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In other words, the intercept from the model with no predictor variables is the estimated log odds of being in honors class for the whole population of interest.

Writing it in an equation, the model describes the following linear relationship. Now we can relate the odds for males and females and the output from the logistic regression. The intercept of -1.

Using the odds we calculated above for males, we can confirm this: log(. The coefficient for female is the log of odds ratio between the female group and male group: log(1.

So we can get the odds ratio by exponentiating the coefficient for female. Most statistical packages display both the raw regression coefficients and the exponentiated coefficients for logistic regression models. The table below is created by Stata. In other words, the odds of being in an honors class when the math score is zero is exp(-9.

These odds are very low, but if we look at the distribution of the variable math, we will see that no one in the sample has math score lower than 30. In fact, all the test scores in the data set were standardized around mean of 50 and standard deviation of 10. So the intercept in this model corresponds to the log odds of being in an honors class when math is at the hypothetical value of zero. How do we interpret the coefficient for math.

We will use 54. Then the conditional logit of being in an honors class when the math score is held at 54 isWe can examine the effect of a one-unit increase in math score. When the math score is held at 55, the conditional logit of being in an honors class isWe can say now that the coefficient for math is the difference in the log odds.

In other words, for a one-unit increase in the math score, the expected change in log odds is. Can we translate this change in log odds to the change in odds. Recall that logarithm converts multiplication and division to addition and subtraction. Its inverse, the exponentiation converts addition and subtraction back to multiplication and division.

Applying such a model to our example dataset, each estimated coefficient is the expected change in the log odds of being in an honors class for a unit increase in the corresponding predictor variable holding the other predictor variables constant at certain value. Each exponentiated coefficient is the ratio of two odds, or the change in odds in the multiplicative scale for a unit increase in the corresponding predictor variable holding other variables at certain value.

Here is an example. In all the previous examples, we have said that the regression coefficient of a variable corresponds to the change in log odds and its exponentiated form corresponds to the odds ratio. This is only true when our model does not have any interaction terms. The interpretation of the regression coefficients become more involved.

In this simple example where we examine the interaction of a binary variable and a continuous variable, we can think that we actually have two equations: one for males and one for females. Now we can map the logistic regression output to these two equations.

More explicitly, we can say that for male students, a one-unit increase in math score yields a change in log odds of 0. On the other hand, for the female students, a one-unit increase in math score yields a change in log odds of (. In terms of odds ratios, we can say that for male students, the odds ratio is exp(.

The ratio of these two odds ratios (female over male) turns out to be the exponentiated coefficient for the interaction term of female by math: 1.Wouldn't want to miss it.

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In the event of a match starting but not being completed, bets will be void unless the outcome is already determined. Unless otherwise stated all Rugby bets are settled on 80 minutes play. The term 80 minutes play includes any stoppage time. Unless otherwise stated Rugby League nines match bets are settled on the specific tournament regulation play and exclude extra-time if played. Are void unless re-arranged and played in the same 'Rugby Week' (Monday - Sunday inclusive UK time).

Regular season only unless otherwise stated. The finishing position of teams at the end of the scheduled programme of matches will determine placings with no allowance for playoffs or subsequent enquiries (and potential point deductions) by the respective leagues.

To Finish Bottom - Will be settled on the team who finish in the bottom position of the stipulated league upon the completion of the regular season. To Be Relegated - Where market is offered settlement is based upon the rules of the specified league. For these markets try includes penalty try.

For all scoring play markets conversions do not count. In the event of the half-time or full-time result ending in a Tie, then bets will be settled on Any Other option. Score 1st, Lead at Half Time and Win at Full time - Predict the outcome of all three. In the event of the combination not being offered then bets will be settled on Any Other option. Winning Margin Including Exact and Alternative - For settlement purposes the margin at full-time is used (Tie option is available).

Race to Markets - Predict the team to reach a specified points total first (Neither is an available option). Half Betting - 1st half markets are settled at the end of the 1st half. Lance Todd Trophy - Applies to the Rugby League Challenge Cup Final. The Man of the Match is decided at the end of the game and awarded the Lance Todd Trophy. Prices are offered for each player participating in the match. Where offered, all markets which indicate Golden Point or Overtime in the title will be settled including all periods of Overtime if played, for settlement purposes.

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