CALC 1 functions follow the spreadsheet functions of the same name in most cases. For statistical distributions we are adding functions similar to R Language functions.
Function | Description | Example | Result |
---|---|---|---|
avedev(list) | The average of the absolute deviations of values from their mean. | c1.avedev(listx) for the data set {2, 2, 3, 4, 14} | 3.600 |
average(list) | The average of the values of the list. | c1average(listx) for the data set {2, 2, 3, 4, 14} | 5.0000 |
combin(totalItems, groupSize) | The number of combinations of a subset of items. | c1combin(52, 5) | 2598960 |
correl(listx, listy) | The correlation coefficient between two data sets. | c1correl(listy, listx) | |
covar(listx, listy) | The covariance of the product of paired deviations. | c1.covar(listy, listx) | |
devsq(list) | The sum of squares of deviations from the mean. | c1.devsq(listx) for the data set {2, 2, 3, 4, 14} | 104.0000 |
geomean(list) | The geometric mean of the values of the list. | c1.geomean(listx) for the data set {2, 2, 3, 4, 14} | 3.6768 |
harmean(list) | The harmonic mean of the values of the list. | c1.harmean(listx) for the data set {2, 2, 3, 4, 14} | 3.0216 |
kurt(list) | The kurtosis of the values of the list, a measure of how peaked or flat a distribution is. | c1.kurt(listx) for the data set {2, 2, 3, 4, 14} | 4.4630 |
median(list) | The median of the values of the list. | c1.median(listx) | |
mode(list) | The mode is the value that appears most often in a set of data. | c1.mode(listx) for the data set {2, 2, 3, 4, 14} | 2.0000 |
permut(objects, elements) | The number of permutations for a given number of objects. | c1.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. | c1.poisson(5, 4.25, 0) | 0.16482 |
stdev(list) | The sample standard deviation of the values of the list. | c1.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. | c1.stdevp(listx) for the data set {2, 2, 3, 4, 14} | 4.5607 |
sum(list) | Sum of the values of the list. | c1.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. | c1.sumprod(listy, listx) | |
sumsq(list) | The sum of the squares of the values of the list. | c1.sumsq(listx) for the data set {2, 2, 3, 4, 14} | 229.0000 |
var(list) | The sample variance of the values of the list. | c1.var(listy) for the data set {2, 2, 3, 4, 14} | 26.0000 |
varp(list) | The population variance of the values of the list. | c1.varp(listy) for the data set {2, 2, 3, 4, 14} | 20.8000 |
Function | Description | Example | Result |
---|---|---|---|
beta(x, alpha, beta) | The beta distribution. | c1.betadist(0.5, 1, 2) | 0.75 |
betainv(prob, alpha, beta) | The beta distribution inverse. | c1.betainv(0.99, 1, 2) | 0.9 |
binomdist(success, trials, probSuccess, formType) | Calculates the probabilities for a binomial distribution. | c1binomdist(3, 98, 0.04, 0) | 0.2 |
chidist(x, df) | Calculates the Chi-squared distribution. | c1.chidist(5, 2); | 0.08208 |
chiinv(prob, df) | Calculates the Chi-squared distribution inverse. | c1.chiinv(0.995, 10); | 2.156 |
fdist(x, df-num, df-denom) | The f distribution. | c1.fdist(0.77, 1, 2); | 0.4727 |
finv(prob, df-num, df-denom) | The f distribution inverse. | c1.finv(0.77, 1, 2); | 0.1117 |
gammadist(x, alpha, beta, formType) | The gamma distribution. formType=true, CDF. formType=false, PDF | c1.gammadist(0.8, 1, 2, true) | 0.3297 |
normdist(x, mean, stdev, mode) | Returns the density function or the normal cumulative distribution. | c1.normdist(70, 63, 5, 1) | 0.92 |
norminv(x, mean, stdev) | Returns the inverse of the normal cumulative distribution. | c1.norminv(0.9192,63,5) | 70 |
normsdist(x) | Returns the standard normal cumulative distribution function. | c1.normsdist(1.0) | 0.84 |
normsinv(x) | Returns the inverse of the standard normal cumulative distribution. | c1.normsinv(0.8413) | 1 |
poisson(events, average, type) | Returns the probability that a specific number of events will occur using the Poisson distribution. Type 1= CDF, 0 = PMF. | c1.poisson(5, 4.25, 0) | 0.16482 |
tdist(x,df,tails) | Returns the probability from the Student’s t‑distribution. tails = 1 or 2. | c1.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. | c1.tinv(0.010, 8.0) | 3.355 |
weibull(x, alpha, beta, formType) | The weibull distribution. formType=true, CDF. formType=false, PDF | c1.weibull(1.5, 0.5, 2, false) | 0.12142 |
These are similar, but not identical to the R language functions.
The parameter, mean=0 (for example) the default value for the mean
parameter.
JavaScript does not support pnorm(2, lowerTail=false).
Distribution | CDF | INV | Rand | |
---|---|---|---|---|
Normal | dnorm(x, mean=0, sd=1) |
pnorm(q, mean=0, sd=1, lowerTail=true) |
qnorm(p, mean=0, sd=1, lowerTail=true) | rnorm(n, mean=0, sd=1) |
Student's t | dt(x, df) |
pt(q, df, ncp, lowerTail=true) Note: ncp is currently ignored. |
qt(p, df, ncp, lowerTail=true) Note: ncp is currently ignored. |
|
F | df(x, df1, df2) |
|||
Uniform | runif(n, min=0, max=1) |
Function | Description | Example | Result |
---|---|---|---|
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. | ||
correlexp(listy, listx) | 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(listy, listx) | The correlation coefficient between two data sets using ln(x). | ||
correlpwr(listy, listx) | The correlation coefficient between two data sets using ln(x) and ln(y). | ||
forecast(xval, listy, listx) | Extrapolates future values based on existing x and y values using the linear regression model. | c1.forecast(70,listy, listx) | |
intercept(listy, listx) | Calculates the point at which a line will intersect the y values by using known x values and y values. | c1.intercept(listy, listx) | |
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. | ||
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. | ||
slope(listy, listx) | The slope of the linear regression line for the lists. | c1.slope(listy, listx) |