CASIO

In the SD Mode and REG Mode, the key operates as the key.

Entering the REG Mode displays screens like the ones shown below.

Press the number key (, , or ) that corresponds to the type of regression you want to use.

Always start data input with (CLR)(Scl) to clear statistical memory.

Input data using the key sequence shown below.

The values produced by a regression calculation depend on the values input, and results can be recalled using the key operations shown in the table below.

The following table shows the key operations you should use to recall results in the case of quadratic regression.

The values in the above tables can be used inside of expressions the same way you use variables.

Linear Regression

The regression formula for linear regression is: y = A + Bx.

Example: Atmospheric Pressure vs. Temperature

Perform linear regression to determine the regression formula terms and correlation coefficient for the data below.

Next, use the regression formula to estimate atmospheric pressure at -5°C and temperature at 1000 hPa. Finally, calculate the coefficient of determination (r2) and sample covariance (^{∑xy - n∙x-∙y-}_{n - 1}).

• In the REG Mode:
  (Lin)
  (CLR)(Scl)(Stat clear)101003

• 

Each time you press to register your input, the number of data input up to that point is indicated on the display (n value).

151005201010251011301014

Regression Coefficient A = 997.4

• (S-VAR)(A)
• 997.4

Regression Coefficient B = 0.56

• (S-VAR)(B)
• 0.56

Correlation Coefficient r = 0.982607368

• (S-VAR)(r)
• 0.982607368

Atmospheric Pressure at 5°C = 994.6

• 5(S-VAR)(yˆ)
• 994.6

Temperature at 1000 hPa = 4.642857143

• 1000(S-VAR)(xˆ)
• 4.642857143

Coefficient of Determination = 0.965517241

• (S-VAR)(r)
• 0.965517241

Sample Covariance = 35

• (S-SUM)(Σxy)
  (S-SUM)(n)
  (S-VAR)(x-)
  (S-VAR)(y-)
  (S-SUM)(n)1
• 35.

Logarithmic, Exponential, Power, and Inverse Regression

Use the same key operations as linear regression to recall results for these types of regression.

The following shows the regression formulas for each type of regression.

Quadratic Regression

The regression formula for quadratic regression is: y = A + Bx + Cx2.

Example:

Perform quadratic regression to determine the regression formula terms for the data below.

Next, use the regression formula to estimate the values for yˆ (estimated value of y) for xi = 16 and xˆ (estimated value of x) for yi = 20.

In the REG Mode:
(Quad)
(CLR)(Scl)(Stat clear)

29165023574380103464118480

Regression Coefficient A = -35.59856934

• (S-VAR)(A)
• -35.59856934

Regression Coefficient B = 1.495939413

• (S-VAR)(B)
• 1.495939413

Regression Coefficient C = -6.71629667 × 10-3

• (S-VAR)(C)
• -6.71629667×10-3

yˆ when xi is 16 = -13.38291067

• 16(S-VAR)(yˆ)
• -13.38291067

xˆ1 when yi is 20 = 47.14556728

• 20(S-VAR)(xˆ1)
• 47.14556728

xˆ2 when yi is 20 = 175.5872105

• 20(S-VAR)(xˆ2)
• 175.5872105

Data Input Precautions

inputs the same data twice.

You can also input multiple entries of the same data using (;). To input the data "20 and 30" five times, for example, press 20 30 (;) 5.

The above results can be obtained in any order, and not necessarily that shown above.

Precautions when editing data input for standard deviation also apply for regression calculations.

Do not use variables A through F, X, or Y to store data when performing statistical calculations. These variables are used for statistical calculation temporary memory, so any data you may have assigned to them may be replaced by other values during statistical calculations.

Entering the REG Mode and selecting a regression type (Lin, Log, Exp, Pwr, Inv, Quad) clear variables A through F, X, and Y. Changing from one regression type to another inside the REG Mode also clears these variables.