Using Linear Regression on ios: Numbers

[ Using Linear Regression on ios: #Numbers ]

Basic course :

https://www.khanacademy.org/math/probability/scatterplots-a1/estimating-trend-lines/e/linear-models-of-bivariate-data

 

Download sample files here :

  1. Developing my muscles.pdf
  2. LinearRegression.numbers.zip

Contents of Using Functions

1. CORREL: Calculates the correlation between two data sets based on linear
regression analysis.
2. FORECAST: Calculates the y (dependent) value that corresponds to a chosen x
(independent) value by using linear regression analysis of known value pairs.
3. INTERCEPT: Calculates the y-intercept of the best-fit line for a data set using linear regression analysis.
4. SLOPE: Finds the slope of the best-fit line for a data set based on linear regression analysis.


1. CORREL
The CORREL function calculates the correlation between two data sets based on linear
regression analysis.
CORREL(y-range, x-range)

  • y-range: A range of cells containing the dependent variable (y).
  • x-range: A range of cells containing the independent variable (x).

Examples
Given the following table:

corell

// Numbers Codes:

CORREL(D2:D7, E2:E7) returns 1.
CORREL(B2:B7, A2:A7) returns 0.977265.

2. FORECAST
The FORECAST function uses linear regression analysis of known value pairs to find the
y (dependent) value that corresponds to a chosen x (independent) value.
FORECAST(x, y-values, x-values)

  • x: The x value for which you want to find a corresponding y value.
  • y-values: A range of cells containing the known y values. Must be the same size as xvalues.
  • x-values: A range of cells containing the known x values.

Notes
You can use the SLOPE and INTERCEPT functions to find the equation used to calculate forecast values.

Examples
Given the following table:

forecast

Numbers Codes:

FORECAST(9, A3:F3, A2:F2) returns 19.

3. INTERCEPT
The INTERCEPT function calculates the y-intercept of the best-fit line for the data set
using linear regression analysis.

INTERCEPT(y-range, x-range)

  • y-range: A list of values for the dependent variable y. Must be the same size as xrange.
  • x-range: A range of cells containing values for the independent variable x. Must be the same size as y-range.

Notes
To find the slope of the best-fit line, use the SLOPE function.

Examples
Given the following table:

intercept

Numbers Code:

INTERCEPT(A2:F2, A1:F1) returns 1.
SLOPE(A2:F2, A1:F1) returns 2.
INTERCEPT(A5:F5, A4:F4) returns 2.392.

4. SLOPE
The SLOPE function calculates the slope of the best-fit line for the data set based on linear regression analysis.

SLOPE(y-range, x-range)

  • y-range: A range of cells containing the dependent variable y. Must be the same size as x-range.
  • x-range: A range of cells containing the independent variable x. Must be the same size as y-range.

Notes
To find the y-intercept of the best-fit line, use the INTERCEPT function.

Examples
Given the following table:

slope

// Numbers Codes:

SLOPE(A2:F2, A1:F1) returns 2.
INTERCEPT(A2:F2, A1:F1) returns 1.
SLOPE(A5:F5, A4:F4) returns 0.402.

Total Examples

TotalLinearRegression

// Numbers Codes:
// y = ax + b

SLOPE(B3:F3,B2:F2)      // a
INTERCEPT(B3:F3,B2:F2)  // b

// Linear Regression line
FORECAST(B2,B3:F3,B2:F2)
FORECAST(C2,B3:F3,B2:F2)
FORECAST(D2,B3:F3,B2:F2)
FORECAST(E2,B3:F3,B2:F2)
FORECAST(F2,B3:F3,B2:F2)

Application (To Expect the Size of muscle in the future)

(Using Linear Function:   y = ax + b  )

LinearRegression_ios

// Numbers Codes:

// 1. Arm size:
SLOPE(E6:E10,A6:A10)
INTERCEPT(E6:E10,A6:A10)
FORECAST(A10,E6:E10,A6:A10)

// 2. Forearm size:
SLOPE(I6:I10,A6:A10)
INTERCEPT(I6:I10,A6:A10)
FORECAST(A10,I6:I10,A6:A10)

END

#numbers #linearregression #stat #correl #forecast #intercept #slope

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Author: iotmaker

I am interested in IoT, robot, figures & leadership. Also, I have spent almost every day of the past 15 years making robots or electronic inventions or computer programs.

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