An article in Optical Engineering reported on use of an optical correlator to perform an experiment by varying brightness and contrast. The useful range of gray levels characterizes the resulting modulation. The data are shown below:
Brightness (%): 54 61 65 100 100 100 50 57 54
Contrast (%): 56 80 70 50 65 80 25 35 26
Useful range (ng): 96 50 50 112 96 80 155 144 255
USE MINITAB TO CARRY OUT THE FOLLOWING:
Fit a multiple linear regression model to these data.
Estimate Ïƒ2.
Predict the useful range when brightness = 80 and contrast = 75.
Test for significance of regression using Î± = 0.05. What is the p-value for this test?
Find a 95% confidence interval and prediction interval when brightness = 80 and contrast = 75.
Analyze R2.
Analyze the residuals.
Analyze using Best Subsets.

For the time being, our first task is to determine the sensitivity, which is the derivative of our discovery. The quotient rule, whose derivative we shall be utilizing, says that the derivative of F org equals G f prime minus f g prime all over g squared.

The bottom function is one plus four times the top function's derivative, or 40.4 times. 40 has a derivative of zero.

When a light source is x cm away from a light probe, the light's intensity is measured as y microwatts per square cm. The outcomes are displayed as ordered pairs( x , y).

So we just need to multiply 24 x240.4.

Using the power rule, we can reduce the 0.4 point to 9.6 x to the minus 0.6.

We subtract one from the initial exponents minus the top function 40 + 24 x to the point for times the bottom function's derivative 10 So we have the derivative of four additional 40.4 by the power rule,

we bring down the 0.4 multiplied by four against 1.6 next to the minus 0.6, and we divide all of that by one plus four x to the 40.4 squared.

To learn more about light sensitivity

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