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Solution - Cumulative probability in the standard normal distribution

Cumulative probability 79.701%
79.701%

Step-by-step explanation

1. Find the cumulative probability of the z-scores values up to 1.327

Use the positive z-table to find the value corresponding to 1.327. This value is the cumulative probability of the area to the left of 1.327.

Z0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.00.50.503990.507980.511970.515950.519940.523920.52790.531880.53586
0.10.539830.54380.547760.551720.555670.559620.563560.567490.571420.57535
0.20.579260.583170.587060.590950.594830.598710.602570.606420.610260.61409
0.30.617910.621720.625520.62930.633070.636830.640580.644310.648030.65173
0.40.655420.65910.662760.66640.670030.673640.677240.680820.684390.68793
0.50.691460.694970.698470.701940.70540.708840.712260.715660.719040.7224
0.60.725750.729070.732370.735650.738910.742150.745370.748570.751750.7549
0.70.758040.761150.764240.76730.770350.773370.776370.779350.78230.78524
0.80.788140.791030.793890.796730.799550.802340.805110.807850.810570.81327
0.90.815940.818590.821210.823810.826390.828940.831470.833980.836460.83891
1.00.841340.843750.846140.848490.850830.853140.855430.857690.859930.86214
1.10.864330.86650.868640.870760.872860.874930.876980.8790.8810.88298
1.20.884930.886860.888770.890650.892510.894350.896170.897960.899730.90147
1.30.90320.90490.906580.908240.909880.911490.913080.914660.916210.91774
1.40.919240.920730.92220.923640.925070.926470.927850.929220.930560.93189
1.50.933190.934480.935740.936990.938220.939430.940620.941790.942950.94408
1.60.94520.94630.947380.948450.94950.950530.951540.952540.953520.95449
1.70.955430.956370.957280.958180.959070.959940.96080.961640.962460.96327
1.80.964070.964850.965620.966380.967120.967840.968560.969260.969950.97062
1.90.971280.971930.972570.97320.973810.974410.9750.975580.976150.9767
2.00.977250.977780.978310.978820.979320.979820.98030.980770.981240.98169
2.10.982140.982570.9830.983410.983820.984220.984610.9850.985370.98574
2.20.98610.986450.986790.987130.987450.987780.988090.98840.98870.98899
2.30.989280.989560.989830.99010.990360.990610.990860.991110.991340.99158
2.40.99180.992020.992240.992450.992660.992860.993050.993240.993430.99361
2.50.993790.993960.994130.99430.994460.994610.994770.994920.995060.9952
2.60.995340.995470.99560.995730.995850.995980.996090.996210.996320.99643
2.70.996530.996640.996740.996830.996930.997020.997110.99720.997280.99736
2.80.997440.997520.99760.997670.997740.997810.997880.997950.998010.99807
2.90.998130.998190.998250.998310.998360.998410.998460.998510.998560.99861
3.00.998650.998690.998740.998780.998820.998860.998890.998930.998960.999
3.10.999030.999060.99910.999130.999160.999180.999210.999240.999260.99929
3.20.999310.999340.999360.999380.99940.999420.999440.999460.999480.9995
3.30.999520.999530.999550.999570.999580.99960.999610.999620.999640.99965
3.40.999660.999680.999690.99970.999710.999720.999730.999740.999750.99976
3.50.999770.999780.999780.999790.99980.999810.999810.999820.999830.99983
3.60.999840.999850.999850.999860.999860.999870.999870.999880.999880.99989
3.70.999890.99990.99990.99990.999910.999910.999920.999920.999920.99992
3.80.999930.999930.999930.999940.999940.999940.999940.999950.999950.99995
3.90.999950.999950.999960.999960.999960.999960.999960.999960.999970.99997

A z-score of 1.327 corresponds to an area of 0.90824
p(z<1.327)=0.90824
The cumulative probability that z<1.327 is 90.824%

2. Find the cumulative probability of the z-scores values up to 1.22

Use the negative z-table to find the value corresponding to -1.22. This value is the cumulative probability of the area to the left of -1.22.

Z0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
-3.90.000050.000050.000040.000040.000040.000040.000040.000040.000030.00003
-3.80.000070.000070.000070.000060.000060.000060.000060.000050.000050.00005
-3.70.000110.00010.00010.00010.000090.000090.000080.000080.000080.00008
-3.60.000160.000150.000150.000140.000140.000130.000130.000120.000120.00011
-3.50.000230.000220.000220.000210.00020.000190.000190.000180.000170.00017
-3.40.000340.000320.000310.00030.000290.000280.000270.000260.000250.00024
-3.30.000480.000470.000450.000430.000420.00040.000390.000380.000360.00035
-3.20.000690.000660.000640.000620.00060.000580.000560.000540.000520.0005
-3.10.000970.000940.00090.000870.000840.000820.000790.000760.000740.00071
-3.00.001350.001310.001260.001220.001180.001140.001110.001070.001040.001
-2.90.001870.001810.001750.001690.001640.001590.001540.001490.001440.00139
-2.80.002560.002480.00240.002330.002260.002190.002120.002050.001990.00193
-2.70.003470.003360.003260.003170.003070.002980.002890.00280.002720.00264
-2.60.004660.004530.00440.004270.004150.004020.003910.003790.003680.00357
-2.50.006210.006040.005870.00570.005540.005390.005230.005080.004940.0048
-2.40.00820.007980.007760.007550.007340.007140.006950.006760.006570.00639
-2.30.010720.010440.010170.00990.009640.009390.009140.008890.008660.00842
-2.20.01390.013550.013210.012870.012550.012220.011910.01160.01130.01101
-2.10.017860.017430.0170.016590.016180.015780.015390.0150.014630.01426
-2.00.022750.022220.021690.021180.020680.020180.01970.019230.018760.01831
-1.90.028720.028070.027430.02680.026190.025590.0250.024420.023850.0233
-1.80.035930.035150.034380.033620.032880.032160.031440.030740.030050.02938
-1.70.044570.043630.042720.041820.040930.040060.03920.038360.037540.03673
-1.60.05480.05370.052620.051550.05050.049470.048460.047460.046480.04551
-1.50.066810.065520.064260.063010.061780.060570.059380.058210.057050.05592
-1.40.080760.079270.07780.076360.074930.073530.072150.070780.069440.06811
-1.30.09680.09510.093420.091760.090120.088510.086920.085340.083790.08226
-1.20.115070.113140.111230.109350.107490.105650.103830.102040.100270.09853
-1.10.135670.13350.131360.129240.127140.125070.123020.1210.1190.11702
-1.00.158660.156250.153860.151510.149170.146860.144570.142310.140070.13786
-0.90.184060.181410.178790.176190.173610.171060.168530.166020.163540.16109
-0.80.211860.208970.206110.203270.200450.197660.194890.192150.189430.18673
-0.70.241960.238850.235760.23270.229650.226630.223630.220650.21770.21476
-0.60.274250.270930.267630.264350.261090.257850.254630.251430.248250.2451
-0.50.308540.305030.301530.298060.29460.291160.287740.284340.280960.2776
-0.40.344580.34090.337240.33360.329970.326360.322760.319180.315610.31207
-0.30.382090.378280.374480.37070.366930.363170.359420.355690.351970.34827
-0.20.420740.416830.412940.409050.405170.401290.397430.393580.389740.38591
-0.10.460170.45620.452240.448280.444330.440380.436440.432510.428580.42465
0.00.50.496010.492020.488030.484050.480060.476080.47210.468120.46414

A z-score of 1.22 corresponds to an area of 0.11123
p(z<1.22)=0.11123
The cumulative probability that z<1.22 is 11.123%

3. Calculate the cumulative probability between 1.327 and -1.22

To find the cumulative probability of the area between the two z-scores, subtract the smaller cumulative probability (everything to the left of -1.22) from the larger cumulative probability (everything to the left of 1.327):

0.90824-0.11123=0.79701
p(-1.22<z<1.327)=0.79701
The cumulative probability that -1.22<z<1.327 is 79.701%

Why learn this

The normal distribution is important because we see it often in nature. Suppose we gather many unrelated measures, like human heights, blood pressure readings, or IQ scores. They will follow the normal distribution.

We see many normally distributed variables in psychology. For example, reading ability, introversion or job satisfaction. In investing, the normal distribution shows asset class returns. Although these distributions are only roughly normal, they are pretty close, and we can treat them as normal.

The normal distribution is easy to work with. Many statistical tests rely on it. Moreover, these tests work well even when the distribution is only approximately normal. For example, if a set's mean and standard deviation are known, and the set follows the normal distribution, we can easily convert between percentiles and raw scores.

Any normal distribution can be standardized to a standard normal distribution. That way, we can compare two or more separate data sets. Using standard normal distribution, we can estimate probabilities of events involving normal distribution. This way, we can estimate how tall a person is likely to grow, for instance.