We will be analyzing data about Mobile Phones from kaggle https://www.kaggle.com/datasets/abdulmalik1518/mobiles-dataset-2025?resource=download

This dataset contains detailed specifications and official launch prices of various mobile phone models from different companies. It provides insights into smartphone hardware, pricing trends, and brand competitiveness across multiple countries. The dataset includes key features such as RAM, camera specifications, battery capacity, processor details, and screen size.

Below are the average price, average ram, average battery capacity, and the standard deviation of price for the mobile phones.

##   avg_price avg_ram avg_battery sd_price
## 1    583.34    7.78     5026.16   421.01

The previous slide shows the graph of average price by brand, represented using a bar plot. It shows the average price by brand of each mobile phone in the market. From the graph, we can see that Sony has the highest average price, followed by Huawei and then Apple. From the graph, we can tell that Nokia had the least average price.

The previous slide shows the boxplot of the price distribution of prices by brand. We can see that Samsung phones has the highest distribution among the prices. We can also see that Huawei has the highest price of a mobile phone, which is an outlier of its price distribution. While Apple phones generally do not have a large price distribution.

We can see that majority of the smartphones has around 6-6.5 inches as the screen size and around 5000mAh of battery capacity, which suggests that most people in the market tends to want a screen size that is not too big and battery capacity that can last throughout the day. The largest phone in terms of screen size and battery capacity is the Samsung Galaxy Tab S8 Ultra at 14.6 inches, having 11200mAh of battery capacity to support the screen size.

We can see from the previous slide that majority of the mobile phones around 8GB of RAM, and 12GB of RAM. We can also see that the higher the RAM, the higher the price in USD generally. However, majority of the mobile phones has a price of less than $1500. This suggest that majority of the people are more likely to buy a mobile phone that costs less than $1500. This also shows that there is a drastic drop in the number of mobile phones that has a battery capcity of more than 8000mAh.

apple_price <- mobiles %>% filter(company_name == "Apple") %>%
                pull(price_usd)
huawei_price <- mobiles %>% filter(company_name == "Huawei") %>%
                pull(price_usd)

t.test(apple_price, huawei_price, alternative = "two.sided")

## 
##  Welch Two Sample t-test
## 
## data:  apple_price and huawei_price
## t = -0.82494, df = 45.898, p-value = 0.4137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -303.0360  126.8623
## sample estimates:
## mean of x mean of y 
##  1028.485  1116.571

From the previous slide, we can see that the t-statistic’s p-value is at 0.4137, larger than 0.05 which suggests strong evidence to fail to reject the null hypothesis where there is no difference between the average prices of Apple and Huawei phones. Moreover, the 95 percent confidence interval of [-303.0360, 126.8623] suggests that we are 95% confident that the difference in average prices is between $-303 and $126, which includes $0. Therefore, we can conclude that Apple phones are not significantly more expensive than Huawei phones on average.