I need to fit a logistic curve to data points on a scatter plot to determine
if regression (r^2) values are closer to 1, then linear or exponential
regression lines. The formula is Y = N/1+Ab^-t where N = 281422, A =
281421, b = %increase/time (slope), and t = time (x axis) my data points are
as follows:
(0,38558),(10,49371),(20,62980),(30,76212),(40,922 28),(50,106022),(60,123203),(70,132165),(80,151326 ),(90,179323),(100,203212),(110,226546),(120,24871 0),(130,281422) examples are greatly appreciated.

I'm a little bit confused. What is the value of b? Are the pairs (t,Y)? Is
t in seconds or minutes?

"TRUSSMAN" wrote:

I need to fit a logistic curve to data points on a scatter plot to determine
if regression (r^2) values are closer to 1, then linear or exponential
regression lines. The formula is Y = N/1+Ab^-t where N = 281422, A =
281421, b = %increase/time (slope), and t = time (x axis) my data points are
as follows:
(0,38558),(10,49371),(20,62980),(30,76212),(40,922 28),(50,106022),(60,123203),(70,132165),(80,151326 ),(90,179323),(100,203212),(110,226546),(120,24871 0),(130,281422) examples are greatly appreciated.

t is in years, the value of b (slope) differs depending on the value of t,
and yes the pairs are (t,Y)

"Joel" wrote:

I'm a little bit confused. What is the value of b? Are the pairs (t,Y)? Is
t in seconds or minutes?

"TRUSSMAN" wrote:

I need to fit a logistic curve to data points on a scatter plot to determine
if regression (r^2) values are closer to 1, then linear or exponential
regression lines. The formula is Y = N/1+Ab^-t where N = 281422, A =
281421, b = %increase/time (slope), and t = time (x axis) my data points are
as follows:
(0,38558),(10,49371),(20,62980),(30,76212),(40,922 28),(50,106022),(60,123203),(70,132165),(80,151326 ),(90,179323),(100,203212),(110,226546),(120,24871 0),(130,281422) examples are greatly appreciated.

Use Linest, Logest, or Trend. These are worksheet function that you can put
on you worksheet or call the from VBA

worksheetfunctions.Linest(Parm1, Parm2, Parm3)
worksheetfunctions.Logest(Parm1, Parm2, Parm3)
worksheetfunctions.Trend(Parm1, Parm2, Parm3)

=Linest(Parm1, Parm2, Parm3)
=Logest(Parm1, Parm2, Parm3)
=Trend(Parm1, Parm2, Parm3)

See worksheet help for more details. In VBA use Range("A1:D4") for Params


"TRUSSMAN" wrote:

t is in years, the value of b (slope) differs depending on the value of t,
and yes the pairs are (t,Y)

"Joel" wrote:

I'm a little bit confused. What is the value of b? Are the pairs (t,Y)? Is
t in seconds or minutes?

"TRUSSMAN" wrote:

I need to fit a logistic curve to data points on a scatter plot to determine
if regression (r^2) values are closer to 1, then linear or exponential
regression lines. The formula is Y = N/1+Ab^-t where N = 281422, A =
281421, b = %increase/time (slope), and t = time (x axis) my data points are
as follows:
(0,38558),(10,49371),(20,62980),(30,76212),(40,922 28),(50,106022),(60,123203),(70,132165),(80,151326 ),(90,179323),(100,203212),(110,226546),(120,24871 0),(130,281422) examples are greatly appreciated.

1. Enter your data points into two columns in Excel, one for the x-axis values (time) and one for the y-axis values.
2. Select the two columns of data and click on the Insert tab in the top menu.
3. Click on the Scatter chart type and select the chart subtype that has markers and lines.
4. Right-click on one of the data points in the chart and select Add Trendline from the drop-down menu.
5. In the Format Trendline pane that appears on the right side of the screen, select Logistic as the trendline type.
6. Check the box next to Display Equation on chart to show the equation for the logistic curve on the chart.
7. Check the box next to Display R-squared value on chart to show the R-squared value for the logistic curve on the chart.
8. Click Close to close the Format Trendline pane.
9. Your chart should now show the logistic curve that fits your data points, along with the equation and R-squared value.
   
   To calculate the logistic curve equation manually, you can use the formula you provided:
   Formula:

   Y = N / (1 + A * b^-t) 

   , where N = 281422, A = 281421, b = %increase/time (slope), and t = time (x axis).
   
   To calculate the slope (b), you can use the formula:
   Formula:

   b = (y2 - y1) / (x2 - x1) 

   , where (x1, y1) and (x2, y2) are any two points on the curve.
   
   Once you have the equation for the logistic curve, you can compare the R-squared value to those of linear and exponential regression lines to determine which best fits your data. A higher R-squared value indicates a better fit.