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# CALC 1 Probability and Statistics Functions

Function Description Example Result
avedev(list) The average of the absolute deviations of values from their mean. avedev(listx) for the data set {2, 2, 3, 4, 14} 3.600
average(list) The average of the values of the list. average(listx) for the data set {2, 2, 3, 4, 14} 5.0000
bfexp(listy, listx) The B factor of the exponential regression for the lists.
bfexpnt(listy,listx) The B factor of the exponent regression for the lists.
bfinv(listy,listx) The B factor of the inverse regression for the lists.
bfln(listy, listx) The B factor of the logarithmic regression for the lists.
bfpwr(listy, listx) The B factor of the power regression for the lists.
binomdist(success, trials, probSuccess, formType) Calculates the probabilities for a binomial distribution. binomdist(3, 98, 0.04, 0) 0.2
combin(totalItems, groupSize) The number of combinations of a subset of items. combin(52, 5) 2598960
correl(listx, listy) The correlation coefficient between two data sets. correl(listy, listx)
correlexp(listx, listy) The correlation coefficient between two data sets using ln(y).
correlexpnt(listy,listx) The correlation coefficient between two data sets using ln(y).
correlinv(listy,listx) The correlation coefficient between two data sets using 1/x.
correlln(listx, listy) The correlation coefficient between two data sets using ln(x).
correlpwr(listx, listy) The correlation coefficient between two data sets using ln(x) and ln(y).
count(list) The count of the values in the list. count(listx) for the data set {2, 2, 3, 4, 14} 5.0000
covar(listx, listy) The covariance of the product of paired deviations. covar(listy, listx)
devsq(list) The sum of squares of deviations from the mean. devsq(listx) for the data set {2, 2, 3, 4, 14} 104.0000
fact(x) The factorial of a number, n!. fact(5) 120
factdouble(x) The double factorial of a number, n!!. factdouble(10) 3840
forecast(xval, listx, listy) Extrapolates future values based on existing x and y values using the linear regression model. forecast(70,listy, listx)
gamma(x) The gamma function. gamma(0.5) 1.77
gammaln(x) calculates the natural logarithm of the absolute value of the gamma function of x. gammaln(0.5) 0.57
geomean(list) The geometric mean of the values of the list. geomean(listx) for the data set {2, 2, 3, 4, 14} 3.6768
harmean(list) The harmonic mean of the values of the list. harmean(listx) for the data set {2, 2, 3, 4, 14} 3.0216
intercept(listx, listy) Calculates the point at which a line will intersect the y values by using known x values and y values. intercept(listy, listx)
kurt(list) The kurtosis of the values of the list, a measure of how peaked or flat a distribution is. kurt(listx) for the data set {2, 2, 3, 4, 14} 4.4630
maxcorrelsqcurvefit(listy,listx) Returns the curve fit with the maximum correlation squared. 0=error, 1=LIN, 2=LN, 3=EXP, 4=PWR, 5=EXPNT, 6=INV.
maxlist(list) The maximum value of the list. maxlist(listx)
median(list) The median of the values of the list. median(listx)
mfexp(listy, listx) The M factor of the exponential regression for the lists.
mfexpnt(listy,listx) The M factor of the exponent regression for the lists.
mfinv(listy,listx) The M factor of the inverse regression for the lists.
mfln(listy, listx) The M factor of the logarithmic regression for the lists.
mfpwr(listy, listx) The M factor of the power regression for the lists.
minlist(list) The minimum value of the list. minlist(listx)
mode(list) The mode is the value that appears most often in a set of data. mode(listx) for the data set {2, 2, 3, 4, 14} 2.0000
multinomial(list) The factorial of the sum divided by the product of the factorials. multinomial(listx) for the data set {2, 2, 3, 4, 14} 3.089E11
normdist(x, mean, stdev, mode) Returns the density function or the normal cumulative distribution. normdist(70, 63, 5, 1) 0.92
norminv(x, mean, stdev) Returns the inverse of the normal cumulative distribution. norminv(0.9192,63,5) 70
normsdist(x) Returns the standard normal cumulative distribution function. normsdist(1.0) 0.84
normsinv(x) Returns the inverse of the standard normal cumulative distribution. normsinv(0.8413) 1
permut(objects, elements) The number of permutations for a given number of objects. permut(52, 5) 311875200
poisson(events, average, type) Returns the probability that a specific number of events will occur using the Poisson distribution. Type 1= CDF, 0 = PMF. poisson(5, 4.25, 0) 0.16482
product(list) Multiplies all the values of the list and returns the product. product(listx) for the data set {2, 2, 3, 4, 14} 672.0000
rand() Returns a random number between 0 and 1. The example used a seed of 1234. rand() 0.22
randbetween(low,high) Returns an integer random number between and including the integers of the low and high values. randbetween(1,6)
slope(listx, listy) The slope of the linear regression line for the lists. slope(listy, listx)
srand(x) Seeds the random number generator. The seed is valid until the application quits. Otherwise srandomdev() is called to generate a seed. srand(-1234.56) 1234
stdev(list) The sample standard deviation of the values of the list. stdev(listx) for the data set {2, 2, 3, 4, 14} 5.0990
stdevp(list) The population standard deviation of the values of the list. stdevp(listx) for the data set {2, 2, 3, 4, 14} 4.5607
sum(list) Sum of the values of the list. sum(listx) for the data set {2, 2, 3, 4, 14} 25.0000
sumprod(list, list) The sum of the products of the values of the lists. sumprod(listy, listx)
sumsq(list) The sum of the squares of the values of the list. sumsq(listx) for the data set {2, 2, 3, 4, 14} 229.0000
tdist(x,df,tails) Returns the probability from the Student’s t‑distribution. tails = 1 or 2. tdist(1.86, 8.0, 1) 0.04996
tinv(prob, df) Returns the t value (a function of the probability and degrees of freedom) from the Student’s t‑distribution. tinv(0.010, 8.0) 3.355
var(list) The sample variance of the values of the list. var(listy) for the data set {2, 2, 3, 4, 14} 26.0000
varp(list) The population variance of the values of the list. varp(listy) for the data set {2, 2, 3, 4, 14} 20.8000