Question

Consider the following stock price data (in dollars) to answer the questions in this section. Use R. 10 7 20 12 75 15 9 18 4 12 14 8 Definitions: 1. Mean, median, mode, and standard deviation are obvious 2. Min-Max normalization: if x is a vector variable, the (x - min(x))/(max(x) - min(x)) 3. To find z-scores use the scale() function in R 4. Decimal scaling: Find the biggest absolute value in the data, count the number of digits in it, and let this value be d. Divide the variable in the dataset by 100 3(mean - median) 5. Use the following to estimate skewness: (standard deviation Questions: 1. Calculate the mean, median, and mode stock price 2. Compute the standard deviation of the stock price. Interpret what this number means. 3. Find the min-max normalized stock price for the stock worth $20. 4. Calculate the midrange stock price. 5. Compute the Z-score standardized stock price for the stock worth $20. 6. Find the decimal scaling stock price for the stock worth $20. 7. Calculate the skewness of the stock price data. NAIVE BAYES CLASSIFICATION: (10 points) Self-Assessment Exercise 1 for Chapter 3 (page 37); Entering the data: Enter the data from Figure 3.1 in the text into R. Either you can enter it directly into Ror alternately you can enter it in Excel, save it as a "csv" file and then import it into R. After it is imported into Rassign it to a variable named train. This is the training dataset. 1. Page 13

Answer 1

1.CODE SCREENSHOT:

2.OUTPUT:

RGui (64-bit) File Edit View Misc Packages Windows Help STOP R Console > source ("D:\\java\\R PROGRAMMING EXAMPLES\\StockPrice.R") [1] 10 7 20 12 75 15 9 18 4 12 8 14 [1] "Mean :" "17" [1] "Median :" "12" [1] "Mode" "12" [1] "Standard Deviation" "18.8293774338254" [1] "The value $20 after Min-Max Normalization" [2] "0.225352112676056" [1] "The Z scores of $20" [1] 0.1593255 [1] "The Stock Price $20After Decaling" [1] 0.2 [1] "Skewness of the stock price" "0.796627506815691"

3.CODE:

# Create the function. getmode <- function(v) { t <- unique(v) t[which.max(tabulate(match(v, t)))] } stock=c(10,7,20,12,75,15,9,18,4,12,8,14) print(stock) Mean=mean(stock); print(c("Mean :",Mean)) Median=median(stock); print(c("Median :",Median)) Mode=getmode(stock); print(c("Mode",Mode)); Sdd=0.0+sd(stock); print(c("Standard Deviation",Sdd)) x=20; xVal=(x-min(stock))/(max(stock)-min(stock)); print(c("The value $20 after Min-Max Normalization",xVal)); print("The Z scores of $20"); print(scale(c(stock))[3,]); d=floor(log10(x)) + 1; DecScaliing=stock/10^d print("The Stock Price $20After Decaling"); print(DecScaliing[3]); skewness=3*(Mean-Median)/Sdd; print(c("Skewness of the stock price",skewness));