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A logistic regression model was proposed for classifying common brushtail possums into their two regions in Exercise $6.13 .$ Use the results of the summary table for the reduced model presented in Exercise 6.13 for the questions below. The outcome variable took value 1 if the possum was from Victoria and 0 otherwise.(a) Write out the form of the model. Also identify which of the following variables are positively associated (when controlling for other variables) with a possum being from Victoria: skull_width, total_length, and tail_length.(b) Suppose we see a brushtail possum at a zoo in the US, and a sign says the possum had been captured in the wild in Australia, but it doesn't say which part of Australia. However, the sign does indicate that the possum is male, its skull is about $63 \mathrm{~mm}$ wide, its tail is $37 \mathrm{~cm}$ long, and its total length is $83 \mathrm{~cm} .$ What is the reduced model's computed probability that this possum is from Victoria? How confident are you in the model's accuracy of this probability calculation?

Intro Stats / AP Statistics

Chapter 6

Multiple and logistic regression

Linear Regression and Correlation

Temple University

Piedmont College

Cairn University

University of St. Thomas

Lectures

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02:40

Use the results from Probl…

06:13

Lobster trap placement. Re…

This is problem number 20 We are given a set of data regarding the length of the bear and the bears weight and our tests to first find the least grocery Russian to do this, we will find our five key data points. So we have some vax is 1017. Some of Y685, some of X times y 96 90 some of X squared is 1 56 5 49. And the sum of Y squared is 60,875. So we use these points with our two equations for coefficients and we find our least squares regression line as white hat equal to 0.61 seven X zero 330 So for part B we can interpret this firstly. We cannot directly interpret the Y intercept since it is negative and having a length of zero does not make sense though we can in turn interpret our slope. This pretty much says that an increase in length corresponds with a smaller increase in weight, specifically one cm more of length is .617. With the unit for this, the unit is kg, so .617 kg increase per centimeter length increase. So we can then find The prediction for the weight of a bear that is 149 cm long. Using our equation for x equal to 149,000 we get a white hat Equal to 91 0.57 Based on our input data, we know that Our value of 149 for why gives us exactly 85. So because our residual is 657 and it's positive, we can say that this bear specifically is below average for weight.

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