Forensic scientists can learn about events at a crime scene by collecting data. Ex: Properties ofglass shards at a crime scene such as chemical composition can indicate what type of glass wasbroken at the scene. Possible types include building glass (building windows or doors), vehicle glass(car windows or doors), or household glass (lightbulbs, baking dishes).The fitted logistic regression model for predicting whether a glass shard is building glass based onsodium is:=20.02+(-1.42) (sodium)1+e20.02+(-1.42) (sodium)Calculate the log-odds that a glass shard with sodium = 13.08 is building glass.Ex: 1.23CCalculate the probability that a glass shard with sodium = 13.08 is building glass.

1. Log-odds CalculationThe logistic regression model given…

Answered: Forensic scientists can learn about…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Variation in gasoline mileage among makes and models of automobiles is influenced substantially by the weight and horsepower of the vehicles. An automobile manufacturer would like to determine if heavier cars consumer more gas than lighter cars. He collects data on 25 heavy and 25 light cars to test this hypothesis. What analysis would you complete to answer this question?
A study was made of movies in 2005. For each movie, the following were recorded: its budget (the amount it cost to make, in millions of dollars), its running time (from start to finish, in minutes), and the genre (drama, comedy, action, etc.). The scatterplot below shows the budget vs. running length for all movies. Two regression lines are shown on the scatterplot, for predicting budget from running length. The lower line is for movies of the "drama" genre, while the upper line is for movies of all other genres. Which of the statements below do you most strongly agree with? (a) The difference in budget between dramas and other movies of the same length remains constant as the movies get longer. (b) Dramas cost less to make than other movies of the same length, and that difference increases as the movies get longer. (c) Dramas cost more to make than other movies of the same length, and that difference increases as the movies get longer. (d) Dramas cost more to make than other movies of…
Studies have shown that people who suffer sudden cardiac arrest have a better chance of survival if a defibrillator shock is administered very soon after cardiac arrest. How is survival rate related to the time between when cardiac arrest occurs and when the defibrillator shock is delivered? The accompanying data give y = survival rate (percent) and x = mean call-to-shock time (minutes) for a cardiac rehabilitation center (in which cardiac arrests occurred while victims were hospitalized and so the call-to-shock time tended to be short) and for four communities of different sizes. Mean call-to-shock time, x 2 6 79 12 Survival rate, y (a) Construct a scatterplot for these data. y 12. 10 O 8 6 20 40 60 91 44 32 6 4 80 100 y 100 80 60 ELL 40 20 2 4 6 8 X 12 10 8 6 4 20 40 60 (b) Find the equation of the least-squares line. (Round your values to four decimal places.) ŷ= Jx 80 100 10 12 (c) Use the least-squares line to predict survival rate for a community with a mean call-to-shock time of…
Explain whether each scenario below is a regression, classification, or unsupervised learn- ing problem. If it is a supervised learning scenario, indicate whether we are more interested in inference or prediction. Finally, provide in each case the number of observations, n, and the number of predictors, p. (1) An online retailer must decide whether to display advertisement A or advertisement B to each customer on the basis of collected customer demographics (age, income, zip code, and gender). A set of 150 of its customers have already expressed a preference for one advertisement or the other. (2) A policy analyst is interested in discovering factors that are associated with the crime rate across different U.S. cities. For each of 500 cities, the policy analyst gathers the following data: the crime rate, unemployment rate, population, median income, median home price, and state. (3) The the channel owner to see where the subscribers are located, their age and gender, the times and days…
For this project you will collect at least 10 pairs of quantitative data and conduct a hypothesis test for linear correlation and also give the best predicted value of y for a given value of x. Below is my example:(you can't use my example, you have to come up with your own) I am collecting data to see if there is correlation between a person's age and how many hours they use the phone I will also give the best predicted value for a person who is 40 years old. My data age hours 19 7.3 25 6.5 31 4.6 18 3.2 25 5.2 27 2.9 26 3.7 39 6.5 47 4.1 55 3.9 A)l will test the claim that there is no correlation (start with the Claim and finish with the conclusion) C: Ho HA etc B) will give the best estimate for how many hours a person who is 35 years old uses the phone. (make sure to use the write y and x and include the P value in your answer and decide if you will plug in x or find the average of the y's) The P value is (big/small) so I have to (either give the average of the y's or plug in the x…
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The equation of a regression line, unlike the correlation, depends on the units we use to measure the explanatory and response variables. Here is the data on percent body fat and preferred amount of salt. Preferred amountof salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 21 30 23 30 39 24 31 In calculating the preferred amount of salt, the weight of the salt was in milligrams. (a) Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in milligrams. (Round your answers to one decimal place.) ŷ = + x (b) A mad scientist decides to measure weight in tenths of milligrams. The same data in these units are as follows. Preferred amountof salt x 2 3 4 5 6 8 11 Percent body fat y 21 30 23 30 39 24 31 Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in tenths of milligrams. (Round your intercept to one decimal place and your slope to two…
Given the equation of a regression line is y= -1.5x-1.8, what is the best predicted value for y given x= -3.0? Assume that the variables x and y have significant correlation.
Please help asap
What does the square of the linear correlation coefficient measure (rxr)?
Would someone familiar with SPSS be able to help with sub-question d please?
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to Excel

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