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Solution - Cumulative probability in the standard normal distribution

Cumulative probability 60.513%
60.513%

Step-by-step explanation

1. Find the cumulative probability of the z-scores values up to 0.351

Use the positive z-table to find the value corresponding to 0.351. This value is the cumulative probability of the area to the left of 0.351.

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 0.351 corresponds to an area of 0.63683
p(z<0.351)=0.63683
The cumulative probability that z<0.351 is 63.683%

2. Find the cumulative probability of the z-scores values greater than 0.351

To find the cumulative probability of the values greater than 0.351, we need to subtract the cumulative probability of the values less than 0.351 from the total probability under the curve, which is equal to 1:

10.63683=0.36317
p(0.702>z>0.351)=0.36317
The cumulative probability of z>0.351 is 36.317%

3. Find the cumulative probability of the z-scores values up to 0.702

Use the negative z-table to find the value corresponding to -0.702. This value is the cumulative probability of the area to the left of -0.702.

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 0.702 corresponds to an area of 0.24196
p(z<0.702)=0.24196
The cumulative probability that z<0.702 is 24.196%

4. Calculate the cumulative probability of the values greater than 0.351 and less than -0.702

Add the cumulative probability of the area to the right of the higher z-score (everything to the right of 0.351) to the cumulative probability of the area to the left of the lower z-score (everything to the left of -0.702):

0.36317+0.24196=0.60513
p(-0.702>z>0.351)=0.60513
The cumulative probability that-0.702>z>0.351is60.513%



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.