Logistic regression is linear when the parameter, $\pi$, that controls the behavior of the Bernoulli response is transformed into a log odds:
$$ \ln\left(\frac{\pi_i}{1-\pi_i}\right) = \beta_0 + \beta_1x_i $$ Your variable PASS is a vector of predicted probabilities. These can be converted into log odds using the LHS of the equation above. Once there, these should form a straight line as a function of RIT. Here is some R code to do this:

oPASS = PASS / (1-PASS)
loPASS = log(oPASS)

A plot of these values shows that there was some rounding in the predicted probabilities that you were given:

You can also see the issue if you look at the loPASS variable:

> loPASS
 [1]       -Inf       -Inf       -Inf -4.5951199 -4.5951199 -4.5951199
 [7] -3.8918203 -3.1780538 -2.7515353 -2.1972246 -1.7346011 -1.2083112
[13] -0.7081851 -0.2006707  0.2818512  0.8001193  1.3249254  1.8152900
[19]  2.3136349  2.7515353  3.1780538  3.8918203  4.5951199  4.5951199
[25]        Inf        Inf        Inf        Inf        Inf        Inf
[31]        Inf        Inf        Inf        Inf        Inf        Inf
[37]        Inf

Thus, we will work with the 7th & 23rd data points to get a reasonably accurate result.

Once we have these values, we can calculate the slope using the point-slope formula, and the intercept, by algebraically rearranging the equation of the line:

b1 = (loPASS[23]-loPASS[7]) / (RIT[23]-RIT[7])
b0 = loPASS[7] - b1*RIT[7]

That yields the parameter estimates that had been used to generate the predicted probabilities that you were given:

> b0
[1] -19.80483
> b1
[1] 0.1060868

For more information about logistic regression, it may help you to read my answer here: difference-between-logit-and-probit-models.

Logistic regression is linear when the parameter, $\pi$, that controls the behavior of the Bernoulli response is transformed into a log odds:
$$ \ln\left(\frac{\pi_i}{1-\pi_i}\right) = \beta_0 + \beta_1x_i $$ Your variable PASS is a vector of predicted probabilities. These can be converted into log odds using the LHS of the equation above. Once there, these should form a straight line as a function of RIT. Here is some R code to do this:

oPASS = PASS / (1-PASS)
loPASS = log(oPASS)

A plot of these values shows that there was some rounding in the predicted probabilities that you were given:

You can also see the issue if you look at the loPASS variable:

> loPASS
 [1]       -Inf       -Inf       -Inf -4.5951199 -4.5951199 -4.5951199
 [7] -3.8918203 -3.1780538 -2.7515353 -2.1972246 -1.7346011 -1.2083112
[13] -0.7081851 -0.2006707  0.2818512  0.8001193  1.3249254  1.8152900
[19]  2.3136349  2.7515353  3.1780538  3.8918203  4.5951199  4.5951199
[25]        Inf        Inf        Inf        Inf        Inf        Inf
[31]        Inf        Inf        Inf        Inf        Inf        Inf
[37]        Inf

Thus, we will work with the 7th & 23rd data points to get a reasonably accurate result.

Once we have these values, we can calculate the slope using the point-slope formula, and the intercept, by algebraically rearranging the equation of the line:

b1 = (loPASS[23]-loPASS[7]) / (RIT[23]-RIT[7])
b0 = loPASS[7] - b1*RIT[7]

That yields the parameter estimates that had been used to generate the predicted probabilities that you were given:

> b0
[1] -19.80483
> b1
[1] 0.1060868

For more information about logistic regression, it may help you to read my answer here: difference-between-logit-and-probit-models.

Logistic regression is linear when the parameter, $\pi$, that controls the behavior of the Bernoulli response is transformed as a log odds:
$$ \ln\left(\frac{\pi_i}{1-\pi_i}\right) = \beta_0 + \beta_1x_i $$ Your variable PASS is a vector of predicted probabilities. These can be converted into log odds using the LHS of the equation above. Once there, these should form a straight line as a function of RIT. Here is some code to do this:

oPASS = PASS / (1-PASS)
loPASS = log(oPASS)

A plot of these values, shows that there was some rounding in the predicted probabilities that you were given:

You can see the issue if you look at the loPASS variable:

> loPASS
 [1]       -Inf       -Inf       -Inf -4.5951199 -4.5951199 -4.5951199
 [7] -3.8918203 -3.1780538 -2.7515353 -2.1972246 -1.7346011 -1.2083112
[13] -0.7081851 -0.2006707  0.2818512  0.8001193  1.3249254  1.8152900
[19]  2.3136349  2.7515353  3.1780538  3.8918203  4.5951199  4.5951199
[25]        Inf        Inf        Inf        Inf        Inf        Inf
[31]        Inf        Inf        Inf        Inf        Inf        Inf
[37]        Inf

Thus, we will work with the 7th & 23rd data points to get a reasonably accurate result.

Once we have these values, we can calculate the slope using the point-slope formula, and the intercept, by algebraically rearranging the equation of the line:

b1 = (loPASS[23]-loPASS[7]) / (RIT[23]-RIT[7])
b0 = loPASS[7] - b1*RIT[7]

That yields the parameter estimates that had been used to generate the predicted probabilities that you were given:

> b0
[1] -19.80483
> b1
[1] 0.1060868

For more information about logistic regression, it may help you to read my answer here: difference-between-logit-and-probit-models.

Logistic regression is linear when the parameter, $\pi$, that controls the behavior of the Bernoulli response is transformed into a log odds:
$$ \ln\left(\frac{\pi_i}{1-\pi_i}\right) = \beta_0 + \beta_1x_i $$ Your variable PASS is a vector of predicted probabilities. These can be converted into log odds using the LHS of the equation above. Once there, these should form a straight line as a function of RIT. Here is some R code to do this:

oPASS = PASS / (1-PASS)
loPASS = log(oPASS)

A plot of these values shows that there was some rounding in the predicted probabilities that you were given:

You can also see the issue if you look at the loPASS variable:

> loPASS
 [1]       -Inf       -Inf       -Inf -4.5951199 -4.5951199 -4.5951199
 [7] -3.8918203 -3.1780538 -2.7515353 -2.1972246 -1.7346011 -1.2083112
[13] -0.7081851 -0.2006707  0.2818512  0.8001193  1.3249254  1.8152900
[19]  2.3136349  2.7515353  3.1780538  3.8918203  4.5951199  4.5951199
[25]        Inf        Inf        Inf        Inf        Inf        Inf
[31]        Inf        Inf        Inf        Inf        Inf        Inf
[37]        Inf

Thus, we will work with the 7th & 23rd data points to get a reasonably accurate result.

Once we have these values, we can calculate the slope using the point-slope formula, and the intercept, by algebraically rearranging the equation of the line:

b1 = (loPASS[23]-loPASS[7]) / (RIT[23]-RIT[7])
b0 = loPASS[7] - b1*RIT[7]

That yields the parameter estimates that had been used to generate the predicted probabilities that you were given:

> b0
[1] -19.80483
> b1
[1] 0.1060868

For more information about logistic regression, it may help you to read my answer here: difference-between-logit-and-probit-models.