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Statistics Registers

Implement a set of statistics registers in the style of a pocket calculator.

The available routines are:

def  Clear():                        clear the stats registers
def  Show():                         print the contents of the stats registers
def  Add(x, y):                      add an X,Y pair
def  Subtract(x, y):                 remove an X,Y pair
def  AddWeighted(x, y, z):           add an X,Y pair with weight Z
def  SubtractWeighted(x, y, z):      remove an X,Y pair with weight Z
def  Mean():                         arithmetic mean of X & Y
def  StdDev():                       standard deviation on X & Y
def  StdErr():                       standard error on X & Y
def  LinearRegression():             linear regression
def  LinearRegressionVariance():     est. errors of slope & intercept
def  LinearRegressionCorrelation():  the regression coefficient
def  CorrelationCoefficient():       relation of errors in slope & intercept
see:http://stattrek.com/AP-Statistics-1/Regression.aspx?Tutorial=Stat

pocket calculator Statistical Registers, Pete Jemian, 2003-Apr-18

Source code documentation

class jldesmear.api.StatsReg.StatsRegClass[source]

pocket calculator Statistical Registers class

Add(x, y)[source]

add an X,Y pair to the statistics registers

Parameters:
  • x (float) – value to accumulate
  • y (float) – value to accumulate
AddWeighted(x, y, z)[source]

add a weighted X,Y+/Z trio to the statistics registers

Parameters:
  • x (float) – value to accumulate
  • y (float) – value to accumulate
  • z (float) – variance (weight = 1/z^2) of y
Clear()[source]

clear the statistics registers:

CorrelationCoefficient()[source]

relation of errors in slope and intercept

Returns:correlation coefficient
Return type:float
LinearEval(x)[source]

Evaluate a linear fit at the given value:

Parameters:x (float) – independent value, x
Returns:y
Return type:float
LinearRegression()[source]

For (x,y) data pairs added to the registers, fit and find (a,b) that satisfy:

Returns:(a, b) for fit of y=a+b*x
Return type:(float, float)
LinearRegressionCorrelation()[source]

the regression coefficient

Returns:(corr_a, corr_b) – is this correct?
Return type:(float, float)
See:http://stattrek.com/AP-Statistics-1/Correlation.aspx?Tutorial=Stat Look at “Product-moment correlation coefficient”
LinearRegressionVariance()[source]

est. errors of slope & intercept

Returns:(var_a, var_b) – is this correct?
Return type:(float, float)
Mean()[source]

arithmetic mean of X & Y

Returns:mean X and Y values
Return type:float
Show()[source]

contents of the statistics registers

StdDev()[source]

standard deviation on X & Y

Returns:standard deviation of mean X and Y values
Return type:(float, float)
StdErr()[source]

standard error on X & Y

Returns:standard error of mean X and Y values
Return type:(float, float)
Subtract(x, y)[source]

remove an X,Y pair from the statistics registers

Parameters:
  • x (float) – value to remove
  • y (float) – value to remove
SubtractWeighted(x, y, z)[source]

remove a weighted X,Y+/-Z trio from the statistics registers

Parameters:
  • x (float) – value to remove
  • y (float) – value to remove
  • z (float) – variance (weight = 1/z^2) of y
determinant()[source]

compute and return the determinant of the square matrices:

|  sum_w   sum_x      |          |  sum_w   sum_y      |
|  sum_x   sum_(x^2)  |          |  sum_y   sum_(y^2)  |
Returns:determinants of x and y summation matrices
Return type:(float, float)