We conducted an Ordinary Least Squares (OLS) regression analysis to investigate the relationship between CPM (cost per thousand impressions) and CTR (click-through rate) for a sample of 72 Meta ad campaigns. The results showed that the model has a high R-squared value of 0.694, which indicates that approximately 69% of the variation in CTR can be explained by the variation in CPM. Additionally, the F-statistic of 159.0 and p-value of 1.10e-19 suggest that the model is statistically significant and that the regression coefficient for CTR is significantly different from zero.
The estimated regression coefficient for CTR was 2.776e+04, which means that for every one unit increase in CTR, we would expect to see an increase of 2.776e+04 in CPM. Furthermore, the 95% confidence interval for the coefficient ranged from 2.34e+04 to 3.21e+04, indicating that we can be fairly confident that the true population coefficient falls within this range.
The regression analysis also provided some diagnostic statistics, such as the Durbin-Watson statistic of 0.681 and the Jarque-Bera test for normality, which suggested that the residuals of the model were not autocorrelated and followed a normal distribution.
Overall, these results indicate that CPM has a significant impact on CTR for Meta ads, and that evaluating ad performance based solely on CTR may not provide a complete picture of the effectiveness of an ad campaign. By considering the cost per impression, marketers can gain a better understanding of the value of their ad campaigns and make more informed decisions about their advertising strategies.