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# 统计作业代写Statistics代考|An Alternative to the LRM: Logistic Regression

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• Date Analysis数据分析
• Actuarial Science 精算科学
• Bayesian Statistics 贝叶斯统计
• Generalized Linear Model 广义线性模型
• Macroeconomic statistics 宏观统计学
• Microeconomic statistics 微观统计学
• Logistic regression 逻辑回归
• linear regression 线性回归

## 统计作业代写STATISTICS代考|probabilities and placing

Binary logistic regression accomplishes this feat by transforming the regression equation through the use of the logistic function, represented in Equation 16.1. 5
$$p(y=1)=\frac{1}{1+\exp \left[-\left(\alpha+\beta_{x} x_{1}+\cdots+\beta_{k} x_{k}\right)\right]}$$
The part of the denominator in the equation in parentheses is similar to the LRM we’ve used in earlier chapters but is transformed in a specific way. The advantage of this function is that it guarantees that the predicted values range from zero to one, just as probabilities are supposed to do. To illustrate, Table $16.1$ shows several negative and positive predicted values from an LRM and their values after running them through the function. If we placed more extreme values in the function it would return numbers closer to zero or one, but, consistent with probabilities, they would never fall outside the boundary of $[0,1]$.

## 统计作业代写STATISTICS代考|hypothetical to two variables

An example closer to the interests of the research community involves the following scenario. Suppose we conduct a survey of adolescents and find that $25 \%$ report using marijuana and $75 \%$ report not using marijuana in the past year. The probability of marijuana use ${P$ (marijuana use) $}$ among adolescents in the sample is $0.25$. What are the odds of marijuana use?
$$\text { Odds }_{m j}=\frac{p(\text { marijuana use })}{1-p(\text { marijuana use })}=\frac{0.25}{1-0.25}=0.33 \text { or } \frac{1}{3}$$
Equation $16.3$ implies that for every adolescent who uses marijuana, we expect three adolescents who do not use marijuana or, stated differently, three times as many adolescents did not use marijuana as did use marijuana in the past year.

Restricting our attention to only one variable is seldom interesting. Let’s extend the hypothetical to two variables. We’ll treat adolescent past-year marijuana use as the outcome variable. To keep it simple, we’ll use an indicator variable that is coded $0=$ female and $1=$ male. We now have four outcomes, with males and females who used or did not use marijuana. If we’re interested only in probabilities, we may compare the two: $P$ (male marijuana use) and $P$ (female marijuana use) and see if one is higher. But keeping our interest on the odds, let’s compare the odds of marijuana use among male and female adolescents.

## 统计作业代写STATISTICS代考|PROBABILITIES AND PLACING

p(和=1)=11+经验⁡[−(一种+bXX1+⋯+b到X到)]

## 统计作业代写STATISTICS代考|HYPOTHETICAL TO TWO VARIABLES

赔率 米j=p( 大麻使用 )1−p( 大麻使用 )=0.251−0.25=0.33 要么 13

## matlab代写

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.