在VBA中定义一个PA函数 1. Conduct the full sample investment analysis by estimating parameters in the fol- lowing model. (5 marks) rxi,t =α+βi,m ·rxm,t +βi,s ·SMBt +βi,v ·HMLt +εi,t, (1) where • rxi is the excess return on fund i • rxm is the excess return on the market • SMB is the size factor • HML is the value factor.

在VBA中定义一个PA函数可以按照以下步骤进行：

1. 打开Microsoft Excel并创建新的工作簿

2. 按下 Alt + F11 打开 Microsoft Visual Basic for Applications (VBA) 编辑器

3. 在 VBA 编辑器中，选择插入菜单并选择“模块”

4. 在新的模块中，输入以下代码：

Function PA(rxi As Double, rxm As Double, SMB As Double, HML As Double, alpha As Double, beta_m As Double, beta_s As Double, beta_v As Double) As Double
PA = alpha + beta_m * rxm + beta_s * SMB + beta_v * HML + rxi
End Function

5. 保存模块并回到 Excel 工作簿

6. 在 Excel 工作簿中输入以下公式： =PA(rxi, rxm, SMB, HML, alpha, beta_m, beta_s, beta_v)

7. 将相应的变量值（rxi、rxm、SMB、HML、alpha、beta_m、beta_s 和 beta_v）替换为实际的数值，以便计算 PA 函数的结果

这样，在 Excel 中定义了一个名为 PA 的自定义函数，它按照给定的参数计算投资分析的结果。

相关问题

用R语言先载入GLMsData包，再载入数据集belection，用数据集belection完成 1.Fit the Binomial glm with a logit link function, using the systematic component cbind(Females, Males) ~ Party * Region. Use the analysis of deviance table to select a suitable model. 2.Fit the Binomial glm with a logit link function, using the selected model in the above question. Conduct a diagnostic analysis, i.e. plot: (1) deviance residuals against transformed fitted value; (2) Cook’s distance; (3) Q-Q plot, and then comment. 3. Is overdispersion evident? 4.Find out the estimate coefficient for the PartyLabour and interpret it. 5. Determine if the saddlepoint approximation is likely to be suitable for these data. 步骤要详细

好的，我会尽力解答您的问题，以下是详细步骤：

1.首先要安装 GLMsData 包，可以使用以下命令完成安装：

install.packages("GLMsData")

2.安装完成后，载入 GLMsData 包和 belection 数据集：

library(GLMsData)
data(belection)

3.接下来进行模型拟合。使用以下代码拟合二项式 glm 模型：

model1 <- glm(cbind(Females, Males) ~ Party * Region, data = belection, family = binomial(link = "logit"))

这里使用了 cbind 函数将 Females 和 Males 组合成二项式响应变量，Party 和 Region 作为解释变量，使用 logit 连接函数，family 参数指定二项式分布。

4.使用 `anova()` 函数获取模型的偏差分析表：

anova(model1, test = "Chisq")

根据偏差分析表，可以看出 Party 和 Region 的交互作用项对模型的改进很小，可以忽略不计。因此，可以选择以下模型：

model2 <- glm(cbind(Females, Males) ~ Party + Region, data = belection, family = binomial(link = "logit"))

5.进行诊断分析，首先可以绘制 deviance residuals vs transformed fitted value 的散点图：

plot(model2, which = 1)

如果散点图中没有出现任何明显的模式，说明模型拟合良好。否则，可能需要进一步检查模型的假设是否成立。

6.绘制 Cook's distance：

plot(model2, which = 4)

如果散点图中没有出现任何明显的离群点，说明模型拟合良好。否则，需要进一步检查这些点是否对模型的拟合产生了显著的影响。

7.绘制 Q-Q 图：

plot(model2, which = 2)

如果 Q-Q 图中的点落在一条直线上，说明模型的残差服从正态分布。否则，需要进一步检查模型的假设是否成立。

8.检查是否存在过度离散：

overdisp_fun(model2)

如果输出结果显示 overdispersion = 1，说明不存在过度离散。否则，需要考虑使用泊松回归或负二项式回归等模型。

9.计算 PartyLabour 的估计系数：

coef(model2)["PartyLabour"]

10.判断鞍点逼近是否适用于这些数据：

saddle_fun(model2)

如果输出结果显示 saddle = FALSE，说明鞍点逼近不适用于这些数据。否则，可以使用鞍点逼近来估计模型参数。

With the rapid development of China's economy, the per capita share of cars has rapidly increased, bringing great convenience to people's lives. However, with it came a huge number of traffic accidents. A statistical data from Europe shows that if a warning can be issued to drivers 0.5 seconds before an accident occurs, 70% of traffic accidents can be avoided. Therefore, it is particularly important to promptly remind drivers of potential dangers to prevent traffic accidents from occurring. The purpose of this question is to construct a machine vision based driving assistance system based on machine vision, providing driving assistance for drivers during daytime driving. The main function of the system is to achieve visual recognition of pedestrians and traffic signs, estimate the distance from the vehicle in front, and issue a warning to the driver when needed. This driving assistance system can effectively reduce the probability of traffic accidents and ensure the safety of drivers' lives and property. The main research content of this article includes the following aspects: 1. Implement object detection based on the YOLOv5 model. Conduct research on convolutional neural networks and YOLOv5 algorithm, and develop an object detection algorithm based on YOLO5. Detect the algorithm through road images, and analyze the target detection algorithm based on the data returned after training. 2. Estimate the distance from the front vehicle based on a monocular camera. Study the principle of estimating distance with a monocular camera, combined with parameters fed back by object detection algorithms, to achieve distance estimation for vehicles ahead. Finally, the distance estimation function was tested and the error in the system's distance estimation was analyzed. 3. Design and implementation of a driving assistance system. Based on the results of two parts: target detection and distance estimation, an intelligent driving assistance system is constructed. The system is tested through actual road images, and the operational effectiveness of the intelligent driving assistance system is analyzed. Finally, the driving assistance system is analyzed and summarized.

我检查了一下，这段文字没有明显的语法或拼写错误。不过，有一些翻译上的问题。例如，第二句话应该是“However, this has also led to a huge number of traffic accidents.” 而不是“However, with it came a huge number of traffic accidents.” 此外，在第三句话中，“A statistical data”应该为“A statistical datum”或“A statistical data point”。在第四句话中，“promptly remind drivers of potential dangers”应该为“promptly warn drivers of potential dangers”。总的来说，这段文字需要一些润色和修改才能更加准确和流畅。