Data 101 Final Project
Nia Pillow
2022-05-12
Topic: Sephora Beauty Website’s Rating and Product Analysis
Dataset: Sephore Website Data
Owner: Raghad Alharbi
Source: Kaggle.com
Data dictionary can be found at: https://www.kaggle.com/datasets/raghadalharbi/all-products-available-on-sephora-website
The variables included in this entire data set are the following: id, brand, category, name, size, rating, number of reviews, love, price, value price, URL, marketing flags, ingredients, online only, exclusive, limited edition, limited time offer.
The variables included in this entire data set are the following: id, brand, category, name, size, rating, number of reviews, love, price, value price, URL, marketing flags, ingredients, online only, exclusive, limited edition, limited time offer. She/he was able to gather all the data by scraping the Sephora Website for variables such as size, love, rating, the number of customers who left reviews, ingredients, brand, name, and category. This dataset contains 9800 observations and 21 variables. My data does contain both quantitative and categorical values. Quantitative values include price, rating, love, number of reviews, and value price. The categorical values include brand, name, category, and ingredients. Zero Missing Values – According to the r functions, I did not find any missing variables within my dataset. The Sephora Beauty Website dataset did contain 0 and 1 values within certain variable columns. These are considered binary variables and I wonder if I could or should have converted them for this project.
Does it have any personal importance for you? I wouldn’t say this project has had any personal importance to me other than simply loving to purchase and apply make-up! My mother went to cosmetology school and made money doing hair when I grew up and that was the only job where she and I could spend a lot of time together. I was surprised to learn that Sephora Beauty carries so many brands online than they do in the physical store, so I had a lot of data to work with.
—————— Loading Libraries ———————
#We will beging loading the packages we will need for this project.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dplyr) #to wrangle out data
library(ggplot2) #for plottinf our data
library(RColorBrewer) #adding some color ## Warning: package 'RColorBrewer' was built under R version 4.1.3———— Readind data……. ————–
library(readr)
sephora_website_dataset <- read_csv("sephora_website_dataset.csv")## Rows: 9168 Columns: 21
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (10): brand, category, name, size, URL, MarketingFlags_content, options,...
## dbl (10): id, rating, number_of_reviews, love, price, value_price, online_on...
## lgl (1): MarketingFlags
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.sephora_website_dataset## # A tibble: 9,168 x 21
## id brand category name size rating number_of_revie~ love price
## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2218774 Acqua Di Pa~ Fragran~ Blu ~ 5 x ~ 4 4 3002 66
## 2 2044816 Acqua Di Pa~ Cologne Colo~ 0.7 ~ 4.5 76 2700 66
## 3 1417567 Acqua Di Pa~ Perfume Aran~ 5 oz~ 4.5 26 2600 180
## 4 1417617 Acqua Di Pa~ Perfume Mirt~ 2.5 ~ 4.5 23 2900 120
## 5 2218766 Acqua Di Pa~ Fragran~ Colo~ 5 x ~ 3.5 2 943 72
## 6 1417609 Acqua Di Pa~ Perfume Fico~ 5 oz~ 4.5 79 2600 180
## 7 1638832 Acqua Di Pa~ Perfume Rosa~ 3.4 ~ 4.5 79 5000 210
## 8 1284462 Acqua Di Pa~ Cologne Colo~ 1.7 ~ 5 13 719 120
## 9 2221588 Acqua Di Pa~ Body Mi~ Peon~ 1.7o~ 4 5 800 58
## 10 2221596 Acqua Di Pa~ Perfume Rosa~ 1.7o~ 3 5 2100 58
## # ... with 9,158 more rows, and 12 more variables: value_price <dbl>,
## # URL <chr>, MarketingFlags <lgl>, MarketingFlags_content <chr>,
## # options <chr>, details <chr>, how_to_use <chr>, ingredients <chr>,
## # online_only <dbl>, exclusive <dbl>, limited_edition <dbl>,
## # limited_time_offer <dbl>———- Battle of the Doctor Brands: Moisturizer Edition ——————
I selected 8 variables from this data set, and then I began filtering using the filter() function from the dplyr package of four different Doctor-owned brands in Sephora. These brands carried so many different products to work with such as face washes, face serums, face masks, sunscreen, and other specialty products.
- Questions
What questions are you considering? 1. I want to see if there is a relationship between price points and product size. 2. I would also like to see if there is a relationship between price point and brand rating 3. I want to see what facial product had the highest rating and look at the cost 4. I want to see what product was the most popular – use a frequency table 5. I want to filter the data and look to see which brand had the best-rated face serum, face wash, and moisturizer.
BOD <- sephora_website_dataset %>%
select(brand, category, price, name, number_of_reviews, love, rating, size, value_price) %>%
filter(brand %in% c("Dr Roebuck's", "Dr. Barbara Sturm", "Dr. Brandt Skincare", "Dr. Dennis Gross Skincare", "Dr. Jart+")) %>%
filter(category %in% c("Face Wash & Cleansers", "Face Serums", "Face Masks", "Toners", "Moisturizers")) %>%
group_by(category)
BOD## # A tibble: 105 x 9
## # Groups: category [5]
## brand category price name number_of_revie~ love rating size value_price
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Dr Roeb~ Moistur~ 45 No W~ 236 5300 4 1.69~ 45
## 2 Dr Roeb~ Face Ma~ 28 Ulur~ 28 4200 4.5 1.69~ 28
## 3 Dr Roeb~ Face Se~ 60 Perk~ 37 2200 4 1 oz~ 60
## 4 Dr Roeb~ Face Ma~ 28 Tama~ 15 3000 4 1.69~ 28
## 5 Dr Roeb~ Face Ma~ 28 Iceb~ 8 1600 4 1.69~ 28
## 6 Dr Roeb~ Face Se~ 60 True~ 22 1900 4.5 1 oz~ 60
## 7 Dr Roeb~ Face Wa~ 25 Noos~ 37 1300 4 3.38~ 25
## 8 Dr Roeb~ Moistur~ 45 Stok~ 3 272 4 1.69~ 45
## 9 Dr Roeb~ Face Wa~ 25 Kibo~ 6 772 4 3.38~ 25
## 10 Dr Roeb~ Face Se~ 60 Surf~ 16 782 4 1 oz~ 60
## # ... with 95 more rows- Calculate the Missing Values: Using the sum() function
My dataset did not contain any missing values. I did find that my dataset contains binary variables such as 0 and 1 within the column variables.
sum(is.na(BOD))## [1] 0(sum (is.na (BOD))/prod (dim (BOD)))*100## [1] 0- Variance:
var.test(BOD$value_price, BOD$price) #As the p value is greater than 0.05, there is no evidence to suggest that the variances are unequal##
## F test to compare two variances
##
## data: BOD$value_price and BOD$price
## F = 0.99926, num df = 104, denom df = 104, p-value = 0.997
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6789316 1.4707262
## sample estimates:
## ratio of variances
## 0.999261varttest <- t.test(BOD$value_price, BOD$price, var.equal = TRUE)
head(varttest) #I prefer this format/view## $statistic
## t
## 0.006690631
##
## $parameter
## df
## 208
##
## $p.value
## [1] 0.9946681
##
## $conf.int
## [1] -22.37379 22.52617
## attr(,"conf.level")
## [1] 0.95
##
## $estimate
## mean of x mean of y
## 84.75238 84.67619
##
## $null.value
## difference in means
## 0- Two Sample T-Test
• Chart: Value Price vs Price: No significance – Welch Two-Sample T-Test: A two-sample t-test is used to test the null hypothesis that the two samples come from distributions with the same mean (i.e. the means are not different). For my two-sample t-test, I calculated and plotted the value price vs price variables for ALL the doctor brands that I selected from this dataset and found the estimated mean of differences was 0.07619048. x= value price and y = price value of x = 84.75238 or $84.75 and the value of y= 84.67619 or $84.67. My chart shows most of the distribution scattered between $50-$100 on both x-axis and y-axis.
pairedtwosampletest <- t.test(BOD$value_price, BOD$price, paired= TRUE)
head(pairedtwosampletest) #I prefer this format/view ## $statistic
## t
## 1.421062
##
## $parameter
## df
## 104
##
## $p.value
## [1] 0.1582902
##
## $conf.int
## [1] -0.0301304 0.1825114
## attr(,"conf.level")
## [1] 0.95
##
## $estimate
## mean of the differences
## 0.07619048
##
## $null.value
## difference in means
## 0- Chi-Square Testing: Two Categorical Variables
H0: The variables are not associated i.e., are independent. (Null Hypothesis) H1: The variables are associated, i.e., are dependent. (Alternative Hypothesis) If the “p” value is above 0.05, it means the probability of independence is high and sufficient enough to conclude that the variables do not have a relationship. However, anything below 0.05 means that the probability of independence is insignificantly low, and the variables share a strong correlation.
I used the chisq.test() function to test brand vs category, brand vs ingredients, and brand vs marketing flag content. I found that the two variables in my dataset that did have a correlation were Brand and Marketing Flags Content.
BOD2 <- sephora_website_dataset %>%
select(brand, category, price, name, MarketingFlags_content, details, ingredients, rating) %>%
filter(brand %in% c("Dr Roebuck's", "Dr. Barbara Sturm", "Dr. Brandt Skincare", "Dr. Dennis Gross Skincare", "Dr. Jart+")) %>%
filter(category %in% c("Face Serums", "Toners", "Moisturizers")) %>%
filter(rating > 4.0)
BOD2## # A tibble: 26 x 8
## brand category price name MarketingFlags_~ details ingredients rating
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
## 1 Dr Roebuck's Face Se~ 60 True~ exclusive · onl~ "What ~ "-Hyaluron~ 4.5
## 2 Dr Roebuck's Toners 28 Life~ exclusive · onl~ "What ~ "-Glycolic~ 4.5
## 3 Dr. Barbara~ Moistur~ 230 Face~ 0 "What ~ "-Skullcap~ 4.5
## 4 Dr. Barbara~ Face Se~ 145 Anti~ 0 "What ~ "-Hyaluron~ 5
## 5 Dr. Barbara~ Face Se~ 310 Nigh~ online only "What ~ "-Extracts~ 4.5
## 6 Dr. Barbara~ Face Se~ 300 Dark~ 0 "What ~ "-Hyaluron~ 5
## 7 Dr. Barbara~ Moistur~ 215 Clar~ 0 "What ~ "-Complex ~ 4.5
## 8 Dr. Barbara~ Face Se~ 55 Clar~ 0 "What ~ "-Complex ~ 5
## 9 Dr. Barbara~ Moistur~ 205 Face~ 0 "What ~ "-Purslane~ 4.5
## 10 Dr. Barbara~ Moistur~ 230 Brig~ 0 "What ~ "-Extract ~ 5
## # ... with 16 more rows- Chi-Square T-Test
chiqSB <- chisq.test(BOD2$brand, BOD2$MarketingFlags_content)## Warning in chisq.test(BOD2$brand, BOD2$MarketingFlags_content): Chi-squared
## approximation may be incorrectchisq.test(BOD2$brand, BOD2$ingredients)## Warning in chisq.test(BOD2$brand, BOD2$ingredients): Chi-squared approximation
## may be incorrect##
## Pearson's Chi-squared test
##
## data: BOD2$brand and BOD2$ingredients
## X-squared = 104, df = 100, p-value = 0.3721chisq.test(BOD2$brand, BOD2$category)## Warning in chisq.test(BOD2$brand, BOD2$category): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: BOD2$brand and BOD2$category
## X-squared = 9.7144, df = 8, p-value = 0.2856H0: The variables are not associated i.e., are independent. (Null Hypothesis) H1: The variables are associated, i.e., are dependent. (Alternative Hypothesis)
If the “p” value is above 0.05, it means the probability of independence is fairly high and sufficient enough to conclude that the variables do not have a relationship. However, anything below 0.05 means that the probability of independence is insignificantly low, and the variables share a strong correlation.
chiqSB##
## Pearson's Chi-squared test
##
## data: BOD2$brand and BOD2$MarketingFlags_content
## X-squared = 31.06, df = 12, p-value = 0.001929The two variables in my data set that did have a strong correlation were Brand and Marketing Flags Content.
correlationSB <- table(BOD2$brand, BOD2$MarketingFlags_content, BOD2$name)
correlationSB## , , = Alpha Beta® Exfoliating Moisturizer
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 1 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Alpha Beta® Pore Perfecting & Refining Serum
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 1 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Anti-Pollution Drops
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Brightening Face Cream
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Ceramidin™ Cream
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 1 0 0 0
##
## , , = Cicapair™ Tiger Grass Calming Gel Cream
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 1 0 0
##
## , , = Cicapair™ Tiger Grass Cream
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 1 0 0
##
## , , = Cicapair™ Tiger Grass Serum
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 1 0 0
##
## , , = Clarifying Face Cream
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Clarifying Spot Treatment
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Dark Spots No More® Triple Acid Spot Minimizing Concentrate
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 1 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Darker Skin Tones Face Cream
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Darker Skin Tones Hyaluronic Serum
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Do Not Age with Dr. Brandt Transforming Pearl Serum
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 1 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Face Cream Light
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Face Cream Rich
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 1 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Hyaluronic Marine Hydration Booster
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 1 0
## Dr. Jart+ 0 0 0 0
##
## , , = Lifesaver Skin Brightening Toner
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 1 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Night Serum
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 1
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Peptidin™ Firming Serum with Energy Peptides
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 1 0 0 0
##
## , , = Peptidin™ Radiance Serum with Energy Peptides
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 1 0 0 0
##
## , , = Stress Repair Face Cream with Niacinamide
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 1 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Stress Rescue Super Serum with Niacinamide
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 1 0 0 0
## Dr. Jart+ 0 0 0 0
##
## , , = Teatreement™ Moisturizer
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 1 0 0
##
## , , = Teatreement™ Toner
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 0 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 1 0 0
##
## , , = True Blue Hydrating Serum
##
##
## 0 exclusive exclusive · online only online only
## Dr Roebuck's 0 0 1 0
## Dr. Barbara Sturm 0 0 0 0
## Dr. Brandt Skincare 0 0 0 0
## Dr. Dennis Gross Skincare 0 0 0 0
## Dr. Jart+ 0 0 0 0- Plot Two
• Dr. Jart+: Winner – Price point, reviews, marketing, etc. – Dr. Jart+ had 4.0 – 4.5 star ratings, an affordable price point for the everyday consumer, and many customers who left product reviews. I would consider Dr. Jart+ to be a top-trusted brand based on these findings and variables. If you look at my frequency table, you will see that Dr. Jart+ is marketing flagged content such as “exclusive only”.
BODplot <- ggplot(BOD, aes(x=rating, y=price, size = number_of_reviews, color=brand)) +
geom_point(alpha=0.9)+
scale_size(range = c(.1, 9), name="Customer Who Left a Review") +
labs(title= "Battle of the Doctors")+
ylab("Price (in USD)") +
xlab("Ratings out of 5 Stars")
BODplot
BOD2bar <- barplot(table(BOD2$brand, BOD2$MarketingFlags_content),
main = "ChiSquare Test: Relationship Between Brand and Marketing Flag Content",
xlab = "Brand",
ylab = "Marketing Flags")
- Plot 3
BOD3 <- sephora_website_dataset %>%
select(brand, category, price, value_price, name) %>%
filter(brand %in% c("Dr. Jart+")) %>%
filter(category %in% c( "Face Serums")) %>%
filter(price < 50) %>%
filter(value_price < 50)
BOD3## # A tibble: 8 x 5
## brand category price value_price name
## <chr> <chr> <dbl> <dbl> <chr>
## 1 Dr. Jart+ Face Serums 46 46 Cicapair™ Tiger Grass Serum
## 2 Dr. Jart+ Face Serums 18 18 Focuspot™ Micro Tip™ Patches
## 3 Dr. Jart+ Face Serums 18 18 Focuspot™ Blemish Micro Tip™ Patch
## 4 Dr. Jart+ Face Serums 48 48 Peptidin™ Radiance Serum with Energy ~
## 5 Dr. Jart+ Face Serums 18 18 Focuspot™ Dark Spot Micro Tip™ Patch
## 6 Dr. Jart+ Face Serums 18 18 Focuspot™ Line & Wrinkle Micro Tip™ P~
## 7 Dr. Jart+ Face Serums 48 48 Peptidin™ Firming Serum with Energy P~
## 8 Dr. Jart+ Face Serums 18 18 Focuspot™ Dark Circle Micro Tip™ Patchvar.test(BOD3$value_price, BOD3$price) #As the p value is greater than 0.05, there is no evidence to suggest that the variances are unequal##
## F test to compare two variances
##
## data: BOD3$value_price and BOD3$price
## F = 1, num df = 7, denom df = 7, p-value = 1
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2002038 4.9949092
## sample estimates:
## ratio of variances
## 1varttest3 <- t.test(BOD3$value_price, BOD3$price, var.equal = TRUE)
t.test(BOD3$value_price, BOD3$price)##
## Welch Two Sample t-test
##
## data: BOD3$value_price and BOD3$price
## t = 0, df = 14, p-value = 1
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.29393 16.29393
## sample estimates:
## mean of x mean of y
## 29 29priceSB <- plot(x = BOD$value_price,y =BOD$price,
xlab = "Value Price in USD",
ylab = "Actual Price is USD",
main = "Value Price vs Actual Price")
priceSB## NULL