Obtain a density plot of the daily long returns of Dollar-Euro exchange rate.

时间: 2024-02-09 15:10:16 浏览: 35
To obtain a density plot of the daily long returns of Dollar-Euro exchange rate, you can follow these steps: 1. Collect the daily exchange rate data of Dollar-Euro from a reliable source such as Yahoo Finance or Quandl. 2. Calculate the daily long returns by taking the natural logarithm of the ratio of current exchange rate to the previous day's exchange rate. 3. Use a programming language such as R or Python to plot the density of the daily long returns. 4. Here is an R code example to create a density plot: ```R library(ggplot2) # read in exchange rate data exchange_rate <- read.csv("exchange_rate.csv") # calculate daily long returns exchange_rate$log_returns <- log(exchange_rate$rate) - log(lag(exchange_rate$rate)) # plot density ggplot(exchange_rate, aes(x = log_returns)) + geom_density(fill = "blue", alpha = 0.5) + labs(title = "Density Plot of Daily Long Returns of Dollar-Euro Exchange Rate") ``` This will produce a density plot of the daily long returns of Dollar-Euro exchange rate.

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GOAL Perform a Poisson regression to predict the number of people in a househouse based on the age of the head of the household. DATA The Philippine Statistics Authority (PSA) spearheads the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three years, is aimed at providing data on family income and expenditure, including levels of consumption by item of expenditure. The data, from the 2015 FIES, is a subset of 1500 of the 40,000 observations (Philippine Statistics Authority 2015). The data set focuses on five regions: Central Luzon, Metro Manila, Ilocos, Davao, and Visayas. The data is in the file fHH1.csv. Each row is a household, and the follow variables are recorded: • location: where the house is located (Central Luzon, Davao Region, Ilocos Region, Metro Manila, or Visayas) • age: the age of the head of household • total: the number of people in the household other than the head • numLT5: the number in the household under 5 years of age • roof: the type of roof in the household (either Predominantly Light/Salvaged Material, or Predominantly Strong Material. STEPS 1. Read in the dataset. 2. Produce a bar-chart of total 3. Produce a scatter-plot of total against age - add a smoothing line. 4. Fit the Poisson regression total ∼ age 5. Interpret the coefficient of age. 6. Obtain the Pearson residuals. Plot these against age. Is the model adequate? 7. Fit the Poisson regression total ∼ age + age2 8. Repeat the residual plots for the new model. 9. Compare the models using a likelihood ratio test, and AIC. 10. Calculate the predicted values for model M2. What is the age of the head of the household associated with the largest fitted value 使用R语言

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