90 lines
2.9 KiB
R
90 lines
2.9 KiB
R
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# testowy egzamin
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# ładowanie danych csv
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computers <- read.csv("http://pp98647.home.amu.edu.pl/wp-content/uploads/2021/06/computers.csv")
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spotify <- read.csv("http://pp98647.home.amu.edu.pl/wp-content/uploads/2021/06/spotify.csv")
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weight_height <- read.csv("http://pp98647.home.amu.edu.pl/wp-content/uploads/2021/06/weight-height.csv")
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# ZAD 2 - tego trochę nie rozumiem
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w_test <- function(x, istotnosc, delta_zero, alternative = c('two.sided', 'less', 'greater')) {
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# statystyka testowa
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ss <- (1 / length(x)) * (var(x) - mean(x))
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statistic <- length(x) * ss / delta_zero * delta_zero
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# parametr w obszarach krytycznych
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d <- length(x) - 1
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# poziom istotności
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alternative <- match.arg(alternative)
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p_value <- istotnosc
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p_value <- switch(alternative,
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'two.sided' = 2 * min(p_value, 1 - p_value),
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'greater' = p_value,
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'less' = 1 - p_value)
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# rezultat
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names(statistic) <- 'T'
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names(d) <- 'num df'
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result <- list(statistic = statistic,
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parameter = d,
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p.value = p_value,
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alternative = alternative,
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method = 'Test istotności dla wariancji w modelu normalnym',
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data.name = deparse(substitute(x)))
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class(result) <- 'htest'
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return(result)
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}
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# ZAD 5
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model_1 <- lm(valence ~ acousticness + danceability + energy + instrumentalness +
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liveness + loudness + speechiness + tempo, data = spotify)
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summary(model_1)
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step(model_1)
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model_2 <- lm(valence ~ acousticness + danceability + energy + instrumentalness +
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liveness + loudness + speechiness, data = spotify)
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new_data <- data.frame(acousticness=2.84e-06, danceability=0.305, energy=0.827,
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instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789,
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speechiness=0.1470, tempo=159.882)
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stats::predict(model_2, new_data, interval = "prediction")
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new_data <- data.frame(acousticness=2.84e-06, danceability=0.405, energy=0.827,
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instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789,
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speechiness=0.1470, tempo=159.882)
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stats::predict(model_2, new_data, interval = "prediction")
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# ZAD 6
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par(mfrow = c(1, 2))
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male <- weight_height[weight_height$Gender == "Male", ]
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shapiro.test(male$Height)
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qqnorm(male$Height)
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mean(male$Height)
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var(male$Height)
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female <- weight_height[weight_height$Gender == "Female", ]
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shapiro.test(female$Height)
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qqnorm(female$Height)
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mean(female$Height)
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var(female$Height)
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t.test(male$Height, female$Height, alternative = 'greater')$p.value
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#bardzo mała wartość, czyli hipoteza że kobiety są jest większe niż mężczyźni jest mało prawdopodobne
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# ZAD 7
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selected <- computers[computers$screen == 14, ]
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liczebnosc <- table(selected$ram)
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prop.table(liczebnosc)*100
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ram_procent <- data.frame(cbind(liczebnosc = table(selected$ram),
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procent = prop.table(selected$ram)))
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