Response Variable Selection: For this analysis, I’ll choose the “streams” column as the response variable. The number of streams a song receives on Spotify is a crucial metric for understanding its popularity and impact.

Explanatory Variable Selection: I’ll select “key,” which represents the musical key of the song. Different keys can evoke different moods and affect listeners differently, potentially influencing the popularity of a song.

Null Hypothesis for ANOVA: Null Hypothesis: The mean number of streams for songs is the same across all musical keys.

ANOVA Test:

dataset <- read.csv("spotify-2023.csv")
anova_result_key <- aov(streams ~ key, data = dataset)
summary(anova_result_key)
##              Df    Sum Sq   Mean Sq F value Pr(>F)
## key          11 2.984e+18 2.713e+17   0.843  0.597
## Residuals   941 3.029e+20 3.219e+17

The p-value associated with the F-statistic is 0.597, which is greater than the typical significance level of 0.05. Therefore, we fail to reject the null hypothesis. This suggests that there is not enough evidence to conclude that the mean number of streams varies significantly among different musical keys. In other words, the choice of musical key does not appear to have a significant impact on the popularity of a song in terms of streaming numbers on Spotify.

Linear Regression Model: For the continuous explanatory variable, I’ll choose “danceability” as it represents a characteristic of the song that could influence its popularity and consequently, its number of streams.

lm_model <- lm(streams ~ danceability_., data = dataset)
summary(lm_model)
## 
## Call:
## lm(formula = streams ~ danceability_., data = dataset)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -621402622 -364211313 -207092800  164430365 3121629204 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    784592561   85642505   9.161  < 2e-16 ***
## danceability_.  -4046534    1249390  -3.239  0.00124 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.64e+08 on 951 degrees of freedom
## Multiple R-squared:  0.01091,    Adjusted R-squared:  0.00987 
## F-statistic: 10.49 on 1 and 951 DF,  p-value: 0.001242

In the provided linear regression model, the coefficient for the “danceability_%” variable is -4046534. This coefficient represents the estimated change in the number of streams for a one-unit increase in danceability_%. Given that this coefficient is negative, it indicates that, on average, as the danceability_ percentage of a song increases, the number of streams decreases.

In the context of the data, this suggests that songs with higher danceability_ percentages may tend to have lower streaming numbers on Spotify. This might seem counterintuitive at first glance, as one might expect more danceable songs to attract more listeners and thus, more streams. However, several factors could contribute to this result:

  1. Listener Preferences: It’s possible that the dataset comprises songs from various genres, and within each genre, there could be different listener preferences regarding danceability_. For instance, while some genres might traditionally be associated with danceable music (e.g., pop, electronic), others might have a smaller audience for highly danceable songs.

  2. Competition and Market Saturation: In highly competitive music markets, where numerous songs are vying for listeners’ attention, a high danceability_ score alone may not guarantee success. Other factors such as artist popularity, promotion, and the overall quality of the song may also play significant roles.

  3. Streaming Algorithms: Streaming platforms like Spotify often use complex algorithms to recommend songs to users based on their listening habits and preferences. These algorithms may prioritize factors beyond danceability_, such as artist popularity, recent releases, and user engagement metrics.

Recommendations: While the negative coefficient for danceability_% may seem discouraging, it’s essential to view it in the context of the broader music landscape. Rather than avoiding danceable music altogether, artists and music producers should consider a balanced approach:

  1. Diversify Offerings: While danceable music has its appeal, artists should aim for diversity in their catalog, including songs with varying levels of danceability_. This ensures they cater to a broader audience with different preferences.

  2. Quality Over Danceability: Instead of solely focusing on making highly danceable tracks, prioritize creating high-quality music that resonates with listeners emotionally. Authenticity and originality often trump pure danceability_ in building a dedicated fan base.

  3. Strategic Marketing and Promotion: Invest in strategic marketing and promotion efforts to ensure songs reach their target audience effectively. Leveraging social media, collaborations, and playlist placements can help increase visibility and engagement, irrespective of danceability_.

  4. Adaptation and Experimentation: Keep an eye on trends and evolving listener preferences. While danceability_ may not be the sole determinant of success, adapting to changing tastes and experimenting with new sounds can help artists stay relevant and attract new listeners.

Ultimately, while the coefficient for danceability_% provides valuable insights, it’s crucial to consider it alongside other factors and to approach music creation and promotion holistically to maximize success in today’s dynamic music industry landscape.