The standard Gaussian distribution may be a Gaussian distribution with a mean of zero and variance of 1. The quality Gaussian distribution is centered at zero and therefore the degree to which a given measurement deviates from the mean is given by the quality deviation. For the quality Gaussian distribution, 68% of the observations lie within 1 variance of the mean; 95% lie within two variance of the mean; and 99.9% lie within 3 standard deviations of the mean. To the present point, we’ve been using “X” to denote the variable of interest (e.g., X=BMI, X=height, X=weight). However, when employing a standard Gaussian distribution, we’ll use “Z” to ask a variable within the context of a typical Gaussian distribution. After standardization, the BMI=30 discussed on the previous page is shown below lying 0.16667 units above the mean of 0 on the quality Gaussian distribution on the proper.

Since the world under the quality curve = 1, we will begin to more precisely define the possibilities of specific observation. For any given Z-score we will compute the world under the curve to the left of that Z-score. The table within the frame below shows the possibilities for the quality Gaussian distribution. Examine the table and note that a “Z” score of 0.0 lists a probability of 0.50 or 50%, and a “Z” score of 1, meaning one variance above the mean, lists a probability of 0.8413 or 84%. That’s because one variance above and below the mean encompasses about 68% of the world, so one variance above the mean represents half that of 34%. So, the five hundred below the mean plus the 34% above the mean gives us 84%.

STANDARD NORMAL DISTRIBUTION: Table Values Represent AREA to the LEFT of the Z score.

Z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 -3.9 .00005 .00005 .00004 .00004 .00004 .00004 .00004 .00004 .00003 .00003 -3.8 .00007 .00007 .00007 .00006 .00006 .00006 .00006 .00005 .00005 .00005 -3.7 .00011 .00010 .00010 .00010 .00009 .00009 .00008 .00008 .00008 .00008 -3.6 .00016 .00015 .00015 .00014 .00014 .00013 .00013 .00012 .00012 .00011 -3.5 .00023 .00022 .00022 .00021 .00020 .00019 .00019 .00018 .00017 .00017 -3.4 .00034 .00032 .00031 .00030 .00029 .00028 .00027 .00026 .00025 .00024 -3.3 .00048 .00047 .00045 .00043 .00042 .00040 .00039 .00038 .00036 .00035 -3.2 .00069 .00066 .00064 .00062 .00060 .00058 .00056 .00054 .00052 .00050 -3.1 .00097 .00094 .00090 .00087 .00084 .00082 .00079 .00076 .00074 .00071 -3.0 .00135 .00131 .00126 .00122 .00118 .00114 .00111 .00107 .00104 .00100 -2.9 .00187 .00181 .00175 .00169 .00164 .00159 .00154 .00149 .00144 .00139 -2.8 .00256 .00248 .00240 .00233 .00226 .00219 .00212 .00205 .00199 .00193 -2.7 .00347 .00336 .00326 .00317 .00307 .00298 .00289 .00280 .00272 .00264 -2.6 .00466 .00453 .00440 .00427 .00415 .00402 .00391 .00379 .00368 .00357 -2.5 .00621 .00604 .00587 .00570 .00554 .00539 .00523 .00508 .00494 .00480 -2.4 .00820 .00798 .00776 .00755 .00734 .00714 .00695 .00676 .00657 .00639 -2.3 .01072 .01044 .01017 .00990 .00964 .00939 .00914 .00889 .00866 .00842 -2.2 .01390 .01355 .01321 .01287 .01255 .01222 .01191 .01160 .01130 .01101 -2.1 .01786 .01743 .01700 .01659 .01618 .01578 .01539 .01500 .01463 .01426 -2.0 .02275 .02222 .02169 .02118 .02068 .02018 .01970 .01923 .01876 .01831 -1.9 .02872 .02807 .02743 .02680 .02619 .02559 .02500 .02442 .02385 .02330 -1.8 .03593 .03515 .03438 .03362 .03288 .03216 .03144 .03074 .03005 .02938 -1.7 .04457 .04363 .04272 .04182 .04093 .04006 .03920 .03836 .03754 .03673 -1.6 .05480 .05370 .05262 .05155 .05050 .04947 .04846 .04746 .04648 .04551 -1.5 .06681 .06552 .06426 .06301 .06178 .06057 .05938 .05821 .05705 .05592 -1.4 .08076 .07927 .07780 .07636 .07493 .07353 .07215 .07078 .06944 .06811 -1.3 .09680 .09510 .09342 .09176 .09012 .08851 .08691 .08534 .08379 .08226 -1.2 .11507 .11314 .11123 .10935 .10749 .10565 .10383 .10204 .10027 .09853 -1.1 .13567 .13350 .13136 .12924 .12714 .12507 .12302 .12100 .11900 .11702 -1.0 .15866 .15625 .15386 .15151 .14917 .14686 .14457 .14231 .14007 .13786 -0.9 .18406 .18141 .17879 .17619 .17361 .17106 .16853 .16602 .16354 .16109 -0.8 .21186 .20897 .20611 .20327 .20045 .19766 .19489 .19215 .18943 .18673 -0.7 .24196 .23885 .23576 .23270 .22965 .22663 .22363 .22065 .21770 .21476 -0.6 .27425 .27093 .26763 .26435 .26109 .25785 .25463 .25143 .24825 .24510 -0.5 .30854 .30503 .30153 .29806 .29460 .29116 .28774 .28434 .28096 .27760 -0.4 .34458 .34090 .33724 .33360 .32997 .32636 .32276 .31918 .31561 .31207 -0.3 .38209 .37828 .37448 .37070 .36693 .36317 .35942 .35569 .35197 .34827 -0.2 .42074 .41683 .41294 .40905 .40517 .40129 .39743 .39358 .38974 .38591 -0.1 .46017 .45620 .45224 .44828 .44433 .44038 .43644 .43251 .42858 .42465 -0.0 .50000 .49601 .49202 .48803 .48405 .48006 .47608 .47210 .46812 .46414 STANDARD NORMAL DISTRIBUTION: Table Values Represent AREA to the LEFT of the Z score. Z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 0.0 .50000 .50399 .50798 .51197 .51595 .51994 .52392 .52790 .53188 .53586 0.1 .53983 .54380 .54776 .55172 .55567 .55962 .56356 .56749 .57142 .57535 0.2 .57926 .58317 .58706 .59095 .59483 .59871 .60257 .60642 .61026 .61409 0.3 .61791 .62172 .62552 .62930 .63307 .63683 .64058 .64431 .64803 .65173 0.4 .65542 .65910 .66276 .66640 .67003 .67364 .67724 .68082 .68439 .68793 0.5 .69146 .69497 .69847 .70194 .70540 .70884 .71226 .71566 .71904 .72240 0.6 .72575 .72907 .73237 .73565 .73891 .74215 .74537 .74857 .75175 .75490 0.7 .75804 .76115 .76424 .76730 .77035 .77337 .77637 .77935 .78230 .78524 0.8 .78814 .79103 .79389 .79673 .79955 .80234 .80511 .80785 .81057 .81327 0.9 .81594 .81859 .82121 .82381 .82639 .82894 .83147 .83398 .83646 .83891 1.0 .84134 .84375 .84614 .84849 .85083 .85314 .85543 .85769 .85993 .86214 1.1 .86433 .86650 .86864 .87076 .87286 .87493 .87698 .87900 .88100 .88298 1.2 .88493 .88686 .88877 .89065 .89251 .89435 .89617 .89796 .89973 .90147 1.3 .90320 .90490 .90658 .90824 .90988 .91149 .91309 .91466 .91621 .91774 1.4 .91924 .92073 .92220 .92364 .92507 .92647 .92785 .92922 .93056 .93189 1.5 .93319 .93448 .93574 .93699 .93822 .93943 .94062 .94179 .94295 .94408 1.6 .94520 .94630 .94738 .94845 .94950 .95053 .95154 .95254 .95352 .95449 1.7 .95543 .95637 .95728 .95818 .95907 .95994 .96080 .96164 .96246 .96327 1.8 .96407 .96485 .96562 .96638 .96712 .96784 .96856 .96926 .96995 .97062 1.9 .97128 .97193 .97257 .97320 .97381 .97441 .97500 .97558 .97615 .97670 2.0 .97725 .97778 .97831 .97882 .97932 .97982 .98030 .98077 .98124 .98169 2.1 .98214 .98257 .98300 .98341 .98382 .98422 .98461 .98500 .98537 .98574 2.2 .98610 .98645 .98679 .98713 .98745 .98778 .98809 .98840 .98870 .98899 2.3 .98928 .98956 .98983 .99010 .99036 .99061 .99086 .99111 .99134 .99158 2.4 .99180 .99202 .99224 .99245 .99266 .99286 .99305 .99324 .99343 .99361 2.5 .99379 .99396 .99413 .99430 .99446 .99461 .99477 .99492 .99506 .99520 2.6 .99534 .99547 .99560 .99573 .99585 .99598 .99609 .99621 .99632 .99643 2.7 .99653 .99664 .99674 .99683 .99693 .99702 .99711 .99720 .99728 .99736 2.8 .99744 .99752 .99760 .99767 .99774 .99781 .99788 .99795 .99801 .99807 2.9 .99813 .99819 .99825 .99831 .99836 .99841 .99846 .99851 .99856 .99861 3.0 .99865 .99869 .99874 .99878 .99882 .99886 .99889 .99893 .99896 .99900 3.1 .99903 .99906 .99910 .99913 .99916 .99918 .99921 .99924 .99926 .99929 3.2 .99931 .99934 .99936 .99938 .99940 .99942 .99944 .99946 .99948 .99950 3.3 .99952 .99953 .99955 .99957 .99958 .99960 .99961 .99962 .99964 .99965 3.4 .99966 .99968 .99969 .99970 .99971 .99972 .99973 .99974 .99975 .99976 3.5 .99977 .99978 .99978 .99979 .99980 .99981 .99981 .99982 .99983 .99983 3.6 .99984 .99985 .99985 .99986 .99986 .99987 .99987 .99988 .99988 .99989 3.7 .99989 .99990 .99990 .99990 .99991 .99991 .99992 .99992 .99992 .99992 3.8 .99993 .99993 .99993 .99994 .99994 .99994 .99994 .99995 .99995 .99995 3.9 .99995 .99995 .99996 .99996 .99996 .99996 .99996 .99996 .99997 .99997

Probabilities of the quality Gaussian distribution Z

This table is organized to supply the world under the curve to the left of or less of a specified value or “Z value”. during this case, because the mean is zero and therefore the variance is 1, the Z value is that the number of ordinary deviation units far away from the mean, and therefore the area is that the probability of observing a worth but that specific Z value. Note also that the table shows probabilities to 2 decimal places of Z. The units place and therefore the first decimal place are shown within the left column, and therefore the second decimal place is displayed across the highest row.

But let’s revisit to the question about the probability that the BMI is a smaller amount than 30, i.e., P(X)

Distribution of BMI and Standard Normal Distribution

The area under each curve is one but the scaling of the X axis is different. Note, however, that the areas to the left of the dashed line are an equivalent. The BMI distribution ranges from 11 to 47, while the standardized Gaussian distribution, Z, ranges from -3 to three. We would like to compute P(X < 30). to try to to this we will determine the Z value that corresponds to X = 30 then use the quality Gaussian distribution table above to seek out the probability or area under the curve. The subsequent formula converts an X value into a Z score, also called a uniform score:

Where μ is that the mean and σ is that the variance of the variable X.

In order to compute P(X < 30) we convert the X=30 to its corresponding Z score (this is named standardizing): Thus, P(X < 30) = P(Z < 0.17). we will then search the corresponding probability for this Z score from the quality Gaussian distribution table, which shows that P(X < 30) = P(Z < 0.17) = 0.5675. Thus, the probability that a male aged 60 has BMI but 30 are 56.75%.

Another Example

Using an equivalent distribution for BMI, what’s the probability that a male aged 60 has BMI exceeding 35? In other words, what’s P(X > 35)? Again we standardize:

Equation image indicator

We now attend the quality Gaussian distribution table to seem up P(Z>1) and for Z=1.00 we discover that P(Z

Before, P(Z>1)=1-0.8413=0.1587. Interpretation: Almost 16% of men aged 60 have BMI over 35.

Normal Probability Calculator

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Z-Scores with R

As an alternative to looking up normal probabilities in the table or using Excel, we can use R to compute probabilities. For example,

> pnorm(0)

[1] 0.5

A Z-score of 0 (the mean of any distribution) has 50% of the area to the left. What is the probability that a 60 year old man in the population above has a BMI less than 29 (the mean)? The Z-score would be 0, and pnorm(0)=0.5 or 50%.

What is the probability that a 60 year old man will have a BMI less than 30? The Z-score was 0.16667.

> pnorm(0.16667)

[1] 0.5661851

So, the probabilty is 56.6%.

What is the probability that a 60 year old man will have a BMI greater than 35?

35-29=6, which is one standard deviation above the mean. So we can compute the area to the left

> pnorm(1)

[1] 0.8413447

And then subtract the result from 1.0.

1-0.8413447= 0.1586553

So the probability of a 60 year ld man having a BMI greater than 35 is 15.8%.

Or, we can use R to compute the entire thing in a single step as follows:

> 1-pnorm(1)

[1] 0.1586553