computers <- read.table("http://pp98647.home.amu.edu.pl/wp-content/uploads/2021/06/computers.csv") View(computers) computers <- read.csv("http://pp98647.home.amu.edu.pl/wp-content/uploads/2021/06/computers.csv") View(computers) spotify <- read.csv("http://pp98647.home.amu.edu.pl/wp-content/uploads/2021/06/spotify.csv") View(spotify) weight_height <- read.csv("http://pp98647.home.amu.edu.pl/wp-content/uploads/2021/06/weight-height.csv") View(weight_height) model_1 <- lm(valence ~ acusticness + danceability + energy + instrumentalness + liveness, data = spotify) model_1 <- lm(valence ~ acousticness + danceability + energy + instrumentalness + liveness, data = spotify) model_1 <- lm(valence ~ acousticness + danceability + energy + instrumentalness + liveness + loudness + speechiness + tempo + song_title, data = spotify) model_1 summary(model_1) model_1 <- lm(valence ~ acousticness + danceability + energy + instrumentalness + liveness + loudness + speechiness + tempo, data = spotify) summary(model_1) step(model_1) step(model_1) step(model_1) summary(model_1) model_2 <- lm(valence ~ acousticness + danceability + energy + instrumentalness + liveness + loudness + speechiness, data = spotify) new_data <- data.frame(acousticness=2.84e-06, danceability=0.305, energy=0.827, instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789, speechiness=0.1470) View(new_data) stats::predict(model_2, new_data, interval = "prediction") summary(model_2)$adj.r.squared new_data <- data.frame(acousticness=2.84e-06, danceability=0.305, energy=0.827, instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789, speechiness=0.1470, tempo=159.882) stats::predict(model_2, new_data, interval = "prediction") new_data <- data.frame(acousticness=2.84e-06, danceability=1.305, energy=0.827, instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789, speechiness=0.1470, tempo=159.882) stats::predict(model_2, new_data, interval = "prediction") new_data <- data.frame(acousticness=2.84e-06, danceability=0.305, energy=0.827, instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789, speechiness=0.1470, tempo=159.882) stats::predict(model_2, new_data, interval = "prediction") new_data <- data.frame(acousticness=2.84e-06, danceability=0.405, energy=0.827, instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789, speechiness=0.1470, tempo=159.882) stats::predict(model_2, new_data, interval = "prediction") new_data <- data.frame(acousticness=2.84e-06, danceability=0.305, energy=0.827, instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789, speechiness=0.1470, tempo=159.882) stats::predict(model_2, new_data, interval = "prediction") new_data <- data.frame(acousticness=2.84e-06, danceability=0.405, energy=0.827, instrumentalness=2.45e-03, liveness=0.3350, loudness=-5.789, speechiness=0.1470, tempo=159.882) stats::predict(model_2, new_data, interval = "prediction") 0.3918359 - 0.3918359 0.3918359 - 0.3229826 male <- ifelse(weight_height$Gender == "Male") male <- weight_height$Gender == "Male" male <- ifelse(weight_height$Gender == "Male", weight_height$Height) male <- ifelse(weight_height$Gender == "Male", weight_height$Height, 0) male <- weight_height[weight_height$Gender == "Male"] male <- weight_height[weight_height$Gender == "Male", ] View(male) shapiro.test(male$Height) qqnorm(male$Height) shapiro.test(male$Weight) qqnorm(male$Weight) shapiro.test(male$Height) qqnorm(male$Height) mean(male$Height) par(mfrow = c(1, 2)) female <- weight_height[weight_height$Gender == "Female", ] shapiro.test(female$Height) qqnorm(female$Height) mean(female$Height) par(mfrow = c(1, 2)) male <- weight_height[weight_height$Gender == "Male", ] shapiro.test(male$Height) qqnorm(male$Height) mean(male$Height) female <- weight_height[weight_height$Gender == "Female", ] shapiro.test(female$Height) qqnorm(female$Height) mean(female$Height) par(mfrow = c(1, 2)) male <- weight_height[weight_height$Gender == "Male", ] shapiro.test(male$Height) qqnorm(male$Height) mean(male$Height) var(male$Height) female <- weight_height[weight_height$Gender == "Female", ] shapiro.test(female$Height) qqnorm(female$Height) mean(female$Height) var(female$Height) t.test(male$Height, female$Height, var.equal = TRUE, alternative = 'greater')$p.value t.test(male$Height, female$Height, var.equal = TRUE, alternative = 'greater') t.test(male$Height, female$Height, paired = TRUE, alternative = 'less')$p.value t.test(male$Height, female$Height, paired = TRUE, alternative = 'greater')$p.value t.test(male$Height, female$Height, alternative = 'greater')$p.value t.test(male$Height, female$Height, alternative = 'greater') t.test(male$Height, female$Height, alternative = 'greater', conf.level = 0.05)$p.value selected <- computers[computers$screen == 14, ] View(selected) ram_procent <- data.frame(cbind(liczebnosc = table(selected$ram), procent = prop.table(selected$ram))) table(selected$ram) prop.table(selected$ram) table(selected$ram) liczebnosc <- table(selected$ram) prop.table(liczebnosc) prop.table(liczebnosc)*100 # ZAD 2 - tego trochę nie rozumiem w_test <- function(x, istotnosc, delta_zero, alternative = c('two.sided', 'less', 'greater')) { # statystyka testowa ss <- (1 / length(x)) * (var(x) - mean(x)) statistic <- length(x) * ss / delta_zero * delta_zero # parametr w obszarach krytycznych d <- length(x) - 1 # poziom istotności alternative <- match.arg(alternative) p_value <- istotnosc p_value <- switch(alternative, 'two.sided' = 2 * min(p_value, 1 - p_value), 'greater' = p_value, 'less' = 1 - p_value) # rezultat names(statistic) <- 'T' names(d) <- 'num df' result <- list(statistic = statistic, parameter = d, p.value = p_value, alternative = alternative, method = 'Test istotności dla wariancji w modelu normalnym', data.name = deparse(substitute(x))) class(result) <- 'htest' return(result) }