Statistics on the HP 15C
The HP 15C performs one and two variable statistics. In statistical calculation, data is entered into Y and X registers. When the key [ ∑+ ] is pressed the following variables are updated:
R2 = n = number of data points
R3 = ∑x = sum of x data
R4 = ∑x^2 = sum of x-squared data
R5 = ∑y = sum of y data
R6 = ∑y^2 = sum of y-squared data
R7 = ∑xy = sum of the products of x-data and y-data
You can recall these values.
Additional statistical functions are available:
Arithmetic Mean: [ g ] [ 0 ]. The arithmetic mean of x-data is displayed. Press [x<>y] to display the arithmetic mean of y-data is displayed.
Standard Deviation: [ g ] [ . ]. The standard deviation of x-data is displayed. Press [x<>y] to display the standard deviation of y-data is displayed.
Clearing the Statistics Registers
[ f ] [GSB] (CLEAR ∑) clears memory registers R2, R3, R4, R5, R6, and R7 to 0.
Linear Regression
The HP 15C includes linear regression functions to fit data to the line:
y = a x + b
where a = slope and b = y-intercept with r = correlation coefficient. If r is near 1 or -1, the data has a good fit to the line. If r is near 0, the data does not have a good fit to a line.
To find a, b, and r:
b: [ f ] [∑+] (L.R.)
a: [ f ] [∑+] (L.R.) [x<>y]
r: a valid number [ f ] [ . ] (y-hat, r) [x<>y]
Entering Data
One Variable Data:
1. Enter x
2. Press [∑+]
Two Variable Data:
1. Enter y
2. Press [ENTER]
3. Enter x
4. Press [∑+]
The key sequence [ g ] [∑+] (∑-) can be used to remove data.
A Example of Linear Regression
Find a linear fit to the data:x y
1 3
2 6
4 11
8 15
16 36
Caution: In two variable data, enter the y data first.
Key press:
[ f ] [GSB] (CLEAR ∑)
3 [ENTER] 1 [∑+]
6 [ENTER] 2 [∑+]
11 [ENTER] 4 [∑+]
15 [ENTER] 8 [∑+]
36 [ENTER] 16 [∑+]
First the slope and y-intercept:
[ f ] [∑+] (L.R.) (** 1.0833 is displayed)
[x<>y] (** 2.1156 is displayed)
1 [ f ] [ . ] (y-hat, r) [x<>y] (** 0.9905 is displayed)
Results: The linear fit is approximately is y ≈ 2.1156 + 1.0833x with r ≈ 0.9905 (a very good fit!)
In the next program, we will use the statistical features to fit data to a logarithmic fit.
Logarithmic Regression
This program fits the data to the equation:
y = b + a ln x
Note that this is similar to the linear equation y = b + a x, except that we are working with "ln x" instead of "x".
This program adjusts the x-data to ln(x-data).
Labels used:
Label A: Initialize data; clear registers R2 - R7
Label B: Adjusts the data and enters the data
Label C: Calculates a, b, and r.
Program ListingKey Codes Key
001 42 21 22 LBL A * Initialization
002 42 32 CLR ∑
003 43 32 RTN
004 42 21 12 LBL B * Data Entry
005 43 12 LN
006 49 ∑+
007 43 32 RTN
008 42 21 13 LBL C * Calculate b, a, r
009 42 49 L.R.
010 31 R/S * Display b
011 34 x<>y
012 31 R/S * Display a
013 1 1
014 42 48 y-hat, r
015 34 x<>y * Display r
016 43 32 RTN
Example
Data is presented regarding the growth of a human male:X = Age (in years) Y = Height (in feet)
1 0.88
2 1.44
3 2.06
4 2.18
5 2.45
10 5.02
15 5.76
20 6.09
Use the program to find a logarithmic fit.
Key Presses:
[ f ] [ √ ] (A)
0.88 [ENTER] 1 [ f ] [e^x] (B)
1.44 [ENTER] 2 [ f ] [e^x] (B)
2.06 [ENTER] 3 [ f ] [e^x] (B)
2.18 [ENTER] 4 [ f ] [e^x] (B)
2.45 [ENTER] 5 [ f ] [e^x] (B)
5.02 [ENTER] 10 [ f ] [e^x] (B)
5.76 [ENTER] 15 [ f ] [e^x] (B)
6.09 [ENTER] 20 [ f ] [e^x] (B)
[ f ] [10^x] (C) (* Display: 0.1363)
[R/S] (* Display: 1.9376)
[R/S] (* Display: 0.9656)
Results:
y ≈ 0.1363 + 1.9376 ln x with r ≈ 0.9656 (very good fit!)
Regressions
The following table shows various fits that can be used using L.R. and y-hat,r functions. Use the necessary adjustments to get the correct data.
For example, for logarithmic fit, take the natural logarithm of x before pressing [∑+]. For exponential fit, take the natural logarithm of y before pressing [∑+]; when pressing [L.R.], take the exponential of both a and b to get the true slope and y-intercept.Fit Equation x y a b
Linear y = ax+b x y a b
Logarithmic y = b + a ln x y a b
Power y = b * x^a ln x ln y a b
Exponential y = b * a^x x ln y e^a e^b
Inverse y = b + a/x 1/x y a b
Logistic y = L/(1+ae^(bx)) x ln(L/y - 1) e^a b
L > 2y_max
Sine y = b + a sin(px) sin(px) y a b
p = (x_max - x_min)/(2π)
Hope you find this table useful.
Until then, happy programming,
Eddie
This tutorial is property of Edward Shore. © 2011
