The OLS regression of average retail price on total weekly sales volume analysed 500 weekly observations. The volume–price relationship is negative and highly significant (p < 0.001), with a slope of -1.89e-08 USD per unit. Total volume alone explains 4.2% of the week-to-week variation in average price (R² = 0.0424), with a root mean squared error of $0.3923.

Across 500 weekly observations, average retail price ranged from $0.51 to $2.90 with a mean of $1.39 (SD $0.40). Total weekly volume spanned from 796 to 43,409,836 units with a mean of 1,059,689 (SD 4,370,936), indicating substantial week-to-week variability in market supply.

The volume slope is -1.89e-08 USD per unit (highly significant (p < 0.001)), indicating that each additional unit of weekly volume is associated with a -1.89e-08 USD shift in average retail price. The model’s R² is 0.0424 (adjusted 0.0405), meaning total volume accounts for 4.2% of the week-to-week price variation. The model RMSE is $0.3923.

The scatter of 500 weekly observations shows a negative association between total volume and average price (Pearson r = -0.206). The cloud confirms the directional intuition from the OLS slope: weeks with higher sales volumes tend to have lower retail prices, consistent with the basic demand-supply mechanism.

The OLS line falls from a fitted price of $1.41 at low weekly volumes (~796 units) to $0.59 at high volumes (~43,409,836 units). The 95% prediction interval width is $1.55 at low volume and $1.68 at high volume — if the interval is appreciably wider at one extreme, this may indicate heteroscedasticity. Note: the OLS model is linear and may extrapolate to physically implausible values (e.g., negative price) at volumes outside the training range; interpret fitted values near the extremes of the volume axis as model extrapolation artefacts.

The residual-vs-fitted plot shows 500 observations with residuals ranging from $-0.87 to $1.49. A random scatter around zero indicates that the constant-variance (homoscedasticity) assumption holds; any funnel shape or systematic curvature would signal a violation. The residual standard deviation is $0.3927 (model RSE: $0.3931).

95.4% of standardised residuals fall within ±2 standard deviations (expected ~95.4% under normality). The histogram is approximately bell-shaped and centred on zero, confirming that the normality assumption required for valid OLS confidence intervals and p-values is broadly met. Severe skew or heavy tails would suggest a log-price or log-volume transformation may improve inference.