 stocks that at least doubled in value during the first day of trading. IPO Offer Prices $17.00 $23.00 $14.00 $16.00 $12.00 $26.00 $20.00 $22.00 $14.00 $15.00 $22.00 $18.00 $18.00 $21.00 $21.00 $19.00 $15.00 $21.00 $18.00 $17.00 $15.00 $25.00 $14.00 $30.00 $16.00 $10.00 $20.00 $12.00 $16.00 $17.44 $16.00 $14.00 $15.00 $20.00 $20.00 $16.00 $17.00 $16.00 $15.00 $15.00 $19.00 $48.00 $16.00 $18.00 $9.00 $18.00 $18.00 $20.00 $8.00 $20.00 $17.00 $14.00 $11.00 $16.00 $19.00 $15.00 $21.00 $12.00 $8.00 $16.00 $13.00 $14.00 $15.00 $14.00 $13.41 $28.00 $21.00 $17.00 $28.00 $17.00 $19.00 $16.00 $17.00 $19.00 $18.00 $17.00 $15.00 $14.00 $21.00 $12.00 $18.00 $24.00 $15.00 $23.00 $14.00 $16.00 $12.00 $24.00 $20.00 $14.00 $14.00 $15.00 $14.00 $19.00 $16.00 $38.00 $20.00 $24.00 $16.00 $8.00 $18.00 $17.00 $16.00 $15.00 $7.00 $19.00 $12.00 $8.00 $23.00 $12.00 $18.00 $20.00 $21.00 $34.00 $16.00 $26.00 $14.00 References Data compiled by Jay R. Ritter of University of Florida using data from Securities Data Co. and Bloomberg .

Business


Mathematical Phrases, Symbols, and Formulas English Phrases Written Mathematically When the English says: Interpret this as: X is at least 4. X ≥ 4 The minimum of X is 4. X ≥ 4 X is no less than 4. X ≥ 4 X is greater than or equal to 4. X ≥ 4 X is at most 4. X ≤ 4 The maximum of X is 4. X ≤ 4 X is no more than 4. X ≤ 4 X is less than or equal to 4. X ≤ 4 X does not exceed 4. X ≤ 4 X is greater than 4. X > 4 X is more than 4. X > 4 X exceeds 4. X > 4 X is less than 4. X < 4 There are fewer X than 4. X < 4 X is 4. X = 4 X is equal to 4. X = 4 X is the same as 4. X = 4 X is not 4. X ≠ 4 X is not equal to 4. X ≠ 4 X is not the same as 4. X ≠ 4 X is different than 4. X ≠ 4 Symbols and Their Meanings Symbols and their Meanings Chapter (1st used) Symbol Spoken Meaning Sampling and Data The square root of same Sampling and Data π Pi 3.14159… (a specific number) Descriptive Statistics Q 1 Quartile one the first quartile Descriptive Statistics Q 2 Quartile two the second quartile Descriptive Statistics Q 3 Quartile three the third quartile Descriptive Statistics IQR interquartile range Q 3 – Q 1 = IQR Descriptive Statistics x – x-bar sample mean Descriptive Statistics μ mu population mean Descriptive Statistics s s sample standard deviation Descriptive Statistics s 2 s squared sample variance Descriptive Statistics σ sigma population standard deviation Descriptive Statistics σ 2 sigma squared population variance Descriptive Statistics Σ capital sigma sum Probability Topics { } brackets set notation Probability Topics S S sample space Probability Topics A Event A event A Probability Topics P ( A ) probability of A probability of A occurring Probability Topics P ( A | B ) probability of A given B prob. of A occurring given B has occurred Probability Topics P ( A ∪ B ) prob. of A or B prob. of A or B or both occurring Probability Topics P ( A ∩ B ) prob. of A and B prob. of both A and B occurring (same time) Probability Topics A ′ A-prime, complement of A complement of A, not A Probability Topics P ( A ') prob. of complement of A same Probability Topics G 1 green on first pick same Probability Topics P ( G 1 ) prob. of green on first pick same Discrete Random Variables PDF prob. density function same Discrete Random Variables X X the random variable X Discrete Random Variables X ~ the distribution of X same Discrete Random Variables ≥ greater than or equal to same Discrete Random Variables ≤ less than or equal to same Discrete Random Variables = equal to same Discrete Random Variables ≠ not equal to same Continuous Random Variables f ( x ) f of x function of x Continuous Random Variables pdf prob. density function same Continuous Random Variables U uniform distribution same Continuous Random Variables Exp exponential distribution same Continuous Random Variables f ( x ) = f of x equals same Continuous Random Variables m m decay rate (for exp. dist.) The Normal Distribution N normal distribution same The Normal Distribution z z -score same The Normal Distribution Z standard normal dist. same The Central Limit Theorem X – X -bar the random variable X -bar The Central Limit Theorem μ x – mean of X -bars the average of X -bars The Central Limit Theorem σ x – standard deviation of X -bars same Confidence Intervals CL confidence level same Confidence Intervals CI confidence interval same Confidence Intervals EBM error bound for a mean same Confidence Intervals EBP error bound for a proportion same Confidence Intervals t Student's t -distribution same Confidence Intervals df degrees of freedom same Confidence Intervals t α 2 student t with α /2 area in right tail same Confidence Intervals p ′ p -prime sample proportion of success Confidence Intervals q ′ q -prime sample proportion of failure Hypothesis Testing H 0 H -naught, H -sub 0 null hypothesis Hypothesis Testing H a H-a , H -sub a alternate hypothesis Hypothesis Testing H 1 H -1, H -sub 1 alternate hypothesis Hypothesis Testing α alpha probability of Type I error Hypothesis Testing β beta probability of Type II error Hypothesis Testing X 1 – – X 2 ¯ X 1-bar minus X 2-bar difference in sample means Hypothesis Testing μ 1 − μ 2 mu -1 minus mu -2 difference in population means Hypothesis Testing P ′ 1 − P ′ 2 P 1-prime minus P 2-prime difference in sample proportions Hypothesis Testing p 1 − p 2 p 1 minus p 2 difference in population proportions Chi-Square Distribution Χ 2 Ky -square Chi-square Chi-Square Distribution O Observed Observed frequency Chi-Square Distribution E Expected Expected frequency Linear Regression and Correlation y = a + bx y equals a plus b-x equation of a straight line Linear Regression and Correlation y ^ y -hat estimated value of y Linear Regression and Correlation r "r" same Linear Regression and Correlation ρ rho ("row") population correlation coefficient Linear Regression and Correlation ε error term for a regression line same Linear Regression and Correlation SSE Sum of Squared Errors same F -Distribution and ANOVA F F -ratio F -ratio Formulas Symbols you must know Population Sample N Size n μ Mean x _ σ 2 Variance s 2 σ Standard deviation s p Proportion p ′ Single data set formulae Population Sample μ = E ( x ) = 1 N ∑ i = 1 N ( x i ) Arithmetic mean x – = 1 n ∑ i = 1 n ( x i ) Geometric mean x ~ = ( ∏ i = 1 n X i ) 1 n Q 3 = 3 ( n + 1 ) 4 , Q 1 = ( n + 1 ) 4 Inter-quartile range I Q R = Q 3 − Q 1 Q 3 = 3 ( n + 1 ) 4 , Q 1 = ( n + 1 ) 4 σ 2 = 1 N ∑ i = 1 N ( x i − μ ) 2 Variance s 2 = 1 n ∑ i = 1 n ( x i − x _ ) 2 Single data set formulae Population Sample μ = E ( x ) = 1 N ∑ i = 1 N ( m i · f i ) Arithmetic mean x – = 1 n ∑ i = 1 n ( m i · f i ) Geometric mean x ~ = ( ∏ i = 1 n X i ) 1 n σ 2 = 1 N ∑ i = 1 N ( m i − μ ) 2 · f i Variance s 2 = 1 n ∑ i = 1 n ( m i − x _ ) 2 · f i C V = σ μ · 100 Coefficient of variation C V = s x _ · 100 Basic probability rules P ( A ∩ B ) = P ( A | B ) · P ( B ) Multiplication rule P ( A ∪ B ) = P ( A ) + P ( B ) − P ( A ∩ B ) Addition rule P ( A ∩ B ) = P ( A ) · P ( B ) or P ( A | B ) = P ( A ) Independence test Hypergeometric distribution formulae n C x = ( n x ) = n ! x ! ( n − x ) ! Combinatorial equation P ( x ) = ( A x ) ( N − A n − x ) ( N n ) Probability equation E ( X ) = μ = n p Mean σ 2 = ( N − n N − 1 ) n p ( q ) Variance Binomial distribution formulae P ( x ) = n ! x ! ( n − x ) ! p x ( q ) n − x Probability density function E ( X ) = μ = n p Arithmetic mean σ 2 = n p ( q ) Variance Geometric distribution formulae P ( X = x ) = ( 1 − p ) x − 1 ( p ) Probability when x is the first success. Probability when x is the number of failures before first success P ( X = x ) = ( 1 − p ) x ( p ) μ = 1 p Mean Mean μ = 1 − p p σ 2 = ( 1 − p ) p 2 Variance Variance σ 2 = ( 1 − p ) p 2 Poisson distribution formulae P ( x ) = e − μ μ x x ! Probability equation E ( X ) = μ Mean σ 2 = μ Variance Uniform distribution formulae f ( x ) = 1 b − a for a ≤ x ≤ b PDF E ( X ) = μ = a + b 2 Mean σ 2 = ( b − a ) 2 12 Variance Exponential distribution formulae P ( X ≤ x ) = 1 − e − m x Cumulative probability E ( X ) = μ = 1 m or m = 1 μ Mean and decay factor σ 2 = 1 m 2 = μ 2 Variance The following page of formulae requires the use of the " Z ", " t ", " χ 2 " or " F " tables. Z = x − μ σ Z-transformation for normal distribution Z = x − n p ′ n p ′ ( q ′ ) Normal approximation to the binomial Probability (ignores subscripts) Hypothesis testing Confidence intervals [bracketed symbols equal margin of error] (subscripts denote locations on respective distribution tables) Z c = x ¯ - μ 0 σ n Interval for the population mean when sigma is known x ¯ ± [ Z ( α / 2 ) σ n ] Z c = x ¯ - μ 0 s n Interval for the population mean when sigma is unknown but n > 30 x ¯ ± [ Z ( α / 2 ) s n ] t c = x ¯ - μ 0 s n Interval for the population mean when sigma is unknown but n < 30 x ¯ ± [ t ( n − 1 ) , ( α / 2 ) s n ] Z c = p ′ - p 0 p 0 q 0 n Interval for the population proportion p ′ ± [ Z ( α / 2 ) p ′ q ′ n ] t c = d – - δ 0 s d n Interval for difference between two means with matched pairs d – ± [ t ( n − 1 ) , ( α / 2 ) s d n ] where s d is the deviation of the differences Z c = ( x 1 – - x 2 – ) − δ 0 σ 1 2 n 1 + σ 2 2 n 2 Interval for difference between two means when sigmas are known ( x 1 – - x 2 – ) ± [ Z ( α / 2 ) σ 1 2 n 1 + σ 2 2 n 2 ] t c = ( x ¯ 1 - x ¯ 2 ) - δ 0 ( ( s 1 ) 2 n 1 + ( s 2 ) 2 n 2 ) Interval for difference between two means with equal variances when sigmas are unknown ( x ¯ 1 - x ¯ 2 ) ± [ t d f , ( α / 2 ) ( ( s 1 ) 2 n 1 + ( s 2 ) 2 n 2 ) ] where d f = ( ( s 1 ) 2 n 1 + ( s 2 ) 2 n 2 ) 2 ( 1 n 1 − 1 ) ( ( s 1 ) 2 n 1 ) + ( 1 n 2 − 1 ) ( ( s 2 ) 2 n 2 ) Z c = ( p ′ 1 - p ′ 2 ) − δ 0 p ′ 1 ( q ′ 1 ) n 1 + p ′ 2 ( q ′ 2 ) n 2 Interval for difference between two population proportions ( p ′ 1 - p ′ 2 ) ± [ Z ( α / 2 ) p ′ 1 ( q ′ 1 ) n 1 + p ′ 2 ( q ′ 2 ) n 2 ] χ c 2 = ( n − 1 ) s 2 σ 0 2 Tests for GOF, Independence, and Homogeneity χ c 2 = Σ ( O − E ) 2 E where O = observed values and E = expected values F c = s 1 2 s 2 2 Where s 1 2 is the sample variance which is the larger of the two sample variances The next 3 formulae are for determining sample size with confidence intervals. (note: E represents the margin of error) n = Z ( a 2 ) 2 σ 2 E 2 Use when sigma is known E = x ¯ − μ n = Z ( a 2 ) 2 ( 0.25 ) E 2 Use when p ′ is unknown E = p ′ − p n = Z ( a 2 ) 2 [ p ′ ( q ′ ) ] E 2 Use when p ′ is unknown E = p ′ − p Simple linear regression formulae for y = a + b ( x ) r = Σ [ ( x − x ¯ ) ( y − y ¯ ) ] Σ ( x − x ¯ ) 2 * Σ ( y − y ¯ ) 2 = S x y S x S y = S S R S S T Correlation coefficient b = Σ [ ( x − x ¯ ) ( y − y ¯ ) ] Σ ( x − x ¯ ) 2 = S x y S S x = r y , x ( s y s x ) Coefficient b (slope) a = y ¯ − b ( x ¯ ) y-intercept s e 2 = Σ ( y i − y ^ i ) 2 n − k = Σ i = 1 n e i 2 n − k Estimate of the error variance S b = s e 2 ( x i − x ¯ ) 2 = s e 2 ( n − 1 ) s x 2 Standard error for coefficient b t c = b − β 0 s b Hypothesis test for coefficient β b ± [ t n − 2 , α / 2 S b ] Interval for coefficient β y ^ ± [ t α / 2 * s e ( 1 n + ( x p − x ¯ ) 2 s x ) ] Interval for expected value of y y ^ ± [ t α / 2 * s e ( 1 + 1 n + ( x p − x ¯ ) 2 s x ) ] Prediction interval for an individual y ANOVA formulae S S R = Σ i = 1 n ( y ^ i − y ¯ ) 2 Sum of squares regression S S E = Σ i = 1 n ( y ^ i − y ¯ i ) 2 Sum of squares error S S T = Σ i = 1 n ( y i − y ¯ ) 2 Sum of squares total R 2 = S S R S S T Coefficient of determination The following is the breakdown of a one-way ANOVA table for linear regression. Source of variation Sum of squares Degrees of freedom Mean squares F -ratio Regression S S R 1 or k − 1 M S R = S S R d f R F = M S R M S E Error S S E n − k M S E = S S E d f E Total S S T n − 1

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This is a traditional approach to organizational behavior. The table of contents of this book was designed to address two main themes. What are the variables that affect how, when, where, and why managers perform their jobs? What theories and techniques are used by successful managers at a variety of organizational levels to achieve and exceed objectives effectively and efficiently throughout their careers? Management is a broad business discipline, and the Organizational Behavior course covers many areas such as individual and group behavior at work, as well as organizational processes such as communication in the workplace and managing conflict and negotiation. No one individual can be an expert in all areas of management, so an additional benefit of this text is that specialists in a variety of areas have authored individual chapters. Finally, we all made an effort to present a balanced approach to gender and diversity throughout the text in the examples used, the photographs selected, and the use of both male and female in alternating chapters when referring to generic managers or employees. Pedagogical Foundation We have taken a structured approach in the writing of the chapters that reduces inconsistencies throughout and makes selecting topics to match the course syllabus easier for faculty. Exploring Managerial Careers. Each chapter starts with a profile that describes a manager and illustrates how the content of the chapter is vital for a successful managerial career. Consistent, integrated learning. Targeted learning outcomes are listed at the beginning of each chapter and then repeated throughout the chapter. The learning outcomes connect to the text and the additional resources that accompany Organizational Behavior. After reading each section, students can test their retention by answering the questions in the Concept Checks. Every learning goal is further reinforced by a summary at the end of the chapter. Hundreds of business examples to bring concepts to life. This book is designed to speak to the typical student. We have done a lot of research about student needs, abilities, experiences, and interests, and then we have shaped the text around them. We have used experiences both inside and outside the classroom to create a book that is both readable and enjoyable. We believe that the real applications found throughout every chapter set the standard for readability and understanding of key concepts. Learning business terminology, made easy. As students begin to study management, they will explore new words and concepts. To help them learn this language, we define each new term in the chapter, display the terms in bold, and offer a complete glossary at the end of the book. 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This feature highlights the challenges and opportunities available in small businesses and other entrepreneurial ventures. Managerial Leadership. It is generally agreed that in a turbulent business climate leadership is an important function of management that helps to maximize efficiency and to achieve organizational goals. Leaders initiate action, motivate organizations, provide guidance, build morale, and create a sense of confidence within the organization and to outside stakeholders. Sustainability and Responsible Management. This feature highlights the knowledge, skills, tools, and self-awareness that are needed to become responsible managers. While the area of corporate social responsibility and sustainability has gained wide general support and commentary, these featured boxed items should provide the reader with insights of how managers can embed responsible practices in their careers. 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Contributing Authors J. Stewart Black, INSEAD David S. Bright, Wright State University Donald G. Gardner, University of Colorado-Colorado Springs Eva Hartmann, University of Richmond Jason Lambert, Texas Woman’s University Laura M. Leduc, James Madison University Joy Leopold, Webster University James S. O’Rourke, University of Notre Dame Jon L. Pierce, University of Minnesota-Duluth Richard M. Steers, University of Oregon Siri Terjesen, American University Joseph Weiss, Bentley University Reviewers Susan Adams, Bentley University Shane Bowyer, Minnesota State University Kim S. Cameron, University of Michigan Stephen J. Carroll, University of Maryland Daniel R. Cillis, Molloy College Linda Davenport, Klamath Community College Diana L. Deadrick, Old Dominion University James J. Freiburger, Southern New Hampshire University Robert A. Giacalone, John Carroll University Gregory O. Ginn, Embry-Riddle Aeronautical University John Goldberg, University of California-Davis Brian Graham-Moore, University of Texas Regina Greenwood, Nova University William F. Grossnickle, East Carolina University Nell Tabor Hartley, Robert Morris University Nai H. Lamb, University of Tennessee at Chattanooga Kristie J. Loescher, University of Texas Marcia Marriott, Monroe Community College Therese Madden, Notre Dame de Namur University Eleonor Moore, Kirtland Community College Bonnie L. McNeely, Murray State University Robert McNulty, Bentley University Jeffrey Muldoon, Emporia State University Karli Peterson, Colorado State University Raymond Pfang, Tarrant Community College Jodell Raymond, Monroe Community College Richard Savior, SUNY Empire State Amit Shah, Frostburg State University Paul L. Starkey, Pennsylvania College of Technology Carolyn Stevenson, Kaplan University Dianna L. Stone, University of New Mexico Maria Vitale, Chaffey College Valerie Wallingford, Bemidji State University

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Introduction Construction workers (leyla.a/ flickr/ Attribution 2.0 Generic (CC BY 2.0)) Learning Outcomes After reading this chapter, you should be able to answer these questions: What is the meaning of work in a societal context? How do we recognize and meet the challenges facing managers in the new millennium? What is expected of a manager? What is the role of the behavioral sciences in management and organizations? The Management Challenge at Apple and Google When Apple was developing iOS 10, a group of 600 engineers was able to debug, develop, and deploy the new programming within two years. Contrarily, Microsoft engineers were able to develop and execute the programming on Vista, but it took considerably longer and was a bigger undertaking, with almost 6,000 engineers at hand. What was the difference? According to the study conducted by leadership consulting firm Bain & Company, companies like Apple, Google, and Netflix are 40 percent more productive than the average company. Some may think that this is a product of the hiring pool; big companies generally attract a more talented group of recruits. With unique benefits and prowess in the industry, this must be the case. Wrong. Google and Apple have found a way to answer the most fundamental question in management: How do you balance productivity while maintaining employee satisfaction and commitment? Companies such as Google have approximately the same percentage of “star players” as other companies, but instead of spreading out the talent, they group them dynamically to achieve more throughout the day. This grouping focuses on grouping key players in the most business-critical roles, and is the key to success for the overall company. You’ve heard the saying “You’re only as strong as your weakest link,” and in the case of Apple, there were no weak links, making their productivity extremely high overall. To make matters more complicated, the fast-paced workplace and technology changes, including the diversity of employees and the global marketplace, takes a considerable toll on employee expectations, as do the overall stresses of the business performance. Apple is just one example of a company that figured out one of the pieces to this puzzle, but it is illustrative of what is happening in the workplace all around the globe. Contemporary managers are witnessing changes in technologies, markets, competition, workforce demographics, employee expectations, and ethical standards. At the heart of these changes is the issue of how to manage people effectively. To attain corporate objectives, each manager must discover how to develop and maintain a workforce that can meet today’s needs while getting ready for tomorrow’s challenges. As a result, managers are asking questions such as: How can we meet the international competition? How can we make this organization more effective? How can we better utilize our human resources? How can we create a more satisfying and rewarding work environment for all employees? How can we improve the quality of our products? How can we improve communication and decision-making processes at work? How should we evaluate and reward performance? How can we develop the company leaders of tomorrow? Questions such as these point to the issue of effective management. That is, what can managers do to improve both organizational and employee performance? Effective management requires an in-depth knowledge of financial management, marketing research and consumer behavior, accounting and control practices, manufacturing and production techniques, and quantitative methods. In addition, however, effective management requires “people skills.” That is, a good manager must be able to motivate his employees, to lead skillfully, to make appropriate and timely decisions, to communicate effectively, to organize work, to deal with organizational politics, and to work to develop both employees and the organization as a whole. These issues constitute the subject of this course. We shall examine principles of the behavioral sciences that can help managers improve both their own skills and abilities and those of their subordinates in order to enhance organizational performance and effectiveness. As a prelude to this analysis, we begin with a brief look at the natures of work and of management. Contemporary challenges are discussed. Next, we consider a model of organizational behavior that will serve as a guide throughout the study of management and organizational behavior. We begin with an examination of work.

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The Nature of Work What is the meaning of work in a societal context? The Meaning of Work What is work, and how do people feel about the work they do? These questions may be answered from several perspectives. Perhaps one of the best ways to understand how people feel about their jobs is simply to ask them. A number of years ago Chicago writer Studs Terkel did exactly that. How did the people he interviewed feel about their jobs? Here are some excerpts from his book Working . S. Terkel, Working (New York: Pantheon, 1974). I’m a dying breed. . . . A laborer. Strictly muscle work . . . pick it up, put it down, pick it up, put it down . . . you can’t take pride any more. You remember when a guy could point to a house he built, how many logs he stacked. He built it and he was proud of it. —Steelworker [p. 1] I changed my opinion of receptionists because now I’m one. It wasn’t the dumb broad at the front desk who took telephone messages. She had to be something else because I thought I was something else. I was fine until there was a press party. We were having a fairly intelligent conversation. Then they asked me what I did. When I told them, they turned around to find other people with name tags. I wasn’t worth bothering with. I wasn’t being rejected because of what I said or the way I talked, but simply because of my function. —Receptionist [p. 57] People ask me what I do, I say, “I drive a garbage truck for the city.” . . . I have nothing to be ashamed of. I put in my eight hours. We make a pretty good salary. I feel I earn my money. . . . My wife’s happy; this is the big thing. She doesn’t look down at me. I think that’s more important than the white-collar guy looking down at me. —Sanitation Truck Driver [p. 149] I’m human. I make mistakes like everybody else. If you want a robot, build machines. If you want human beings, that’s what I am. —Policeman [p. 186] I usually say I’m an accountant. Most people think it’s somebody who sits there with a green eyeshade and his sleeves rolled up with a garter, poring over books, adding things—with glasses. I suppose a certified public accountant has status. It doesn’t mean much to me. Do I like the job or don’t I? That’s important. —Accountant [p. 351] The boss . . . lost his secretary. She got promoted. So they told this old timekeeper she’s to be his secretary-assistant. Oh, she’s in her glory. No more money or anything and she’s doing two jobs all day long. She’s rushin’ and runnin’ all the time, all day. She’s a nervous wreck. And when she asked him to write her up for an award, he refused. That’s her reward for being so faithful, obedient. —Process Clerk [p. 461] Examples such as these—and there are many, many more—show how some employees view their jobs and the work they perform. Obviously, some jobs are more meaningful than others, and some individuals are more easily satisfied than others. Some people live to work, while others simply work to live. In any case, people clearly have strong feelings about what they do on the job and about the people with whom they work. In our study of behavior in organizations, we shall examine what people do, what causes them to do it, and how they feel about what they do. As a prelude to this analysis, however, we should first consider the basic unit of analysis in this study: work itself. What is work, and what functions does it serve in today’s society? Work has a variety of meanings in contemporary society. Often we think of work as paid employment—the exchange of services for money. Although this definition may suffice in a technical sense, it does not adequately describe why work is necessary. Perhaps work could be more meaningfully defined as an activity that produces something of value for other people. This definition broadens the scope of work and emphasizes the social context in which the wage-effort bargain transpires. It clearly recognizes that work has purpose—it is productive. Of course, this is not to say that work is necessarily interesting or rewarding or satisfying. On the contrary, we know that many jobs are dull, repetitive, and stressful. Even so, the activities performed do have utility for society at large. One of the challenges of management is to discover ways of transforming necessary yet distasteful jobs into more meaningful situations that are more satisfying and rewarding for individuals and that still contribute to organizational productivity and effectiveness. Functions of Work We know why work activities are important from an organization’s viewpoint. Without work there is no product or service to provide. But why is work important to individuals? What functions does it serve? First, work serves a rather obvious economic function. In exchange for labor, individuals receive necessary income with which to support themselves and their families. But people work for many reasons beyond simple economic necessity. Second, work also serves several social functions. The workplace provides opportunities for meeting new people and developing friendships. Many people spend more time at work with their co-workers than they spend at home with their own families. Third, work also provides a source of social status in the community. One’s occupation is a clue to how one is regarded on the basis of standards of importance prescribed by the community. For instance, in the United States a corporate president is generally accorded greater status than a janitor in the same corporation. In China, on the other hand, great status is ascribed to peasants and people from the working class, whereas managers are not so significantly differentiated from those they manage. In Japan, status is first a function of the company you work for and how well-known it is, and then the position you hold. It is important to note here that the status associated with the work we perform often transcends the boundaries of our organization. A corporate president or a university president may have a great deal of status in the community at large because of his position in the organization. Hence, the work we do can simultaneously represent a source of social differentiation and a source of social integration. Fourth, work can be an important source of identity and self-esteem and, for some, a means for self-actualization. It provides a sense of purpose for individuals and clarifies their value or contribution to society. As Freud noted long ago, “Work has a greater effect than any other technique of living in binding the individual more closely to reality; in his work he is at least securely attached to a part of reality, the human community.” S. Freud, Lecture XXXIII, New Introductory Lectures on Psychoanalysis (New York: Norton, 1933), p. 34. Work contributes to self-esteem in at least two ways. First, it provides individuals with an opportunity to demonstrate competence or mastery over themselves and their environment. Individuals discover that they can actually do something. Second, work reassures individuals that they are carrying out activities that produce something of value to others—that they have something significant to offer. Without this, the individual feels that they have little to contribute and is thus of little value to society. We clearly can see that work serves several useful purposes from an individual’s standpoint. It provides a degree of economic self-sufficiency, social interchange, social status, self-esteem, and identity. Without this, individuals often experience sensations of powerlessness, meaninglessness, and normlessness—a condition called alienation . In work, individuals have the possibility of finding some meaning in their day-to-day activities—if, of course, their work is sufficiently challenging. When employees are not involved in their jobs because the work is not challenging enough, they usually see no reason to apply themselves, which, of course, jeopardizes productivity and organizational effectiveness. This self-evident truth has given rise to a general concern among managers about declining productivity and work values. In fact, concern about this situation has caused many managers to take a renewed interest in how the behavioral sciences can help them solve many of the problems of people at work. Define work. What functions does work serve in modern society? What is the meaning of work in a societal context? Work will almost inevitably be a large part of your life. An understanding of organizational behavior will aid you in making that part of life more productive and enjoyable for yourself as well those you are in a position to influence. In this course, our objective is to provide sound and relevant insights concerning individuals, groups, and overall organizational systems that will be helpful to you not just as an executive or CEO but also when you are starting your career as an individual contributor or subordinate. Alienation The experience of being isolated from a group or an activity to which one should belong, or in which one should be involved. Work All activity involving mental or physical effort done in order to achieve a purpose or result.

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The Changing Workplace How do we recognize and meet the challenges facing managers in the new millennium? It has often been said that the only constant in life is change, and nowhere is this truer than in the workplace. As one recent study concluded, “The United States is a competitive location to the extent that firms operating in the U.S. are able to compete successfully in the global economy while supporting high and rising living standards for the average American. Although the U.S. retains profound competitive strengths—for instance, in higher education and entrepreneurship—those strengths are increasingly threatened by weaknesses in areas such as the tax code, basic education, macroeconomic policies, and regulation.” Michael E. Porter and Jan V. Rivkin, The Looming Challenge to U.S. Competitiveness, Harvard Business Review, March 2012. Companies face a variety of changes and challenges that will have a profound impact on organizational dynamics and performance. In fact, in many ways these changes and challenges will determine who will survive and prosper into the next century and who will not. Among these challenges are the following: The Challenge of International Competition Until the 1980s, many American firms had little in the way of serious international competition. As a result, there was little incentive to innovate and remain efficient and competitive. Many companies became lazy and lost touch with their customers. This situation changed abruptly as companies in Asia and Western Europe developed more sophisticated products and marketing systems and gained significant market shares in home electronics, automobiles, medical equipment, telecommunications, and shipbuilding, to name a few areas. As a result, American companies lost considerable clout—and profitability. In the 1990s and into the new millennium, the lowering of trade barriers and acceptance of trade agreements like NAFTA led corporations to seek less expensive labor overseas. This led to lower costs and the ability to offer products at more competitive prices, but also led to a drop in manufacturing in industries like steel production, a drop in manufacturing of products like iPhones, and the relocation of call centers from the U.S. to India. If we examine corporate behavior during the early decades of the new millennium, it is not difficult to see some of the reasons for the demise. In short, many North American firms lost their industrial competitiveness ; that is, they lost their capacity to compete effectively in global markets, or they chose to locate in foreign countries as a way to broaden their reach and become more competitive. Consider the following examples: World Economic Outlook Database, International Monetary Fund. Retrieved 2018-07-15. During the last year reported, India experienced a 7.5 percent annual growth rate in real GDP while China recorded an increase of 6.7 percent. This is a measure of how economies are progressing. Great Britain, France, and Italy all had close to 2 percent increases. At the same time, however, the United States recorded a 3.8 percent annual increase (and Canada had a 3 percent increase), a larger increase after a lethargic recovery from the 2009 financial crisis. While traditional jobs have shifted to developing countries, countries like the United States and Canada have transformed their economies by incorporating more technology and automation as well as having a greater proportion of the workforce in the service sectors. It is anticipated that the coming decades will continue to bring disruption to traditional workplace skills that will result in challenging workers to continually evolve their skills. Finally, the number of products that were invented in the United States but are now primarily manufactured overseas has increased dramatically—advances in technology are helping the United States regain the top spot in world manufacturing. There had been a significant decline in our manufacturing sector as less expensive labor in markets like India and China led companies to locate factories there. Since 2010, however, the United States has risen from fourth place to second and is expected to claim the spot as the leading nation by 2020. The major reasons for this are: advanced manufacturing capabilities require fewer “line workers,” and having products produced near their major markets reduces transport and time to market. Considering several indicators of the relative competitiveness of economies using seven metrics, the U.S. performs quite well. The seven metrics are institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, goods market efficiency, and labor market efficiency. When taking all of these factors into consideration (see ), the United States ranks very well and has an environment of stable growth. One challenge is that workers will need to be nimble and evolve as new skills arise and will need to embrace continuous education and training as a way of managing their careers. Global Competitive Index Rank Country/Economy Score Distance from Best Source: Adapted from World Economic Forum, “Global Competitiveness Index,” http://reports.weforum.org/global-competitiveness-index-2017-2018/competitiveness-rankings/#series=GCI, accessed July 19, 2018. 1 Switzerland 5.9 0.00% from best 2 United States 5.9 0.09% from best 3 Singapore 5.7 2.60% from best 4 Netherlands 5.7 3.34% from best 5 Germany 5.7 3.46% from best 6 Hong Kong SAR 5.5 5.56% from best 7 Sweden 5.5 5.78% from best 8 United Kingdom 5.5 5.99% from best 9 Japan 5.5 6.19% from best 10 Finland 5.5 6.29% from best (Attribution: Copyright Rice University, OpenStax, under CC BY NC-SA-NC-SA 4.0 license) In terms of organizational survival, herein lies what is perhaps management’s biggest challenge: how to become more competitive. Greater competitiveness requires an understanding of individuals, groups, and entire organizational systems. Throughout this course, we shall see numerous examples of how companies from around the world are meeting the challenges of global competition. Particular emphasis will be placed on management practices in other countries as a point of comparison. The Challenge of New Technologies Although it is common to think of “high tech” as applying only to the aerospace and telecommunications industries, advanced technologies can be found throughout most industries. For example, most of us are familiar with the explosive growth in computing. Both hardware and software change so rapidly that it is difficult for many companies to keep up. Personal computers are being replaced by cell phones that are now faster and more powerful than their predecessors. Cloud computing and access to big data and applications transform data into useful information that is increasingly complex and increasingly user friendly. In November of 1971 Intel launched the first microchip. Today, a modern Intel Skylake processor contains around 1.75 billion transistors—half a million of them would fit on a single transistor from the 4004—and collectively they deliver about 400,000 times as much computing muscle. “The Future of Computing,” The Economist, March 12, 2015, https://www.economist.com/leaders/2016/03/12/the-future-of-computing. More and more companies are using computer-based systems and equipment—such as e-mail, real-time messaging and file sharing, PDAs, and cell phones—for communications. As a result, the way in which employees and managers communicate and make decisions is changing dramatically, and the importance of educated and knowledgeable workers is increasing rapidly. Technological changes also can be seen in the increased use of robotics, expert systems, and computer-integrated manufacturing systems, which have changed the way many products are manufactured today. Such changes affect not only production efficiency and product quality but also the nature of jobs. In many industries, the first-line supervisors are disappearing and being replaced by self-managing work teams who assume responsibility for production scheduling, quality control, and even performance appraisals. All of these technological changes require managers who are capable of effectively implementing technological change in the workplace—managers who can adapt to the technological imperative while still maintaining and developing the organization’s human resources. We will examine the role of technology as it relates to organization structure, job design, communication, decision-making, and work-related stress. We will see how some companies successfully adapted to technological change in a way that benefited all parties concerned. Siri Struggles to Keep Up with the Competition Many executives struggle in the ongoing competitive landscape of technology. With fast-paced changes, staying one step ahead as well as being able to pivot quickly to respond to action are two critical elements to successful leadership. Apple Inc. has made its third change in the past year to the leadership of the artificial intelligence voice-assistance system Siri. Due to many factors, including being outperformed by the competition such as Google Assistant and Amazon Inc.’s Alexa, the company decided to pivot and make the change. These two systems have seen incredible growth in 2018, with the Amazon Echo and Google Home claiming each 34 percent of the market. Now John Giannandrea, formerly Google’s head of search and AI, has joined the Apple team and is tasked with getting on the rival’s level from which he came (Verge 2018). He will be challenged not only by having a new culture and company to fit into, but also by finding a good balance on how to innovate in his new role, as well as taking the best practices that he has from his previous role and applying it to boost the success of the Apple artificial intelligence. Keys to his success will be how quickly he can adapt to the new role, learning, adapting, and making changes along the way to bring Apple back to the playing field of artificial intelligence. Question 1: What other challenges would a new executive have coming from a competing company? Question 2: How much change is too much? What cautions should Apple be concerned about with all of the turnover for this position? Sources: Nick Statt, “Apple’s New AI Chief Now Oversees Siri, Core ML, and Machine Learning Teams,” The Verge , July 10, 2018, https://www.theverge.com/2018/7/10/17555652/apple-siri-ai-john-giannandrea-machine-learning-core-ml-teams; Stephen Nellis, “Apple Shifts Responsibility For Siri to Operating System Chief,” Reuters , September 1, 2017, https://www.reuters.com/article/us-apple-siri/apple-shifts-responsibility-for-siri-to-operating-system-chief-idUSKCN1BC65B; Tripp Mickle, Apple Hands Siri Responsibility to Executive Poached from Google,” The Wall Street Journal , July 10, 2018, https://www.wsj.com/articles/apple-hands-siri-responsibilities-to-executive-poached-from-google-1531261759. The Challenge of Increased Quality The challenge of industrial competitiveness incorporates several interrelated factors, including an appropriate product mix, manufacturing efficiency, effective cost controls, investment in research and development, and so forth. Not to be ignored in this pursuit is the quest for increased quality control of the products and services offered in the marketplace. Total Quality Management (TQM) is a term often used to describe comprehensive efforts to monitor and improve all aspects of quality within a firm. BMW established and continues to maintain its reputation in part because customers have come to respect its high level of quality. Quality is also a major reason for the success of many Japanese products in North America. Simply put, if companies are going to compete, renewed efforts must be devoted to enhanced quality assurance. This, too, is a management challenge. How can managers get employees to care about the products they produce or the services they offer? In this book, we will consider both the issue of quality control (what is it?) and mechanisms of ensuring improved product quality (how do we get it?). Moreover, quality control includes several organizational issues. For instance, how can managers get parties who are traditionally independently associated with a product to work together to build a better product? That is, how can they get the design staff, manufacturing engineers, workers, suppliers—and potential customers—to come together and cooperate in developing and manufacturing a superior product? Later in the book we will examine several instances in which such teamwork played a major role in quality improvement. The Challenge of Employee Motivation and Commitment A major hurdle in the pursuit of industrial competitiveness is the traditional adversarial relationship between management and workers. Whether a company is unionized or not, we see situations in which the average employee simply sees no reason to increase output or to improve the quality of existing outputs. Frequently, the company’s reward system restricts, rather than increases, performance. At other times, rewards encourage employees to increase quantity at the expense of quality. Furthermore, North American companies often view their workforce as a variable expense (in contrast to Japan, where the workforce is viewed as a fixed expense) and lay workers off when they are not needed for short-run activities. As a result, returning the favor, employees see little reason to be committed or loyal to their employers. Turnover and absenteeism rates are often unreasonably high, further eroding performance efficiency and effectiveness. If companies are to succeed in an increasingly turbulent environment, managers must discover better ways to develop and motivate employees. A company’s human resources often represent its biggest single asset, and failing to properly nurture this asset leads to suboptimal return on an organization’s resources. Part of solving this problem involves knowing and understanding today’s employees. illustrates the various characteristics employees consider important in their employers. Overall, employees seem to have a fairly positive outlook on their employers. As illustrated in , however, many millennials do not see their tenure lasting for a long period and expect to have another job soon. How Employees View Their Employers Source: Adapted from Deloitte, “2016 Deloitte Millennial Survey,” accessed July 18, 2018, https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/gx-millenial-survey-2016-exec-summary.pdf. (Attribution: Copyright Rice University, OpenStax, under CC BY NC-SA-NC-SA 4.0 license) Millennials and the Workplace Source: Adapted from Deloitte, “2016 Deloitte Millennial Survey,” accessed July 18, 2018, https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/gx-millenial-survey-2016-exec-summary.pdf. (Attribution: Copyright Rice University, OpenStax, under CC BY NC-SA-NC-SA 4.0 license) This problem is made all the more difficult by the changing nature of occupations. As shown in , we are seeing a sharp increase in the number of technicians, service workers, and sales workers. Growth also can be expected in engineering and managerial positions. These changes require a new look at how such employees are motivated. For example, do we motive an engineer the same way we motivate a sales representative? How do we motivate senior executives as opposed to junior managers? In this book, we shall touch on these issues when we examine approaches to employee motivation. Managers have at their disposal several ways in which to increase employee motivation and performance, and an effective manager learns how and when to use each approach. The Fastest-Growing Occupations Occupation Growth Rate 2016–2026 2017 Median Pay Source: “Fastest Growing Occupations,” Occupational Outlook Handbook, Bureau of Labor Statistics, https://www.bls.gov/ooh/fastest-growing.htm, accessed July 18, 2018. Solar photovoltaic installers 105% $39,490 Wind turbine service technicians 96% $53,580 Home health aides 47% $23,210 Personal care aids 39% $23,110 Physician assistants 37% $104,860 Nurse practitioner 36% $103,880 Statistician 34% $84,060 Physical therapist assistant 31% $57,440 Software developers, applications 31% $101,790 Mathematicians 30% $103,010 Physical therapist aides 29% $25,730 Bicycle repairs 29% $28,390 Medical assistants 29% $32,480 (Attribution: Copyright Rice University, OpenStax, under CC BY NC-SA-NC-SA 4.0 license) The Challenge of Managing a Diverse Workforce Historically, the American economy has been dominated by White males. They have filled the vast majority of managerial positions and many of the more important blue-collar jobs, becoming skilled craftsmen. Traditionally, women filled lower-paying clerical positions and often left the workforce to raise their families. Minorities of both genders found considerable barriers to entering the labor market at the higher (and higher-paying) levels. Now, things are changing, and the pace of this change is accelerating. Among other changes, the twenty-first century will also bring major changes in terms of workforce demographics. We will see changes in gender, race, and age. Kaisee Permanente The winner of the E Pluribus Unum Corporate Leadership Award, Kaiser Permanente focuses on the elimination of racial and ethnic health care disparities and has been in the vanguard of efforts to create innovative, scalable approaches that address the cultural and linguistic needs of patients, and thereby improve overall health care quality and outcomes. Its industry-leading training, testing, and certification process for multilingual staff who serve as health care interpreters, as well as for the physicians who speak with patients in languages other than English, helps to improve the quality of patient care while also capitalizing on the organization’s diverse workforce. (Credit: Ted Eytan/ flickr/ Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0)) For example, we are seeing a drop in the percentage of White American-born male workers in the workplace. Only 15 percent of new entrants into the workforce will be White males. Bureau of labor Statistics, “Labor Force Characteristics by Race and Ethnicity, 2016,” October 2017, https://www.bls.gov/opub/reports/race-and-ethnicity/2016/home.htm. The percentages for non-Whites and immigrants of both genders will increase (see ). In general, there are more women in positions of responsibility in both the public and private sectors and more opportunities for minorities. Some predict that the coming labor shortage will cause many companies to try to retain older workers for longer periods of time, beyond the traditional retirement age. Additionally, the belief that mentally or physically challenged individuals can play productive roles at work is increasing. Such changes bring opportunities for companies but also potential problems of adjustment if not managed intelligently. We will examine several of these issues when we discuss careers and employee development. Employed People by Race and Latino or Hispanic Ethnicity, 2016 Note: People whose ethnicity is identified as Hispanic or Latino may be of any race. Data may not sum to 100 percent because of rounding. Source: U.S. Bureau of Labor Statistics, Current Population Survey (CPS). (Attribution: Copyright Rice University, OpenStax, under CC BY NC-SA-NC-SA 4.0 license) The Challenge of Ethical Behavior Finally, the future will bring a renewed concern with maintaining high standards of ethical behavior in business transactions and in the workplace. Many executives and social scientists see unethical behavior as a cancer working on the fabric of society both in business and beyond. Many are concerned that we face a crisis of ethics in the West that is undermining our competitive strength. This crisis involves business, government, customers, and employees. Especially worrisome is unethical behavior among employees at all levels of the organization. For example, recent reports found that employees and vendors accounted for a higher percentage of thefts than did retail customers. Elaine Pofeldt, “This Crime in the Workplace is Costing US Business $50 Billion a Year,” CNBC , September 12, 2017, https://www.cnbc.com/2017/09/12/workplace-crime-costs-us-businesses-50-billion-a-year.html; and “Shoplifting, other Fraud Cost US Retailers $44 Billion in 2014: Survey,” CNBC, June 24, 2015, https://www.cnbc.com/2015/06/24/shoplifting-other-fraud-cost-us-retailers-44-billion-in-2014-survey.html. Papa John’s Founder under Fire As a manager, and leader, the words and actions you take are incredibly important. John Schnatter, founder and chairman of Papa John’s Pizza, found this out the hard way. During a media training conference call, Schnatter used derogatory comments and racial slurs. This call, although intended to be a role-playing exercise, quickly turned into a bad dream for Schnatter. In response to this action, and having admitted the fault, Schnatter was forced to resign as chairman after the local NAACP branch called for his resignation. In addition, the board of directors decided that he would be removed from all marketing, publicity, and pizza boxes, and they took the stance that “Papa John’s is not an individual. Papa John’s is a pizza company with 120,000 corporate and franchise team members around the world” (Forbes 2018). Shares of stock for Papa John’s soared after the announcement of his resignation, adding $50 million to Schnatter’s total net worth (CNN Money 2018). The values of the company prevailed through the actions of Schnatter, showcasing that despite making a mistake, the commitment to maintaining an ethical standard is still an important value to Schnatter as well as the company overall. Question 1: Do you think the actions of the board of directors were enough to uphold Papa John’s reputation? Question 2: What other actions or types of training should Papa John’s take with their employees in light of the current state of ethical defamation of the company and founder? Sources: Julie Jargon, “Papa John’s Stock Soars After Chairman’s resignation,” The Wall Street Journal , July 12, 2018, https://www.wsj.com/articles/papa-johns-directors-accept-chairmans-resignation-1531404524; Megan Friedman, “John Schnatter Will No longer Be the Face of Papa John’s,” Delish , July 16, 2018, https://www.delish.com/food-news/a22162275/papa-johns-john-schnatter-removed-marketing/; Noah Kirsch, “Papa John’s Founder Resigns, Gains $50 Million in a Day,” Forbes, July 13, 2018, https://www.forbes.com/sites/noahkirsch/2018/07/13/papa-johns-founder-john-schnatter-resigns-net-worth-rises-50-million-in-a-day/#6aaf997f7123; Jordan Valinsky, “Papa John’s Founder John Schnatter Kicked Out of His Office,” CNN Money , July 16, 2018, https://money.cnn.com/2018/07/16/news/companies/papa-johns-office/index.html In addition, we hear about illegal and unethical behavior on Wall Street—pension scandals in which disreputable executives gamble on risky business ventures with employee retirement funds, companies that expose their workers to hazardous working conditions, and blatant favoritism in hiring and promotion practices. Although such practices occur throughout the world, their presence nonetheless serves to remind us of the challenges we face. This challenge is especially difficult because standards for what constitutes ethical behavior lie in a “gray zone” where clear-cut right-or-wrong answers may not always exist. For example, if you were a sales representative for an American company abroad and your foreign competitors used bribes to get business, what would you do? In the United States such behavior is illegal, yet it is perfectly acceptable in other countries. What is ethical here? Similarly, in many countries women are systematically discriminated against in the workplace; it is felt that their place is in the home. In the United States, again, this practice is illegal. If you ran an American company in one of these countries, would you hire women in important positions? If you did, your company might be isolated in the larger business community, and you might lose business. If you did not, you might be violating what most Americans believe to be fair business practices. Effective managers must know how to deal with ethical issues in their everyday work lives; therefore, we will devote parts of this course to the role of ethics in decision-making, the exercise of power, performance appraisals and reward systems, and so forth. Describe the extent and nature of the challenges facing the workplace in the next decade. What can be done about these challenges? How do we recognize and meet the challenges facing managers in the new millennium? The fundamental challenge facing managers is how to achieve performance goals while simultaneously providing for employee welfare and satisfaction. Work may be defined as an activity that produces something of value for other people. Work serves several functions, including economic, social, status, self-esteem, and self-actualization. As managers in today’s environment, several challenges arise, including international competition, new technologies, the need for increased quality, employee motivation and commitment, a diverse workforce, and ethical behavior. These challenges must be met by managers concerned about survival and competitiveness in the future. Ethics Moral principles that govern a person's behavior or the conducting of an activity. Industrial competitiveness The ability to provide products and services more effectively and efficiently than competitors. Technology The application of scientific knowledge for practical purposes.

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