DataDive9 Mohler
Carson
2024-10-23
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsproject_data <- read.csv("online_shoppers_intention.csv")# Create a horizontal box plot of VisitorType vs. PageValues
ggplot(project_data, aes(y = VisitorType, x = PageValues, fill = VisitorType)) +
geom_boxplot(outlier.color = "red", outlier.shape = 16, outlier.size = 2) +
scale_fill_manual(values = c("lightblue", "lightcoral", "white")) + # Custom colors for each VisitorType
labs(title = "Distribution of Page Values by Visitor Type",
y = "Visitor Type",
x = "Page Values") +
theme_minimal(base_size = 14) + # Clean theme
theme(legend.position = "none") # Remove the legend
ggplot(project_data, aes(x = PageValues)) +
geom_histogram(bins = 30, fill = "lightblue", color = "black") +
labs(title = "Distribution of Page Values",
x = "Page Value",
y = "Frequency") +
scale_x_continuous(limits = c(0, 100)) +
scale_y_continuous(limits = c(0,300))## Warning: Removed 82 rows containing non-finite values (`stat_bin()`).## Warning: Removed 4 rows containing missing values (`geom_bar()`).
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## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE1.Response Variable:
For this analysis, the PageValues column is selected as the response variable. It represents the value of a page in terms of potential revenue, and it is a key metric for understanding user behavior in e-commerce. This variable is continuous, making it suitable for this analysis.
Why PageValue?
It directly reflects how much value a user generates during a session.
Understanding what influences PageValues is valuable for optimizing user experience and boosting revenue.
2. Categorical Explanatory Variable
The VisitorType column, which categorizes users as either New Visitors or Returning Visitors, is chosen as the explanatory variable.
Why VisitorType?
Different types of users (new vs. returning) may exhibit distinct behaviors, which can affect how much value they generate per page.
It’s important for marketing and product teams to know how user type impacts revenue potential.
3. Null Hypothesis for ANOVA
Null Hypothesis (H₀): The mean PageValues for New Visitors and Returning Visitors are the same.
Alternative Hypothesis (H₁): The mean PageValues for New Visitors and Returning Visitors are different.
# Perform an ANOVA test
anova_model <- aov(PageValues ~ VisitorType, data = project_data)
summary(anova_model)## Df Sum Sq Mean Sq F value Pr(>F)
## VisitorType 2 61483 30742 90.45 <2e-16 ***
## Residuals 12327 4189394 340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14. Interpretation of ANOVA Output:
Degrees of Freedom (Df):
VisitorType: There is 1 degree of freedom for VisitorType, which makes sense as we are comparing two groups: New Visitors and Returning Visitors.
Residuals: There are 173 degrees of freedom for the residuals, which corresponds to the remaining variability after accounting for the visitor type.
Sum of Squares (Sum Sq):
- The sum of squares for VisitorType is 5817, and for the residuals, it’s 36505. This tells us how much variation in PageValues is explained by VisitorType compared to the total variation.
Mean Sum of Squares (Mean Sq):
For VisitorType, the mean sum of squares is 5817.
For the residuals, the mean sum of squares is 211.
F-value:
- The F-value is 27.57, which is quite large. This indicates that the difference between New Visitors and Returning Visitors in terms of PageValues is statistically significant.
p-value (Pr(>F)):
- The p-value is 4.41e-07, which is much smaller than the conventional significance level of 0.05. This means the difference in PageValues between the two groups (New Visitors and Returning Visitors) is statistically significant.
Conclusion:
Since the p-value is extremely small (less than 0.001), we reject the null hypothesis. This suggests that there is a significant difference in PageValues between New Visitors and Returning Visitors.
Based on the ANOVA test, we find that the visitor type significantly influences the value generated per page. For example, if the test results show that New Visitors generate higher page values, it would suggest that marketing efforts could focus on attracting new visitors, or that returning visitors should be incentivized to engage more deeply with the site.
- Next, we’ll look at an explanatory variable which is continuous.
1. Selecting a Continuous Explanatory Variable
Next, we choose ProductRelated_Duration, which represents the time a user spends on product-related pages, as the continuous variable that could influence PageValues.
Why ProductRelated_Duration?
- It makes sense that users spending more time on product pages would generate more value, as they are more engaged with the content and more likely to make purchases.
# Create a scatter plot with ProductRelated_Duration vs. PageValues
ggplot(project_data, aes(x = ProductRelated_Duration, y = PageValues)) +
geom_point(color = "blue", alpha = 0.5) +
geom_smooth(method = "lm", color = "red", se = FALSE) + # Add linear regression trend line in red
labs(title = "Relationship Between ProductRelated_Duration and PageValues",
x = "Time Spent on Product Pages (ProductRelated_Duration)",
y = "Page Values Generated") +
ylim(0, NA) + # Ensures y-axis does not go below 0
theme_minimal(base_size = 14) # Clean theme with readable font size## `geom_smooth()` using formula = 'y ~ x'
2. Building a Linear Regression Model
We can now build a linear regression model to predict PageValues based on ProductRelated_Duration.
Here’s how to run the linear regression in R:
lm_model <- lm(PageValues ~ ProductRelated_Duration, data = project_data)
summary(lm_model)##
## Call:
## lm(formula = PageValues ~ ProductRelated_Duration, data = project_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.07 -5.72 -5.40 -5.28 356.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.277e+00 1.969e-01 26.804 < 2e-16 ***
## ProductRelated_Duration 5.125e-04 8.727e-05 5.873 4.38e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.54 on 12328 degrees of freedom
## Multiple R-squared: 0.00279, Adjusted R-squared: 0.002709
## F-statistic: 34.49 on 1 and 12328 DF, p-value: 4.384e-09Interpretation of the Linear Regression Output:
Coefficients:
Intercept (23.83): This is the predicted PageValues when ProductRelated_Duration is zero, meaning when no time is spent on product pages, the page value is around 23.83.
ProductRelated_Duration (-0.00338): For each additional unit of ProductRelated_Duration, the PageValues decreases slightly by 0.00338. This suggests a negative relationship, meaning more time spent on product pages may not always result in higher page values.
Statistical Significance:
- Both the intercept and ProductRelated_Duration are statistically significant (p-values < 0.001), meaning this relationship is not due to random chance.
R-squared (0.166):
- The model explains about 16.6% of the variation in PageValues, suggesting that other factors not included in the model account for the remaining variation.
F-statistic (34.43, p-value < 2.195e-08):
- The model as a whole is statistically significant, indicating that ProductRelated_Duration does influence PageValues, though the effect size is relatively small.
Conclusion:
There is a small but significant negative relationship between time spent on product pages and page values, suggesting more time spent does not always lead to higher value.
Given the negative relationship between ProductRelated_Duration and PageValues, it seems that spending more time on product pages does not necessarily lead to higher value generation. This may indicate that users who spend too long on product pages are either getting distracted or not finding what they need efficiently.
Recommendations:
Improve Product Page Design:
- Consider simplifying product pages or providing clearer calls to action (e.g., “Buy Now” buttons, product comparisons, or better product descriptions). This can help users make faster decisions and potentially lead to higher page values.
Target Returning Visitors with Promotions:
- Since time spent on product pages negatively impacts page value, it’s worth offering quick purchase incentives for returning visitors to reduce the time spent browsing (e.g., discount codes or fast checkout options).
Monitor Users Who Spend Too Much Time:
- Look into specific sessions where users spend excessive time on product pages but don’t generate value. Identify possible issues (e.g., poor product information or confusing navigation) and address them to improve conversion rates.
So the data set I analyzed above was cleaned somewhere else on R, but still saved in the Environment in a different form than what would appear when the file is knit, it’s not using the same version of the data set and therefor all the results are different for the tasks above. Unfortunately, I’m running into this issue too late and can’t figure out how to get “filtered_data” back into the same form I had it in during the analysis. I had to change all the code above to use “project_data” which produces much messier results more heavily influenced by the outliers in our data set. Fortunately I do have these visuals and model summaries saved from my analysis:
