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 8 14 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 104 5. Use the following to estimate skewness: 3(mean - median) (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 (page37); Entering the data: 1. 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. Page 13

Answer 1

#defining vector
x = c(10,7,20,12,75,15,9,18,4,12,8,14)

1.

# 1. calculate mean , median and mode
mean_x = mean(x)
median_x = median(x)
# Create the function.
getmode <- function(x) {
uniqv <- unique(x)
uniqv[which.max(tabulate(match(x, uniqv)))]
}
mode_x = getmode(x)

#printing
> mean_X
[1] 17
> median_x
[1] 12
> mode_x
[1] 12

2.

> #standard deviation of x
> standard_dev_X = sd(x)
> standard_dev_X
[1] 18.82938

Interpretation: There is alot of variation in stock prices and standard deviation is $18.83

3.

> #min max normalisation for stock price $20
> min_max_normalised_value = (20 - min(x))/(max(x) - min(x))
> min_max_normalised_value
[1] 0.2253521

4.

> #mid range stock value
> mid_range = (max(x) - min(x))/2
> mid_range
[1] 35.5

5.

> #computing z score
> z_score_value = scale(20, center = mean_x, scale = standard_dev_X)
> z_score_value
[,1]
[1,] 0.1593255
attr(,"scaled:center")
[1] 17
attr(,"scaled:scale")
[1] 18.82938

6.

> #decimal scaling
> decimal_scaled_value = 20/(10^2)
> decimal_scaled_value
[1] 0.2

7.

> #skewness
> skewness_x = 3*(mean(x) - median(x))/(sd(x))
> skewness_x
[1] 0.7966275

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